Downlink interference minimization in cooperative cognitive LTEfemtocell networks
 Xin Tao^{1},
 Zhifeng Zhao^{1}Email author,
 Rongpeng Li^{1},
 Jacques Palicot^{2} and
 Honggang Zhang^{1, 3}
https://doi.org/10.1186/168714992013194
© Tao et al.; licensee Springer. 2013
Received: 30 March 2013
Accepted: 5 July 2013
Published: 18 July 2013
Abstract
Femtocell is considered to be one of the most promising solutions for future indoor wireless communication. Due to the scarcity of spectrum resources, femtocells need to share the spectrum with other networks, which will inevitably bring in severe interference. Therefore, minimizing the crosstier and cotier interference while maintaining high system throughput or spectrum efficiency is one of main challenges before largely deploying femtocell networks. In order to effectively mitigate the interference, cognitive radioenabled techniques can play a key role by providing more secondary spectrum access opportunities, especially in dense femtocells deployment scenarios. Supported by cognitive radio functionality, femtocell users can access and share these licensed spectra including the frequency bands of both macrocells and other licensed systems (e.g., TV white spaces) as long as not causing harmful interference to the coexisting licensed systems. In this paper, based on cognitive sensing, we propose a joint channel assignment and power allocation scheme, aiming to minimize the aggregate interference from multiple femtocells to the licensed users while satisfying the constraints of each femtocell’s capacity and power budget. It is believed that the cooperation among multiple femtocells is quite helpful in mitigating the interference considering the mobility of the licensed users. Specifically, Hungarian algorithm is involved in our scheme to address the cotier femtocell interference issue. In order to illustrate our scheme more explicitly, we come up with the concepts of Physical Cluster and Virtual Cluster and synthetically apply the related algorithms to reduce the interference step by step. Finally, the performances of employed algorithms are evaluated and analyzed. Numerical results have validated that the proposed scheme is viable and effective in managing the femtocell interference.
Keywords
1 Introduction
With the advent of big data era and the emergence of new handheld devices such as tablet PC and smart phones, data intensive applications like online video streaming and network gaming have inexorably occupied more and more users’ focus. Future mobile wireless networks call for higher data rate for providing more high quality services and better user experience. Recent studies have suggested that this rapidly increasing demand for high data rate is chiefly generated from indoor environments [1], where exist more than 50% voice calls and 70% data traffic [2]. However, indoor radio coverage is generally poor due to the wall penetration losses inside buildings especially when the user is located in the cell edge. This clear discrepancy between high data rate demand and low received signal to interference and noise ratio (SINR) leads to many research discussions. The idea of femtocells, which are principally designed to extend macro cellular services into indoor environments, is one of them.
Femtocells are smallcoverage, lowcost, plugandplay networking systems, where a femtocell access point (FAP) or femtocell base station is installed at home or in an office. Afterwards, the indoor femtocell user equipment (FUE) can be connected to the FAP instead of a macrocell base station to get highquality voice and data services with much lower power consumption, and all the network traffic will be backhauled to the macrocell network and/or the internet via either wired broadband connections such as digital subscriber line, passive optical network, or a divided wireless backhaul channel [3]. The FAP is also called as Home Node B in WCDMA systems and Home e Node B in longterm evolution (LTE) systems in the 3GPP femtocell standardization [4]. And the latter, namely LTEfemtocells, using orthogonal frequencydivision multiple access (OFDMA) as the physical layer technology, are considered as one of the most promising solutions for future indoor wireless communication with large economic potentials.
Despite the many advantages of femtocells, however, there are a number of challenges in technical, regulatory, and economic aspects that need to be addressed systematically. The works of Zahir et al. [5] and Mhiri et al. [6] provide an overview of the main research challenges toward the deployment of femtocells, among which interference management including the crosstier and intratier interference is one of the biggest technical challenges. In the coexisting macrocellfemtocell networks, macrocells and femtocells interfere with each other for spectrum sharing, and there is also mutual interference among femtocells. In fact, the interference problem can be extremely intractable in a dense deployment scenario due to the lack of spectrum resources. Given that, various interference management strategies have been proposed to address this issue including for instance, collaborative resource allocation [7], fractional frequency reuse (FFR) [8, 9], directional beamforming [10], cognitive radio approach [11], and power control [12–17]. Among variety of methods, power control has been extensively researched and used as an effective interference mitigation solution for both crosstier and cotier interference. Specifically, distributed solutions like game theory [12] or reinforcement learning [13] could explore appropriate power level to minimize the crosstier interference in largescale deployments. More often than not, power control can be combined with other methods like cognitive radio (CR) to reduce the interference. For that reason, we will put more attention on the related work later.
