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SELECTED PAPERS FROM IEEE ICCC'12
Adaptive Soft Frequency Reuse Scheme for In-building Dense Femtocell Networks CHEN Jiming1, WANG Peng2, ZHANG Jie3 1
Ranplan Wireless Network Design Ltd., UK Centre for Wireless Network Design, University of Bedfordshire, Luton, UK 3 The Communications Group, University of Sheffield, Sheffield, UK 2
Received: 2012-09-29 Revised: 2012-11-30 Editor: NIU Zhisheng
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Abstract: Femtocell networks have emerged as a key technology in residential, office building or hotspot deployments that can significantly fulfill high data demands in order to offload indoor traffic from outdoor macro cells. However, as one of the major challenges, inter-femtocell interference gets worse in 3D in-building scenarios because of the presence of numerous interfering sources and then needs to be considered in the early network planning phase. The indoor network planning and optimization tool suite, Ranplan Smallcell®, makes accurate prediction of indoor wireless RF signal propagation possible to guide actual indoor femtocell deployments. In this paper, a new adaptive soft frequency reuse scheme in the dense femtocell networks is proposed, where multiple dense femtocells are classified into a number of groups according to the dominant interference strength to others, then the minimum subchannels with different frequency reuse factors for these groups are determined and transmit powers of the grouping sub-channels are adaptively adjusted based on the strength to mitigate the mutual interference. Simulation results show the proposed scheme yields great performance gains in terms of the spectrum efficiency relative to the legacy soft frequency reuse and universal frequency reuse. Key words: in-building femtocell networks;
soft frequency reuse; adaptive interference coordination
I. INTRODUCTION Recently, it has been reported that about 80% of wireless communication traffic comes from indoors [1]. However, penetration loss and complex indoor environment make that it is difficult for existing cellular network to sufficiently follow the demand of indoor mobile traffic. The deployment of femtocells is envisioned to be a key solution for providing high wireless data-rates, offloading the macrocell traffic and enhancing the coverage of existing networking [2]. Femtocell is a low-power consuming base station connected to the service provider’s network via broadband such as DSL or cable. It can improve the indoor coverage and data performance by means of frequency reuse. Femtocells can be densely deployed in a small area such as an office building, hotspot area, or residential area. Nevertheless, an unplanned or random user-installed deployment faces several problems and challenges in terms of interference management and backhaul constraints [3]. In co-channel deployments, all femtocells reuse the spectrum resources, and interference from other femtocells might significantly deteriorate the overall system performance by multiple dominant interferences,
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which are from femtocells not only on the same floor due to adjacent deployment, but also on the upper and lower floors due to cross-floor signal penetration. Hence, interference modeling used in traditional cellular scenario is not proper in a building environment. Currently, several interference mitigation schemes in macro base station scenario have been widely studied mainly by means of interference randomization, interference control, interference suppress, and interference coordination. In the first class the interference is averaged across the whole spectrum via spreading sequences (e.g. scrambling and interleaving), and therefore is not actually cancelled out. The second class uses the power control and static beamforming to reduce the interference level. But by contrast, in the third class, the interference is successfully suppressed by using advanced signal processing techniques [4]. Although these techniques are becoming popular, however, the complexity at the receiver side and backhaul constraint are still the challenging issues particularly in the presences of multiple dominant interferences. Inter-Cell Interference Coordination (ICIC) techniques [5], on the other hand, present pragmatically a more feasible solution. Unlike the macro base station, femto base stations can be installed by users in a random manner, making it difficult to handle the interference problem. The traditional methods can be applied to the mitigation of inter-femtocell interference when femtocells are deployed in a systematic way with low density [6]. However, when multiple femtocells are densely deployed in a building environment, the interference source will be greatly increased, and the interference scenario will drastically vary due to a large number of dominant random interfering nodes. In order to mitigate the inter-femtocell interference in the dense environment, several methods have been proposed, e.g. Fractional Frequency Reuse (FFR) [7-8], which uses the flexible Frequency Reuse Factors (FRF) in the cell- edge China Communications January 2013
area and cell-centre area. But in Ref. [7], dense inter-femtocell interference is not specially considered. While in Ref. [8], a femtocell can be granted admission into a group only when it interferes with all the femtocells already admitted by that group. However, it should be noted that it has high computational complexity and the graph based method is very flexible. Therefore, different admission control criterions can be developed to tune the size of each group, and spectrum resource can be fully used for each group to improve the spectrum efficiency. In this paper, a new soft frequency reuse scheme is proposed for interference management in dense femtocell networks, which was partly presented in Ref. [9]. Based on the Reference Signal Received Power (RSRP) from the serving users, the multiple interfering femtocells can be determined to form several groups, and then the minimum subchannels with different soft frequency reuse factors for these groups are allocated to provide optimal performance near the cell edge. After the allocation of sub-channels to femtocells in each group, the transmit power on the sub-channel part for the group is adaptively adjusted in term of the interference strength. By making the use of higher frequency reuse factor with the least number of orthogonal sub-channels according to the deployment and interference environment, the dense femtocells can perform interference mitigation, and consequently improve the overall system performance. Simulation results show the SINR performance gain is more than 3 dB and the average spectrum efficiency outperforms 10.12% comparing with the legacy soft FFR scheme. The remainder of this paper is organized as follows. Sections II and III describe the system model and existing frequency reuse schemes. Section IV proposes the adaptive soft frequency reuse scheme in a multi-femtocell deployment environment. In Section V, the simulation results of the proposed scheme based on the Ranplan Small-cell® tool suite [10] is presented, and finally, Section VI
In the proposed adaptive soft frequency reuse scheme, multiple dominant interference from in-building dense femtocells is classified into a number of groups, and different frequency reuse factors and transmit powers for these groups are adjusted adaptively to mitigate the mutual interference.
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summarizes the conclusions and further works.
II. SYSTEM MODEL Considering the downlink transmission of a femtocell network in a multi-floor office building, as shown in Figure 1, M femtocells and K User Equipments (UEs) are deployed, respectively. These femtocells use an Orthogonal Frequency Division Multiple Access (OFDMA) technique. Here, we assume that the building is located in the macrocell coverage area, and femtocells use separated frequency band so that interference from macrocell can be neglected. For any transmission between a femtocell m and one of its UE k, the received signal at UE k over the subcarrier of OFDM systems can be denotes as
yk = H k ,m xm +
M
å
H k ,i xi + nk
(1)
i=1,i ¹m
where yk denotes the received signal vector
by the UE k; H k ,i are complex channel state information from femtocell i to UE k; xm is the transmitted data stream of femtocell m; and nk represents the noise at UE k which is considered as a power spectrum density N 0 additive white Gaussian noise vector (AWGN). Then the average Signal to Interference plus Noise Ratio (SINR) of UE k from femtocell m at sub-channel n can be represented as
kn
=
pmn 10-Lk M
å
n
10
H m,n
- Lk 10
2
Pi 10
2
(2)
H i,n + b f N0
i =1,i ¹ m
here, Pmn denotes the transmit power of femtocell m at sub-channel n; bf is the sub-channel spacing; and Lk = 10 ⋅ log10 (d ma ) + Lw,m denotes the path loss in dB, where dm denotes the distance from femtocell m to user, Lw,m denotes the wall loss in dB, and α denotes the path loss exponent.
Fig.1 3D office building model
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III. PREVIOUS FREQUENCY REUSE SCHEMES In this section, we will review several frequency reuse schemes [5] that are related to our work.
edge users are scheduled in the protected resources and cell centre users are scheduled in the shared resources with a lower transmit power, as shown in Figure 4.
3.1 Frequency reuse 1 (FR 1) Frequency reuse 1 is well known as universal frequency reuse scheme, where the entire frequency spectrum is reused over all cells, as shown in Figure 2. However, by employing this scheme only the cell centre users experience good channel quality whereas cell edge users suffer from low radio conditions due to severe inter-cell interference.
Fig.4 The soft frequency reuse scheme
IV. PROPOSED ADAPTIVE SOFT FREQUENCY REUSE SCHEME 4.1 Optimal threshold
Fig.2 The frequency reuse 1 scheme
3.2 Hard frequency reuse 3 Figure 3 illustrates how the power-frequency resource restrictions are applied at each cell in frequency reuse 3 scheme. It is important to note that the frequency reuse factor is restricted by a list of integer numbers: {1, 3, 4, 7, …, i2 + i · j + j2 | i, j ∈ N }.
In the coverage of femtocell m, considering the inter-femtocell interference, especially in the cell-edge region, we can partition the cell coverage into cell centre and cell edge regions. Users in the centre and at the edge of femtocell are identified based on the received SINR when the frequency resource is reused. A user is regarded as in the centre of a femtocell if its SINR is above a threshold; otherwise, it is regarded as at the edge. Denote MC and ME to be the index sets of the centre users and the edge users, respectively. Then M c = {k : k > Gth } and M E = {k : k ≤ Gth }
(3)
Fig.3 The hard frequency reuse 3 scheme
3.3 Soft frequency reuse Soft frequency reuse scheme suggests that cell
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where Gth represents the SINR threshold. In Ref. [11], the distance-based and SINR-based thresholds are optimized to maximize the mean to variance ratio of SINR, i.e. maximizing the mean to variance ratio of the received SINR for a given user. Thus, this parameter is used to achieve an optimal dimension of the centre region and an efficient use of the available bandwidth. The objective function can be written as G( ) =
( ) V ( )
(4)
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where denotes the mean and V is the variance of SINR, respectively. The objective function is maximized by Gth which determined as Gth = max G( )
gl ¬ Tl -1 < Qs - Qi ≤ Tl , for all i = 1, 2, , M -1
(6)
4.2 Femtocell grouping
where Tl is the grouping threshold in dB of lth group.
In the dense femtocell networks, a user at the edge will be interfered by multiple dominant femtocells, especially in the multi-floor office building, as shown in Figure 1, a user on the floor 3 will be interfered by femtocells not only on the same floor but also on the upper and lower floor, such as floor 1, 2, 4, and 5, and so on due to the cross-floor penetration, which is obviously different from the traditional macro cellular scenario. Based on the cell-edge regions of femtocells m, the cell-edge UE k will receive the signal from M femtocells, which can be classified into a number of groups to determine the frequency reuse factors and to set the priority order for the sub-channel allocation of each group. But for each cell-edge user, there have the different interfering femtocells, so the same interfering femtocells set is selected to do the grouping based on the femtocell neighboring relationship, which is similar to the legacy soft FFR scheme in Ref. [5]. Figure 5 gives an example of femtocell grouping. Here, the femtocells grouping is based on the interference graph modeling method. Based on the RSRP of femtocells set, define Qs as the received signal strength from serving femtocell, and Qi, i=1,2,...,M-1 as the received signal strength from interference femtocells. Let G={g1, …, gL} be a set of
4.3 Grouping-based frequency reuse factor selection
Fig.5 An example of femtocell grouping
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(5)
groups, where gl, l=1,…,L, comprises a sets of femtocells interfering with each other. So define
From the grouping results, this subsection introduces the selection of grouping-based frequency reuse factor. Figure 6 illustrates an example of the proposed scheme, where femtocells are classified into three groups. Instead of having only a group on top of the cell-edge region, it is possible to form up to multiple virtual groups each using different parts of spectrum and different frequency reuse factors. Following the concept, the group with the strongest interference strength has the lowest frequency reuse factor, the group with the weakest interference strength has the highest frequency reuse factor, and the others are in the middle. Therefore, for Figure 6, the frequency reuse factor for group 1 is 1/3, the group 2 is 2/3, and the group 3 is 3/4. Accordingly, if there are more groups and more femtocells per group, different frequency reuse factors and frequency partition are used based on cyclic difference sets, where the cyclic property allows a quick FRF adjustment.
4.4 Group reuse pattern algorithm After grouping in Subsections 4.2 and 4.3, next step is to allocate the sub-channel for femtocells in these groups based on the different frequency reuse factor, i.e. selecting the frequency reuse pattern for the groups. If the interference cells are sorted out according to the serving cells, the neighborhood information of each serving cell can be obtained as a result. If we consider each cell as a node in a graph, G(V, E), where V represents the set of grouped femtocells and E is the set of edges. It is expected that two nodes that have an edge between them select different sub-channel
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Fig.6 An example of proposed soft frequency reuse scheme
patterns and frequency reuse factor according to grouping criterion in order to improve the cell edge performance. The problem of finding out the smallest number of sub-channels in the in-building scenario can be formulated as a graph coloring problem. Each node chooses a color different from that of neighbor nodes if there has an edge between them. The smallest number of colors will be the solution of the problem. In the network planning phase, Ranplan Smallcell® tool suite is able to offline solve the problem and perform the sub-channel pattern assignment for each cell through the femtocell management system. The node coloring problem is an NP-complete problem and a heuristic algorithm is presented to address the problem to seek a near optimal solution. The value of currentColor is considered as the smallest number of colors used in the scenario. The whole frequency band or resource blocks in LTE should be divided into currentColor sub-channels and each
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sub-channel is assigned to each cell for cell edge users with higher transmit power according to the color of the node. Note that if there are not enough cell edge users to be scheduled at the reserved sub-channel, the sub-channels can be also used for cell-centre users to achieve high resource efficiency. 错误！ Pseudo-code of the Developed Heuristic Algorithm: INPUT G=(V, E) 1
Compute Degree(v) for all v in V, and V is
2
Set uncoloredCells = V sorted in ascending order
classified into L groups of Degree(v) 3
Set currentColor = 0
4
For each element in uncoloredCells:
5
currentColor = currentColor + 1
6
Set u = first element of uncoloredCells
7
If u is in the group l:
8
Set cellColor(u) = currentColor in group l
9
Set coloredCells = {u}
10
Set coloredGroup = {l}
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femtocells in each group.