There have been a substantial research focusing on the interference mitigation through power control. In [14], the authors have studied the downlink crosstier interference problem in macrofemto twotier networks with shared spectrum, and a distributed power control scheme is proposed and analyzed. In [15], the authors have studied downlink spectrum sharing cotier interference in an overlay mode in cognitive femtocell networks. Then, they employed dual decomposition method to solve the problem and proposed a joint channel allocation and fast power control scheme. In [16], resource allocation in open access OFDMA femtocell networks has been studied, while a new resource allocation method is proposed to reduce crosstier interference and improve performance of both neighboring macrocell users and femtocell users. In [17], a subcarrier and power allocation method has been presented to manage crosstier interference in underlay femtocell networks. Basically, these papers only take the interference power as a constraint rather than an optimization objective. In that case, they may not be applicable when the situations vary. Moreover, [15, 16], and [17] all tackled power control problem in a distributed manner due to the selforganizing feature of femtocell networks, and they all involved CR technology to mitigate the interference efficiently.
Indeed, the interference generated by femtocells will tend to be a localized phenomenon when the femtocells are heavily deployed in urban areas in the future. Since the FAP coverage is much smaller, CR technology could play a crucial role in obtaining this localized interference information including sensing, processing, and decision making. The Federal Communications Commission in USA has authorized dynamic spectrum access operation for cognitive radio in TV white spaces (TVWS) since 2008 [18], which has created new opportunities for femtocells to utilize TVWS for interference mitigation. Interference study in [19] mainly focused on cognitive LTEfemtocell in TV white spaces. The paper proposed two interferenceavoiding antenna schemes as a reference for future cognitive femtocell deployment using TV white spaces, which can also be a solution to ensure successful femtocell operation.
Due to the complexity in the real implementation scenarios, regulations relative to TV white spaces may not be fully implemented, and even meeting all the regulatory requirements cannot guarantee that the primary users are not influenced completely. Moreover, the interference threshold varies a lot under different circumstances, which means that the power allocation algorithms taking the interference as a constraint may be not very effective in a more realistic setting. Additionally, most prior studies [14–17] address either crosstier or cotier femtocell interference in isolation under the assumption that the other kind of interference is already well resolved. In this paper, a new interference mitigation scheme is presented to address both cotier and crosstier interference problem for future cognitive LTEfemtocell networks. We take the interference power as the optimization objective, which is different from the ideas of the related papers mentioned above. Through cognitive spectrum sensing [20, 21], joint macrofemto channel scheduling, or other spectrum utilization approaches (e.g., authorized shared access proposed by Qualcomm and its partners), femtocell users in a local area can obtain accessible channels. Then, channel and power resources can be collaboratively allocated among multiple femtocells for interference mitigation based on the physical cluster and the virtual cluster. However, the performance gain is achieved at the cost of some cooperative overheads including the exchange of information like access channel, location, link quality estimation and mobility of PUs among femtocells. Basically, our contributions can be summarized as follows:

We come up with the new concepts of physical cluster (PC) and virtual cluster (VC) for multiple femtocells to collaboratively allocate resources.

We propose two independent algorithms including subcarrier power allocation algorithm for interference minimization in a single femtocell and virtual clusterbased power budget adjustment algorithm to be part of solutions for the femtocell interference management.

We employ Hungarian algorithm, which is a typical solution to the linear task allocation problem, to minimize crosstier interference from femtocells to the users of licensed systems including macrocell networks and TV broadcast systems while avoiding cotier femtocell interference based on the physical cluster.