11
Set frequencyReuseFactorCells
12
Remove u from uncoloredCells and
V. SIMULATION RESULTS
group l 13
For each v in uncoloredCells:
14
If v is not adjacent to any cells in coloredCells of group l:
15
Set cellColor (v) =
16
Add v to coloredCells
17
Remove v from
currentColor
uncoloredCells 18
End if
19 20
End for End if
21 End for
In this way, the proposed scheme allows not only to control the degree of coverage but also to adaptively adjust the transmit power between the cell-edge, cell-centre region, and groups, which effectively improves the frequency reuse efficiency of UEs on the celledge region.
4.5 Adaptive power control of sub-channel on the edge region In order to improve the performance of cell-edge UE, we adjust the transmit power on the sub-channels of the group based on the RSRP, which should be adaptively select the transmit power of sub-channel for cell-centre and celledge [12]. So the transmit power of femtocell m in the group l at sub-channel n can be expressed as Ng
l
å å RSRPin
gl : Pmn =
iÎ gl n=1 M N
åå RSRPin
Pmtotal
(7)
i=1 n=1
where N gl denotes the number of sub-channel in the group gl; N is the number of total subchannels; and Pmtotal denotes the total transmit N
power of femtocell m, i.e., Pmtotal = å Pmn . n=1
That means the transmit power of each group is adjusted based on the RSRP in a distribution manner after the allocation of sub-channels to
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The modeling platform is based on the Ranplan Small-cell® tool suite [10], which is an in-building network planning & optimization software tool produced by Ranplan Wireless Network Design Ltd., and path loss model is Ranplan Radio-wave Propagation Simulator (RRPS). RRPS implements Intelligent Ray Launching Algorithm (IRLA) [13], which is based on ray tracing/ray launching techniques to model the physical characteristics of the rough terrain and urban building features, performs the electromagnetic calculations, and then evaluates the signal propagation characteristics. The propagation model offers to predict coverage and other channel characteristics such as Angles of Arrival (AoA), Power Delay Profile (PDP). LTE system simulation is based on 9-floor Office building with 4 femtocells deployment on each floor, where 12 dB penetration loss for a heavy wall and 20 dB penetration loss for a floor are considered. The deployment of femtocell network is shown in Figure 1, and the simulation parameters are given in Table I [14]. In the proposed scheme, how to select the grouping threshold is a key step, which will affect the system performance. Coverage radius-based grouping method [8] can obtain good performance in the outdoor scenario, but in the building, due to the irregular coverage area, the performance will be degraded. Minimizing capacity-based method can find the optimal threshold, but it has high complexity. In the paper, simulation-based method is used to find the suboptimal threshold, where the femtocells are classified as 4 groups, and the grouping thresholds are 1dB, 2.5 dB, and 6 dB, respectively, and the number of femtocells in these groups are 3, 3, 4, and 2, respectively, i.e. for a UE on the cell-edge region, total 12 femtocells should be considered to group due to the strength interference from each other, and the femtocells, which are Qs - Qi ≤ 1dB , are added in the first group, which has the
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strongest interference, the femtocells, which are 1 < Qs - Qi ≤ 2.5 , are added in the second group, the third group is based on 2.5 < Qs - Qi ≤ 6 , and the fourth group is based on 6 < Qs - Qi , which has the weakest interference.
building, which is a 3-dimensional interference environment with dense interference sources, up to 11 interference femtocells. Therefore, using legacy soft frequency reuse will decrease the system spectrum efficiency because too much frequency partition will result in the low frequency reuse gain.
Table I Simulation parameters Parameter
Value
Cell layout
36 Femtocells
Area
9 600 m2 with 9 floors
Path loss model Shadowing Bandwidth # of resource blocks (RB) Femtocell Tx power Femtocell antenna
iBuildNet RRPS propagation modeling Lognormal shadowing with 3 dB std for LOS, 4 dB for NLOS 2 GHz, 10 MHz 50 8 dBm 0 dBi, Omnidirectional
UE antenna gain
0 dBi
UE noise figure Downlink scheduler UE number
5 dB Round Robin
User traffic model
Full Buffer
Fig.7 Best signal level
25 UEs per cell/5 RBs per UE
Figure 7 shows the best signal level of one floor, where 2 femtocells is deployed. From this figure, we can see that the signal levels to one location are up to 60dBm and 64dBm, respectively, which means that there have severe interference at the femtocell edge point. Figure 8 shows the cross-floor transmission of femtocell signal, where a femtocell on the 3rd floor is shown. From this figure, comparing to the 41 dBm signal level on this floor, it can be seen that the signal strength on the 2nd and 4th floors are up to 77 dBm and 66 dBm, respectively, and the signal strength are also up 100 dBm on the 1st and 5th floors, which shows cross-floor signal penetration will greatly affect the system performance. Figures 7-8 mean that a UE on the 4th floors will receive the interference from other femtocells not only on the 4th floor, but also on the 3rd and 5th floors. Therefore, the signal penetrating from other floors will make inter-cell interference more complicated in the
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Fig.8 Cross-floor signal level
Figure 9 depicts the objective function as function of the instantaneous SINR γ. It can be seen that the optimal size of the centre region is achieved with Gth = 6.5dB , which means the region where SINR is less than 6.5 dB is considered as a cell-edge region, and UE in this region will be scheduled based on the proposed adaptive soft frequency reuse scheme. Figure 10 presents the SINR and data rate map with legacy soft frequency reuse scheme. From the figures, it can be observed that the in-building environment is more complex, and
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Fig.9 Optimal SINR threshold
encounters more penetration loss and shadowing fading. But due to the transmission loss of wall and pillar, the signals between the
femtocells can be well isolated, so the interference can be reduced. Therefore, in the femtocell deployment, walls and pillars can be utilized to form directional transmission. But at the cell-edge region there have low SINR and data rate. Figure 11 presents the SINR and data rate map of proposed scheme when Gth = 6.5dB. Comparing to Figure 10, it can be observed the proposed adaptive soft frequency reuse scheme can greatly improve the cell centre and cell-edge performance up to 3.7 dB and 3.2 dB, respectively, as shown in Figure 12. Figure 13 shows the CDF of the spectrum efficiency when Gth = 6.5dB . It can be seen that the universal frequency reuse scheme can have the good system spectrum efficiency but
Fig.10 SINR and data rate map based on legacy soft FFR
Fig.11 SINR and data rate map of proposed scheme according to Gth = 6.5dB
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Fig.12 CDF of SINR for legacy soft FFR and proposed scheme
it cannot provide desired cell-edge performance. On the other hand, using the legacy soft frequency reuse [5] provides relatively low spectrum efficiency due to the multiple dominant interferences in the dense femtocell networks, which means that too much frequency partition will degrade the frequency reuse gain, and then degrades the system performance. It can be also observed that the system performance of proposed scheme is close to that of universal frequency reuse in the centre of femtocell while cell-edge performance is better than that of legacy soft frequency reuse. From the table II, comparing with the legacy soft FFR scheme, the average performance gain is up to 10.12%, and cell-edge performance gain is up to 25.3%. It is reasonable because the proposed scheme can adjust the FRF and transmit power for different interference groups, and in the severe interference region, the FRF is the smallest, which is close to the FRF of legacy soft frequency reuse. Table II Spectrum efficiency comparison Average spectrum efficiency (bps/Hz/cell)
Cell edge spectrum efficiency (bps/Hz)
Universal FR
1.354
0.179
Legacy soft FFR
1.392
0.418
Proposed scheme
1.533
0.524
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Fig.13 CDF of spectrum efficiency for different FFR schemes
VI. CONCLUSIONS AND FUTURE WORKS Small cell networks, particularly dense femtocell networks, are promising to provide extra capacity for high indoor data demands in enterprise environments. However, dense femtocell networks will introduce excessive inter-cell interference in the 3D building scenarios. More irregular neighbor cell boundaries will be created due to cross-floor signal penetration and then efficient interference mitigation schemes are required. In this paper, an adaptive soft frequency reuse scheme was proposed for interference management in dense femtocell networks. Based on RSRP, it classifies all interfering femtocells into a number of groups, and then selects the frequency reuse factor and the transmit power for different groups according to the interference strength. Furthermore, a power adaptive scheme in which can effectively control the level of coverage between the cell-edge and cell-centre region was devised, and optimal SINR threshold was given by simulation. The final simulation results show that the proposed scheme achieves not only an enhanced cell-edge spectral efficiency, but also minimal degradation of a forthcoming co-channel femtocell deployment. However, Due to the different load and re-
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source scheduling of each femtocell, the interference environment is time-varying; thus, inter-femtocell interference analysis is also a new challenge for interference coordination or cancellation. These problems need to be further investigated in the future works. At the same time, how to group the femtocells optimally will be the work of next step.
Proceedings of the IEEE International Conference on Communications: August 15-17, 2012, Beijing, China, 2012: 530-534. ®
[10] RANPLAN. SMALL-CELL [EB/OL]. [2001] http: //www.ranplan.co.uk. [11] NAJJAR A, HAMDI N, BOUALLEGUE A. Efficient Frequency
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ACKNOWLEDGEMENT
261–265. [12] PROAKIS J G. Digital Communications (5th
This work was supported by the EU-FP7 iPLAN under Grant No.230745 and EU-FP7 IAPP@RANPLAN under Grant No.218309.
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Biographies CHEN Jiming, received his M.S. and Ph.D. degrees in communication and information system from University of Electronic and Science Technology, China in 2003 and 2006, respectively. Now he is a senior research fellow at Ranplan Wireless Network Design Ltd. (UK) from 2011. Prior to the time, he was a research scientist in Bell labs at Alcatel-Lucent Shanghai Bell Ltd. from 2006 to 2011, and then became a member of Alcatel-Lucent Technical Academy in 2010. In Bell labs his research interests were the key techniques of cooperative processing and small cells for 3GPP LTE-A system. Currently, he focuses on the small cell technologies and multiple wireless system-level simulation, such as UMTS/LTE/WiFi, for indoor and outdoor scenarios, and implement in Ranplan planning and optimization tools. Up to now, he held about 21 international patents and 23 publications. Email: [email protected] WANG Peng, received his B.S. and M.Phil. degrees from Centre for Wireless Network Design (CWiND) of University of Bedfordshire (UK). He joined Ranplan Wireless Network Design Ltd. (UK) as an engineer in 2008, and became senior engineer in 2010. He participated in executing and managing EU 7th Frame-
Town, South Africa, 2010: 1-5.
work projects at Ranplan. He was also involved in
CHEN Jiming, WANG Peng, and ZHENG Jie.
developing of a world leading automatic indoor radio
Adaptive Soft Frequency Reuse Scheme for
network planning and optimization tool suite-
Inbuilding Dense Femtocell Networks[C]//
iBuildNet. Email: [email protected]
China Communications January 2013
ZHANG Jie, is a full professor and holds the Chair in
(EPSRC), the European Commission (EC) FP6/FP7 and
Wireless Systems at the Department of Electronic and
the industries etc. He is a lead author of the book
Electrical Engineering, the University of Sheffield, UK.
"Femtocells: Technologies and Deployment" (Wiley,
His research interests are focused on radio propaga-
Jan. 2010). He and his colleagues published a widely
tion, indoor-outdoor radio network planning and
cited femtocell paper "OFDMA femtocells: A roadmap
optimization, small/femto cell, HetNet, Self-Organi-
on interference avoidance". He was involved in the
sing Network (SON) and smart building/city. Since
setting up of RANPLAN Wireless Network Design Ltd.