We recommend femtocells to utilize TVWS through cognitive sensing and propose an integrated joint channel assignment and power allocation scheme to deal with the interference problem for femtocells with fewer available channels in a dense deployment scenario.
The rest of the paper is organized as follows: Section 2 describes the system model as well as the concepts of PC and VC. In Section 3, the primary interference minimization problem is formulated, derived, and analyzed. Afterward, a solution algorithm for interference minimization in a single femtocell will be provided. In section 4, two secondary problems are illustrated respectively, and relevant algorithms are presented. And then, we incorporate both Sections 3 and 4 together to form our proposed integrated scheme. Numerical results are given in Section 5, while Section 6 concludes the paper.
2 System model
where d denotes the distance (m) between the FAP and the FUE, f_{ c } is the carrier frequency (GHz), and L_{W} represents the wall penetration loss (dB) with L_{W} = 5n_{w} for light walls and L_{W} = 12n_{w} for heavy walls where n_{w} is the number of walls between BS and MS.
where G_{k,i} is the channel power gain between the k th subcarrier of the i th femtocell and the PU receiver. d_{k,i} is the spectral distance between the k th subcarrier of the i th femtocell and the PU band. Φ_{k,i} is the PSD of the k th subcarrier of the i th femtocell. Besides, the expression of the PSD depends on the adopted multicarrier technique, such as OFDM. P_{k,i} denotes the transmission power emitted by the k th subcarrier of the i th femtocell and Ω_{k,i} denotes the interference factor of the k th subcarrier of the i th femtocell. We can see that Ω_{k,i} is mainly associated with G_{k,i} if OFDM technique is adopted.
The level of interference induced by the femtocell varies depending on the distance between the femtocell and other systems as well as the transmission power of the FAP. Thus, we may firstly assign the available channels to the femtocells based on the distance and then adjust the power budgets of different FAPs to alleviate the underlying interference. Traditional graph coloring approach is not efficient for lack of enough channels. To deal with this problem, we come up with the new concepts of physical cluster (PC) and virtual cluster (VC), which is essentially a question of femtocell grouping.
where γ_{0} is the minimum spatial correlation depending on safety distance d_{0}. Each physical cluster has a clustering center that could be found through some clustering algorithms. In a dense deployment scenario, there are quite finite channels, say six channels (1.4 MHz), for femtocells so that the number of femtocells grouped in a PC cannot be larger than 6. That is because femtocells in a PC need to use different channels to avoid cotier interference.
where ${\gamma}_{0}^{\prime}=R/(2R+{d}_{0})$.If R is defined as half of the d_{0}, then we have ${\gamma}_{0}^{\prime}=1/4$. In other words, member femtocells operating on the same channel in a virtual cluster should be separated as far as possible to guarantee minimum interference.
3 Problem formulation and interference mitigation
Given the fact that the interference generated by femtocells tends to be a localized phenomenon due to the small coverage and large number of femtocells, centralized methods may be confronted with more challenges with limited control and instruction information from the radio network controller, which implies local and possibly distributed solutions will be more practical and efficient. Following this idea, we are interested in the feasibility of interference minimization by multiple femtocells collaboratively in a local area. In this section, the primary problem will be formulated and analyzed. And in the following section, two secondary problems will be illustrated, respectively. Finally, we will incorporate them together to produce our proposed scheme.
where ρ_{k,j,i} denotes the subcarrier allocation index. If the k th subcarrier is allocated to the j th FUE of the i th femtocell, ρ_{k,j,i} = 1, otherwise ρ_{k,j,i} = 0. P_{k,j,i} denotes the transmission power in the k th subcarrier from the i th FAP to the j th FUE, and Ω_{k,j,i} denotes the interference factor of the j th FUE of the i th femtocell in the k th subcarrier. C_{k,j,i} denotes the capacity of the j th FUE of the i th femtocell in the k th subcarrier, and ${C}_{{T}_{i}}$ denotes the total capacity requirement of the i th femtocell. ${P}_{{T}_{i}}$ denotes the total power budget of the i th femtocell. N is the number of femtocells, M is the number of the users of each femtocell (usually 2 to 4), while K is the total number of subcarriers in each femtocell.
where H_{k,j} denotes the channel power gain of the k th subcarrier from the FAP to the j th FUE. And σ^{2} = I_{k,j}+P_{ N } where I_{k,j} denotes the interference power of the j th FUE in the k th subcarrier while P_{ N } denotes the power of noise. The assignment is mainly considering the channel power gain to interference and noise ratio. The maximum data rate in downlink can be obtained if the subcarriers are assigned to the user who has the best channel gain for that subcarrier. In this regard, other effective assignment strategy may also be applicable.
where [ x] ^{+} = max(0,x).