2006, he has been an Investigator of over 20 research
(www.ranplan.co.uk) that produces the world leading
projects worth over £17 million funded by the Engi-
Small Cell and HetNet planning and optimization
neering and Physical Science Research Council
tools. Email: [email protected]
China Communications January 2013
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Adaptive Soft Frequency Reuse Scheme for In-building Dense Femtocell Networks CHEN Jiming1, WANG Peng2, ZHANG Jie3 1
Ranplan Wireless Network Design Ltd., UK Centre for Wireless Network Design, University of Bedfordshire, Luton, UK 3 The Communications Group, University of Sheffield, Sheffield, UK 2
Received: 2012-09-29 Revised: 2012-11-30 Editor: NIU Zhisheng
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Abstract: Femtocell networks have emerged as a key technology in residential, office building or hotspot deployments that can significantly fulfill high data demands in order to offload indoor traffic from outdoor macro cells. However, as one of the major challenges, inter-femtocell interference gets worse in 3D in-building scenarios because of the presence of numerous interfering sources and then needs to be considered in the early network planning phase. The indoor network planning and optimization tool suite, Ranplan Smallcell®, makes accurate prediction of indoor wireless RF signal propagation possible to guide actual indoor femtocell deployments. In this paper, a new adaptive soft frequency reuse scheme in the dense femtocell networks is proposed, where multiple dense femtocells are classified into a number of groups according to the dominant interference strength to others, then the minimum subchannels with different frequency reuse factors for these groups are determined and transmit powers of the grouping sub-channels are adaptively adjusted based on the strength to mitigate the mutual interference. Simulation results show the proposed scheme yields great performance gains in terms of the spectrum efficiency relative to the legacy soft frequency reuse and universal frequency reuse. Key words: in-building femtocell networks;
soft frequency reuse; adaptive interference coordination
I. INTRODUCTION Recently, it has been reported that about 80% of wireless communication traffic comes from indoors [1]. However, penetration loss and complex indoor environment make that it is difficult for existing cellular network to sufficiently follow the demand of indoor mobile traffic. The deployment of femtocells is envisioned to be a key solution for providing high wireless data-rates, offloading the macrocell traffic and enhancing the coverage of existing networking [2]. Femtocell is a low-power consuming base station connected to the service provider’s network via broadband such as DSL or cable. It can improve the indoor coverage and data performance by means of frequency reuse. Femtocells can be densely deployed in a small area such as an office building, hotspot area, or residential area. Nevertheless, an unplanned or random user-installed deployment faces several problems and challenges in terms of interference management and backhaul constraints [3]. In co-channel deployments, all femtocells reuse the spectrum resources, and interference from other femtocells might significantly deteriorate the overall system performance by multiple dominant interferences,
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which are from femtocells not only on the same floor due to adjacent deployment, but also on the upper and lower floors due to cross-floor signal penetration. Hence, interference modeling used in traditional cellular scenario is not proper in a building environment. Currently, several interference mitigation schemes in macro base station scenario have been widely studied mainly by means of interference randomization, interference control, interference suppress, and interference coordination. In the first class the interference is averaged across the whole spectrum via spreading sequences (e.g. scrambling and interleaving), and therefore is not actually cancelled out. The second class uses the power control and static beamforming to reduce the interference level. But by contrast, in the third class, the interference is successfully suppressed by using advanced signal processing techniques [4]. Although these techniques are becoming popular, however, the complexity at the receiver side and backhaul constraint are still the challenging issues particularly in the presences of multiple dominant interferences. Inter-Cell Interference Coordination (ICIC) techniques [5], on the other hand, present pragmatically a more feasible solution. Unlike the macro base station, femto base stations can be installed by users in a random manner, making it difficult to handle the interference problem. The traditional methods can be applied to the mitigation of inter-femtocell interference when femtocells are deployed in a systematic way with low density [6]. However, when multiple femtocells are densely deployed in a building environment, the interference source will be greatly increased, and the interference scenario will drastically vary due to a large number of dominant random interfering nodes. In order to mitigate the inter-femtocell interference in the dense environment, several methods have been proposed, e.g. Fractional Frequency Reuse (FFR) [7-8], which uses the flexible Frequency Reuse Factors (FRF) in the cell- edge China Communications January 2013
area and cell-centre area. But in Ref. [7], dense inter-femtocell interference is not specially considered. While in Ref. [8], a femtocell can be granted admission into a group only when it interferes with all the femtocells already admitted by that group. However, it should be noted that it has high computational complexity and the graph based method is very flexible. Therefore, different admission control criterions can be developed to tune the size of each group, and spectrum resource can be fully used for each group to improve the spectrum efficiency. In this paper, a new soft frequency reuse scheme is proposed for interference management in dense femtocell networks, which was partly presented in Ref. [9]. Based on the Reference Signal Received Power (RSRP) from the serving users, the multiple interfering femtocells can be determined to form several groups, and then the minimum subchannels with different soft frequency reuse factors for these groups are allocated to provide optimal performance near the cell edge. After the allocation of sub-channels to femtocells in each group, the transmit power on the sub-channel part for the group is adaptively adjusted in term of the interference strength. By making the use of higher frequency reuse factor with the least number of orthogonal sub-channels according to the deployment and interference environment, the dense femtocells can perform interference mitigation, and consequently improve the overall system performance. Simulation results show the SINR performance gain is more than 3 dB and the average spectrum efficiency outperforms 10.12% comparing with the legacy soft FFR scheme. The remainder of this paper is organized as follows. Sections II and III describe the system model and existing frequency reuse schemes. Section IV proposes the adaptive soft frequency reuse scheme in a multi-femtocell deployment environment. In Section V, the simulation results of the proposed scheme based on the Ranplan Small-cell® tool suite [10] is presented, and finally, Section VI
In the proposed adaptive soft frequency reuse scheme, multiple dominant interference from in-building dense femtocells is classified into a number of groups, and different frequency reuse factors and transmit powers for these groups are adjusted adaptively to mitigate the mutual interference.
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summarizes the conclusions and further works.
II. SYSTEM MODEL Considering the downlink transmission of a femtocell network in a multi-floor office building, as shown in Figure 1, M femtocells and K User Equipments (UEs) are deployed, respectively. These femtocells use an Orthogonal Frequency Division Multiple Access (OFDMA) technique. Here, we assume that the building is located in the macrocell coverage area, and femtocells use separated frequency band so that interference from macrocell can be neglected. For any transmission between a femtocell m and one of its UE k, the received signal at UE k over the subcarrier of OFDM systems can be denotes as
yk = H k ,m xm +
M
å
H k ,i xi + nk
(1)
i=1,i ¹m
where yk denotes the received signal vector
by the UE k; H k ,i are complex channel state information from femtocell i to UE k; xm is the transmitted data stream of femtocell m; and nk represents the noise at UE k which is considered as a power spectrum density N 0 additive white Gaussian noise vector (AWGN). Then the average Signal to Interference plus Noise Ratio (SINR) of UE k from femtocell m at sub-channel n can be represented as
kn
=
pmn 10-Lk M
å
n
10
H m,n
- Lk 10
2
Pi 10
2
(2)
H i,n + b f N0
i =1,i ¹ m
here, Pmn denotes the transmit power of femtocell m at sub-channel n; bf is the sub-channel spacing; and Lk = 10 ⋅ log10 (d ma ) + Lw,m denotes the path loss in dB, where dm denotes the distance from femtocell m to user, Lw,m denotes the wall loss in dB, and α denotes the path loss exponent.
Fig.1 3D office building model
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III. PREVIOUS FREQUENCY REUSE SCHEMES In this section, we will review several frequency reuse schemes [5] that are related to our work.
edge users are scheduled in the protected resources and cell centre users are scheduled in the shared resources with a lower transmit power, as shown in Figure 4.
3.1 Frequency reuse 1 (FR 1) Frequency reuse 1 is well known as universal frequency reuse scheme, where the entire frequency spectrum is reused over all cells, as shown in Figure 2. However, by employing this scheme only the cell centre users experience good channel quality whereas cell edge users suffer from low radio conditions due to severe inter-cell interference.
Fig.4 The soft frequency reuse scheme
IV. PROPOSED ADAPTIVE SOFT FREQUENCY REUSE SCHEME 4.1 Optimal threshold
Fig.2 The frequency reuse 1 scheme
3.2 Hard frequency reuse 3 Figure 3 illustrates how the power-frequency resource restrictions are applied at each cell in frequency reuse 3 scheme. It is important to note that the frequency reuse factor is restricted by a list of integer numbers: {1, 3, 4, 7, …, i2 + i · j + j2 | i, j ∈ N }.
In the coverage of femtocell m, considering the inter-femtocell interference, especially in the cell-edge region, we can partition the cell coverage into cell centre and cell edge regions. Users in the centre and at the edge of femtocell are identified based on the received SINR when the frequency resource is reused. A user is regarded as in the centre of a femtocell if its SINR is above a threshold; otherwise, it is regarded as at the edge. Denote MC and ME to be the index sets of the centre users and the edge users, respectively. Then M c = {k : k > Gth } and M E = {k : k ≤ Gth }
(3)
Fig.3 The hard frequency reuse 3 scheme
3.3 Soft frequency reuse Soft frequency reuse scheme suggests that cell
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where Gth represents the SINR threshold. In Ref. [11], the distance-based and SINR-based thresholds are optimized to maximize the mean to variance ratio of SINR, i.e. maximizing the mean to variance ratio of the received SINR for a given user. Thus, this parameter is used to achieve an optimal dimension of the centre region and an efficient use of the available bandwidth. The objective function can be written as G( ) =
( ) V ( )
(4)
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where denotes the mean and V is the variance of SINR, respectively. The objective function is maximized by Gth which determined as Gth = max G( )
gl ¬ Tl -1 < Qs - Qi ≤ Tl , for all i = 1, 2, , M -1
(6)
4.2 Femtocell grouping
where Tl is the grouping threshold in dB of lth group.
In the dense femtocell networks, a user at the edge will be interfered by multiple dominant femtocells, especially in the multi-floor office building, as shown in Figure 1, a user on the floor 3 will be interfered by femtocells not only on the same floor but also on the upper and lower floor, such as floor 1, 2, 4, and 5, and so on due to the cross-floor penetration, which is obviously different from the traditional macro cellular scenario. Based on the cell-edge regions of femtocells m, the cell-edge UE k will receive the signal from M femtocells, which can be classified into a number of groups to determine the frequency reuse factors and to set the priority order for the sub-channel allocation of each group. But for each cell-edge user, there have the different interfering femtocells, so the same interfering femtocells set is selected to do the grouping based on the femtocell neighboring relationship, which is similar to the legacy soft FFR scheme in Ref. [5]. Figure 5 gives an example of femtocell grouping. Here, the femtocells grouping is based on the interference graph modeling method. Based on the RSRP of femtocells set, define Qs as the received signal strength from serving femtocell, and Qi, i=1,2,...,M-1 as the received signal strength from interference femtocells. Let G={g1, …, gL} be a set of
4.3 Grouping-based frequency reuse factor selection
Fig.5 An example of femtocell grouping
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(5)
groups, where gl, l=1,…,L, comprises a sets of femtocells interfering with each other. So define
From the grouping results, this subsection introduces the selection of grouping-based frequency reuse factor. Figure 6 illustrates an example of the proposed scheme, where femtocells are classified into three groups. Instead of having only a group on top of the cell-edge region, it is possible to form up to multiple virtual groups each using different parts of spectrum and different frequency reuse factors. Following the concept, the group with the strongest interference strength has the lowest frequency reuse factor, the group with the weakest interference strength has the highest frequency reuse factor, and the others are in the middle. Therefore, for Figure 6, the frequency reuse factor for group 1 is 1/3, the group 2 is 2/3, and the group 3 is 3/4. Accordingly, if there are more groups and more femtocells per group, different frequency reuse factors and frequency partition are used based on cyclic difference sets, where the cyclic property allows a quick FRF adjustment.
4.4 Group reuse pattern algorithm After grouping in Subsections 4.2 and 4.3, next step is to allocate the sub-channel for femtocells in these groups based on the different frequency reuse factor, i.e. selecting the frequency reuse pattern for the groups. If the interference cells are sorted out according to the serving cells, the neighborhood information of each serving cell can be obtained as a result. If we consider each cell as a node in a graph, G(V, E), where V represents the set of grouped femtocells and E is the set of edges. It is expected that two nodes that have an edge between them select different sub-channel
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Fig.6 An example of proposed soft frequency reuse scheme
patterns and frequency reuse factor according to grouping criterion in order to improve the cell edge performance. The problem of finding out the smallest number of sub-channels in the in-building scenario can be formulated as a graph coloring problem. Each node chooses a color different from that of neighbor nodes if there has an edge between them. The smallest number of colors will be the solution of the problem. In the network planning phase, Ranplan Smallcell® tool suite is able to offline solve the problem and perform the sub-channel pattern assignment for each cell through the femtocell management system. The node coloring problem is an NP-complete problem and a heuristic algorithm is presented to address the problem to seek a near optimal solution. The value of currentColor is considered as the smallest number of colors used in the scenario. The whole frequency band or resource blocks in LTE should be divided into currentColor sub-channels and each
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sub-channel is assigned to each cell for cell edge users with higher transmit power according to the color of the node. Note that if there are not enough cell edge users to be scheduled at the reserved sub-channel, the sub-channels can be also used for cell-centre users to achieve high resource efficiency. 错误！ Pseudo-code of the Developed Heuristic Algorithm: INPUT G=(V, E) 1
Compute Degree(v) for all v in V, and V is
2
Set uncoloredCells = V sorted in ascending order
classified into L groups of Degree(v) 3
Set currentColor = 0
4
For each element in uncoloredCells:
5
currentColor = currentColor + 1
6
Set u = first element of uncoloredCells
7
If u is in the group l:
8
Set cellColor(u) = currentColor in group l
9
Set coloredCells = {u}
10
Set coloredGroup = {l}
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femtocells in each group.