The proof is detailed in the ‘6’ section.
However, it is computationally complex to solve more than one Lagrangian multiplier shown in Eq. (13), these multipliers can be found numerically using ellipsoid or interior point method with a polynomial time complexity o(N^{3}) [25]. In addition, the solution indicates that aggregate interference minimization of multiple femtocells tends to be a distributed result, which means that as long as the interference from each femtocell is minimized, the aggregate interference of multiple femtocells will reach the minimum level. Actually, this can be regarded as a noncollaborative way for resource allocation. Since the PUs at the cell coverage of the licensed systems should satisfy at least a target SINR or an outage probability, which will produce an acceptable interference threshold, the optimized aggregate interference from multiple femtocells should be controlled under this threshold by appropriately selecting capacity requirement and power budget of each femtocell as well as the number of femtocells. However, this is the case that we did not consider the cotier interference among multiple femtocells. Moreover, the interference component from each femtocell to the cochannel PU is also different. In Section 4, we will further discuss the two secondary problems based on PC and VC.
Interference minimization in a single femtocell is very important in our analysis. Therefore, we will first formulate the subproblem and then provide the solution algorithm since it will be used for collaborative resource allocation in the following section.
3.1 Interference minimization in a single femtocell
Then, we summarize the power of all the subcarriers and compare it with the total power budget. If it is over the budget, the problem P 3 has no solution. Otherwise, i.e., $\sum _{k=1}^{K}{{P}_{k}}^{\prime}\le {P}_{T},\forall k\in \left\{1,2,\cdots \phantom{\rule{0.3em}{0ex}},K\right\}$, then Eqs. (15) and (16) will be the optimal solution for P 3.
Basically, there are two thoughts about the left power. That is, we can add it to all the subcarriers equally or we can add it to the subcarrier with the minimal interference factor Ω_{ k }. In fact, the latter will produce less interference, which is also validated in the numerical simulations section. Therefore, we adopt this strategy, and our proposed interference minimization algorithm can be described in Algorithm 1.
Algorithm 1 Interference minimization (IM) algorithm
4 Collaborative resource allocation based on PC and VC
In Section 3, we formulated the primary optimization problem and provided a solution algorithm for interference minimization in a single femtocell. In other words, Section 3 addressed the issue of crosstier interference minimization from multiple femtocells in a noncollaborative way. In this section, we will concentrate more on dealing with the interfemtocell interference and the cooperation among femtocells.
4.1 Physical clusterbased femtocell channel assignment
where I_{m,n} denotes the minimal interference induced by the m th femtocell using the n th channel to the cochannel primary user and can be calculated by applying Algorithm 1.
It is assumed that each femtocell is assigned only one channel that consists of a group of subcarriers and the primary users using these channels are in different locations. However, when Hungarian algorithm is employed in a PC, certain femtocells cannot be assigned the best channel due to member cooperation. Nevertheless, Hungarian algorithm has got much better performance based on a minimum interference generation criterion, which can be demonstrated in the numerical results section.
It is worthwhile to note that what Hungarian algorithm minimized is the interference to the whole primary system including multiple primary users. As for each primary user, the aggregate interference is not minimized and may be still unacceptable. That is why we take further measures to deal with the problem based on the virtual cluster.