11
Set frequencyReuseFactorCells
12
Remove u from uncoloredCells and
V. SIMULATION RESULTS
group l 13
For each v in uncoloredCells:
14
If v is not adjacent to any cells in coloredCells of group l:
15
Set cellColor (v) =
16
Add v to coloredCells
17
Remove v from
currentColor
uncoloredCells 18
End if
19 20
End for End if
21 End for
In this way, the proposed scheme allows not only to control the degree of coverage but also to adaptively adjust the transmit power between the cell-edge, cell-centre region, and groups, which effectively improves the frequency reuse efficiency of UEs on the celledge region.
4.5 Adaptive power control of sub-channel on the edge region In order to improve the performance of cell-edge UE, we adjust the transmit power on the sub-channels of the group based on the RSRP, which should be adaptively select the transmit power of sub-channel for cell-centre and celledge [12]. So the transmit power of femtocell m in the group l at sub-channel n can be expressed as Ng
l
å å RSRPin
gl : Pmn =
iÎ gl n=1 M N
åå RSRPin
Pmtotal
(7)
i=1 n=1
where N gl denotes the number of sub-channel in the group gl; N is the number of total subchannels; and Pmtotal denotes the total transmit N
power of femtocell m, i.e., Pmtotal = å Pmn . n=1
That means the transmit power of each group is adjusted based on the RSRP in a distribution manner after the allocation of sub-channels to
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The modeling platform is based on the Ranplan Small-cell® tool suite [10], which is an in-building network planning & optimization software tool produced by Ranplan Wireless Network Design Ltd., and path loss model is Ranplan Radio-wave Propagation Simulator (RRPS). RRPS implements Intelligent Ray Launching Algorithm (IRLA) [13], which is based on ray tracing/ray launching techniques to model the physical characteristics of the rough terrain and urban building features, performs the electromagnetic calculations, and then evaluates the signal propagation characteristics. The propagation model offers to predict coverage and other channel characteristics such as Angles of Arrival (AoA), Power Delay Profile (PDP). LTE system simulation is based on 9-floor Office building with 4 femtocells deployment on each floor, where 12 dB penetration loss for a heavy wall and 20 dB penetration loss for a floor are considered. The deployment of femtocell network is shown in Figure 1, and the simulation parameters are given in Table I [14]. In the proposed scheme, how to select the grouping threshold is a key step, which will affect the system performance. Coverage radius-based grouping method [8] can obtain good performance in the outdoor scenario, but in the building, due to the irregular coverage area, the performance will be degraded. Minimizing capacity-based method can find the optimal threshold, but it has high complexity. In the paper, simulation-based method is used to find the suboptimal threshold, where the femtocells are classified as 4 groups, and the grouping thresholds are 1dB, 2.5 dB, and 6 dB, respectively, and the number of femtocells in these groups are 3, 3, 4, and 2, respectively, i.e. for a UE on the cell-edge region, total 12 femtocells should be considered to group due to the strength interference from each other, and the femtocells, which are Qs - Qi ≤ 1dB , are added in the first group, which has the
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strongest interference, the femtocells, which are 1 < Qs - Qi ≤ 2.5 , are added in the second group, the third group is based on 2.5 < Qs - Qi ≤ 6 , and the fourth group is based on 6 < Qs - Qi , which has the weakest interference.
building, which is a 3-dimensional interference environment with dense interference sources, up to 11 interference femtocells. Therefore, using legacy soft frequency reuse will decrease the system spectrum efficiency because too much frequency partition will result in the low frequency reuse gain.
Table I Simulation parameters Parameter
Value
Cell layout
36 Femtocells
Area
9 600 m2 with 9 floors
Path loss model Shadowing Bandwidth # of resource blocks (RB) Femtocell Tx power Femtocell antenna
iBuildNet RRPS propagation modeling Lognormal shadowing with 3 dB std for LOS, 4 dB for NLOS 2 GHz, 10 MHz 50 8 dBm 0 dBi, Omnidirectional
UE antenna gain
0 dBi
UE noise figure Downlink scheduler UE number
5 dB Round Robin
User traffic model
Full Buffer
Fig.7 Best signal level
25 UEs per cell/5 RBs per UE
Figure 7 shows the best signal level of one floor, where 2 femtocells is deployed. From this figure, we can see that the signal levels to one location are up to 60dBm and 64dBm, respectively, which means that there have severe interference at the femtocell edge point. Figure 8 shows the cross-floor transmission of femtocell signal, where a femtocell on the 3rd floor is shown. From this figure, comparing to the 41 dBm signal level on this floor, it can be seen that the signal strength on the 2nd and 4th floors are up to 77 dBm and 66 dBm, respectively, and the signal strength are also up 100 dBm on the 1st and 5th floors, which shows cross-floor signal penetration will greatly affect the system performance. Figures 7-8 mean that a UE on the 4th floors will receive the interference from other femtocells not only on the 4th floor, but also on the 3rd and 5th floors. Therefore, the signal penetrating from other floors will make inter-cell interference more complicated in the
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Fig.8 Cross-floor signal level
Figure 9 depicts the objective function as function of the instantaneous SINR γ. It can be seen that the optimal size of the centre region is achieved with Gth = 6.5dB , which means the region where SINR is less than 6.5 dB is considered as a cell-edge region, and UE in this region will be scheduled based on the proposed adaptive soft frequency reuse scheme. Figure 10 presents the SINR and data rate map with legacy soft frequency reuse scheme. From the figures, it can be observed that the in-building environment is more complex, and
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Fig.9 Optimal SINR threshold
encounters more penetration loss and shadowing fading. But due to the transmission loss of wall and pillar, the signals between the
femtocells can be well isolated, so the interference can be reduced. Therefore, in the femtocell deployment, walls and pillars can be utilized to form directional transmission. But at the cell-edge region there have low SINR and data rate. Figure 11 presents the SINR and data rate map of proposed scheme when Gth = 6.5dB. Comparing to Figure 10, it can be observed the proposed adaptive soft frequency reuse scheme can greatly improve the cell centre and cell-edge performance up to 3.7 dB and 3.2 dB, respectively, as shown in Figure 12. Figure 13 shows the CDF of the spectrum efficiency when Gth = 6.5dB . It can be seen that the universal frequency reuse scheme can have the good system spectrum efficiency but