4.2 Virtual clusterbased femtocell power allocation
After the assignment of available channels, interfemtocell interference could be avoided and the interference between femtocells and primary systems could be mitigated to some extent. However, the aggregate interference from multiple femtocells sharing the same channel might be still inadmissible to the cochannel primary user. If a distributed approach is adopted, the femtocells in a virtual cluster is noncollaborative with a fixed power budget. They could have different contributes to the interference generation because of different distances from the victim primary user. Moreover, the mobility of the primary user also leads to the variation of the interference component.
where P_{ i } denotes the power budget of the i th FAP and Ψ_{ i } denotes the pass loss component from the i th FAP to the primary user. P_{total} = N P_{0} where P_{0} denotes the initially fixed power budget for all FAPs while N is the number of the femtocells in a VC. P_{min} and P_{max} are available minimum and maximum power budgets for the FAPs, respectively.
This is a simple linear optimization problem or portfolio optimization problem [25] more exactly. P_{ i } represents the investment in asset i, and the return of each investment is fixed and given by −Ψ_{ i }. It is obvious that we should invest in those assets that have larger rate of return on investment. Then, the concrete solution will be described in the summarized power budget adjustment algorithm (Algorithm 2).
Algorithm 2 Power budget adjustment algorithm
Based on the analysis stated above, we can combine both Sections 3 and 4 together to form our proposed clusterbased cooperative femtocell interference mitigation scheme. Generally, the total implementation procedure is described in Algorithm 3.
Algorithm 3 Proposed clusterbased interference minimization (CIM) algorithm
5 Simulation results
Simulation parameters
Parameter description  Value 

Femtocell radius  10 m 
Maximum number of FUEs per femtocell  4 
Femtocell transmission power (fixed)  10 dBm 
Femtocell transmission power (min)  8 dBm 
Femtocell transmission power (max)  12 dBm 
The noise power  2.4×10^{−13}W 
Light wall penetration loss  5 dB 
Heavy wall penetration loss  12 dB 
Carrier frequency  2 GHz (600 MHz) 
Channel bandwidth  180 KHz 
Subcarrier bandwidth  15 KHz 
Number of subcarriers per channel  12 
It is assumed that there is a light wall between the FAP and the FUE, but a heavy wall between the FAP and the primary user. Without loss of generality, the interference introduced by the primary systems to the FUEs is assumed to be negligible due to the separation of a long enough safety distance. If femtocells are located at the cell margin of the primary systems, there will be no degradation in terms of the capacity of femtocell users.
5.1 Interference minimization in a single femtocell
Figure 3 illustrates the performance of the proposed IM algorithm compared with the other two power allocation schemes. To be specific, it is shown that the total interference produced by IM algorithm is approximately one third of that by average power allocation scheme with a fixed 10dBm power budget of the FAP when the capacity threshold is 10 bit/s/Hz and that proportion becomes one half when the capacity threshold increases to 13.4 bit/s/Hz. The effect of interference mitigation is absolutely remarkable. Additionally, IM algorithm also outperforms left power fair allocation scheme mentioned in subsection 3.1. As the capacity threshold increases, the total interference stays invariable for average power allocation, decreasing for left power fair allocation and increasing for IM algorithm. That is because when capacity threshold increases, the power allocated to each subcarrier increases, which leads to the decrease of left power budget. Thus, the gap between left power fair allocation and IM algorithm will be narrowed. In the extreme cases where there is no left power budget, left power fair allocation and IM algorithm will achieve the same performance.
Figure 4 plots the total interference versus the capacity constraint with different power budgets of the FAP using the proposed IM algorithm in a single femtocell. It is obvious that the total interference increases along with the increase of the capacity threshold and the power budget since it is just a function of the two input parameters. In fact, there is a tradeoff between the desirable minimum total interference and expected maximum system throughput. Thus, the capacity threshold should be appropriately selected to control the interference under a certain level.
Figure 5 describes the influence of distance from the femtocell to the victim primary user. As Figure 5 shows, a longer separation distance to the primary user will make the femtocell generate less interference, which is the most direct and effective approach for interference mitigation. In order to meet the interference threshold of the primary user, a safety distance is required. However, this is not suitable for the case that multiple femtocells use the same channel where the single femtocell safety distance is invalid because of the aggregate interference.
Figure 6 compares the performances of two different frequencies used by the femtocell. Actually, they represent two typical licensed systems, that is, 2 GHz for the macrocell networks and 600 MHz for the TV broadcast system. Figure 6 indicates that the TVWS could satisfy higher capacity requirement than the macrocell frequency bands under different power budget constraints. This result can be attributed to the good transmission character of the TV bands, which also demonstrates that the femtocell could utilize TVWS to achieve higher data rate for more highquality services.