Fig.10 SINR and data rate map based on legacy soft FFR
Fig.11 SINR and data rate map of proposed scheme according to Gth = 6.5dB
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Fig.12 CDF of SINR for legacy soft FFR and proposed scheme
it cannot provide desired cell-edge performance. On the other hand, using the legacy soft frequency reuse [5] provides relatively low spectrum efficiency due to the multiple dominant interferences in the dense femtocell networks, which means that too much frequency partition will degrade the frequency reuse gain, and then degrades the system performance. It can be also observed that the system performance of proposed scheme is close to that of universal frequency reuse in the centre of femtocell while cell-edge performance is better than that of legacy soft frequency reuse. From the table II, comparing with the legacy soft FFR scheme, the average performance gain is up to 10.12%, and cell-edge performance gain is up to 25.3%. It is reasonable because the proposed scheme can adjust the FRF and transmit power for different interference groups, and in the severe interference region, the FRF is the smallest, which is close to the FRF of legacy soft frequency reuse. Table II Spectrum efficiency comparison Average spectrum efficiency (bps/Hz/cell)
Cell edge spectrum efficiency (bps/Hz)
Universal FR
1.354
0.179
Legacy soft FFR
1.392
0.418
Proposed scheme
1.533
0.524
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Fig.13 CDF of spectrum efficiency for different FFR schemes
VI. CONCLUSIONS AND FUTURE WORKS Small cell networks, particularly dense femtocell networks, are promising to provide extra capacity for high indoor data demands in enterprise environments. However, dense femtocell networks will introduce excessive inter-cell interference in the 3D building scenarios. More irregular neighbor cell boundaries will be created due to cross-floor signal penetration and then efficient interference mitigation schemes are required. In this paper, an adaptive soft frequency reuse scheme was proposed for interference management in dense femtocell networks. Based on RSRP, it classifies all interfering femtocells into a number of groups, and then selects the frequency reuse factor and the transmit power for different groups according to the interference strength. Furthermore, a power adaptive scheme in which can effectively control the level of coverage between the cell-edge and cell-centre region was devised, and optimal SINR threshold was given by simulation. The final simulation results show that the proposed scheme achieves not only an enhanced cell-edge spectral efficiency, but also minimal degradation of a forthcoming co-channel femtocell deployment. However, Due to the different load and re-
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source scheduling of each femtocell, the interference environment is time-varying; thus, inter-femtocell interference analysis is also a new challenge for interference coordination or cancellation. These problems need to be further investigated in the future works. At the same time, how to group the femtocells optimally will be the work of next step.
Proceedings of the IEEE International Conference on Communications: August 15-17, 2012, Beijing, China, 2012: 530-534. ®
[10] RANPLAN. SMALL-CELL [EB/OL]. [2001] http: //www.ranplan.co.uk. [11] NAJJAR A, HAMDI N, BOUALLEGUE A. Efficient Frequency
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OFDMA Systems[C]// Proceedings of the IEEE Symposium on Computers and Communications: July 5-8, 2009, Sousse, Tunisia, 2009:
ACKNOWLEDGEMENT
261–265. [12] PROAKIS J G. Digital Communications (5th
This work was supported by the EU-FP7 iPLAN under Grant No.230745 and EU-FP7 IAPP@RANPLAN under Grant No.218309.
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3GPP, Coordinated Multi-point Operation for LTE: Physical Layer Aspects[S], 3GPP Technical Report (TR 36.819) v1.1.0, August, 2011.
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Biographies CHEN Jiming, received his M.S. and Ph.D. degrees in communication and information system from University of Electronic and Science Technology, China in 2003 and 2006, respectively. Now he is a senior research fellow at Ranplan Wireless Network Design Ltd. (UK) from 2011. Prior to the time, he was a research scientist in Bell labs at Alcatel-Lucent Shanghai Bell Ltd. from 2006 to 2011, and then became a member of Alcatel-Lucent Technical Academy in 2010. In Bell labs his research interests were the key techniques of cooperative processing and small cells for 3GPP LTE-A system. Currently, he focuses on the small cell technologies and multiple wireless system-level simulation, such as UMTS/LTE/WiFi, for indoor and outdoor scenarios, and implement in Ranplan planning and optimization tools. Up to now, he held about 21 international patents and 23 publications. Email: [email protected] WANG Peng, received his B.S. and M.Phil. degrees from Centre for Wireless Network Design (CWiND) of University of Bedfordshire (UK). He joined Ranplan Wireless Network Design Ltd. (UK) as an engineer in 2008, and became senior engineer in 2010. He participated in executing and managing EU 7th Frame-
Town, South Africa, 2010: 1-5.
work projects at Ranplan. He was also involved in
CHEN Jiming, WANG Peng, and ZHENG Jie.
developing of a world leading automatic indoor radio
Adaptive Soft Frequency Reuse Scheme for
network planning and optimization tool suite-
Inbuilding Dense Femtocell Networks[C]//
iBuildNet. Email: [email protected]
China Communications January 2013
ZHANG Jie, is a full professor and holds the Chair in
(EPSRC), the European Commission (EC) FP6/FP7 and
Wireless Systems at the Department of Electronic and
the industries etc. He is a lead author of the book
Electrical Engineering, the University of Sheffield, UK.
"Femtocells: Technologies and Deployment" (Wiley,
His research interests are focused on radio propaga-
Jan. 2010). He and his colleagues published a widely
tion, indoor-outdoor radio network planning and
cited femtocell paper "OFDMA femtocells: A roadmap
optimization, small/femto cell, HetNet, Self-Organi-
on interference avoidance". He was involved in the
sing Network (SON) and smart building/city. Since
setting up of RANPLAN Wireless Network Design Ltd.
2006, he has been an Investigator of over 20 research
(www.ranplan.co.uk) that produces the world leading
projects worth over £17 million funded by the Engi-
Small Cell and HetNet planning and optimization
neering and Physical Science Research Council
tools. Email: [email protected]
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