5.2 Physical clusterbased femtocell channel assignment
5.3 Virtual clusterbased femtocell power allocation
Figure 9 extends the range of power budget adjustment but still with a minor interference decrease. This is mainly because the capacity threshold is the same among member femtocells in a VC considering the fairness of the femtocells. If we adjust both the power budget and the capacity threshold of the femtocell according to different interference factors of femtocells, the total aggregate interference will be reduced further. In addition, a large number of femtocells in a VC will definitely result in an increase of total aggregate interference. Therefore, the femtocell number in a VC cannot be excessive in order to control the interference under a certain level.
6 Conclusions
In this paper, an interference minimization scheme in downlink cognitive femtocell networks is proposed. The joint channel assignment and power allocation scheme aims at minimizing the interference from femtocells to the primary users while avoiding the cotier femtocell interference. Based on the physical cluster and the virtual cluster, multiple femtocells could utilize resources cooperatively to mitigate the interference. The related interference minimization problems are formulated, and employed algorithms are combined together to reduce the interference layer by layer. Moreover, by taking advantage of cognitive radio technology as well as joint scheduling, the proposed scheme could address the severe interference issue even with fewer available channels in the heavily deployed femtocell networks. Finally, the numerical simulation results verify that the effect of interference mitigation is generally notable.
Appendix
The proof of Theorem 1.
Proof
Considering ${\mu}_{k,i}{P}_{k,i}^{\ast}=0$, if $\frac{{\sigma}^{2}}{{H}_{k,i}}<\frac{{\alpha}_{i}}{{\Omega}_{k,i}+{\beta}_{i}{\mu}_{k,i}}$, we have μ_{k,i} = 0. Then, ${P}_{k,i}^{\ast}=\frac{{\alpha}_{i}}{{\Omega}_{k,i}+{\beta}_{i}}\frac{{\sigma}^{2}}{{H}_{k,i}}$. Otherwise, if $\frac{{\sigma}^{2}}{{H}_{k,i}}\ge \frac{{\alpha}_{i}}{{\Omega}_{k,i}+{\beta}_{i}{\mu}_{k,i}}$, owing to that ${P}_{k,i}^{\ast}=\frac{{\alpha}_{i}}{{\Omega}_{k,i}+{\beta}_{i}{\mu}_{k,i}}\frac{{\sigma}^{2}}{{H}_{k,i}}\ge 0$, we can get ${P}_{k,i}^{\ast}=0$. Summarizing the above derivations achieves the claim. □
Declarations
Acknowledgements
This paper is partially supported by the National Basic Research Program of China (973 Program 2012CB316000) and the National Natural Science Foundation of China (NSFC) under grant number 61071130. Moreover, this work has received a French government support granted to the CominLabs excellence laboratory and managed by the French National Research Agency in the “Investing for the Future” program under reference ANR10LABX0701. The authors would also like to thank the Bretagne Region, France, for its support of this work.
Authors’ Affiliations
References
 Chandrasekhar V, Andrews J, Gatherer A: Femtocell networks: A survey. IEEE Commun. Mag 2008, 46: 5967.View ArticleGoogle Scholar
 Wang S, et al.: Low energy indoor network: deployment optimisation. EURASIP J. Wireless Commun. Networking 2012, 2012: 193.View ArticleGoogle Scholar
 Kulkarni P, Chin WH, Farnham T: Radio resource management considerations for LTE femto cells. ACM SIGCOMM Comput. Commun. Rev 2010, 40: 2630.View ArticleGoogle Scholar
 3GPP Release 8 Accessed September 2012 [http://www.3gpp.org/Release8.html] Accessed September 2012
 Zahir T, Arshad K, Nakata A, Moessner K: Interference management in femtocell. IEEE Commun. Surv. Tutorials 2012, 99: 119.Google Scholar
 Mhiri F, Sethom K, Bouallegue R: A survey on interference management techniques in femtocell selforganizing networks. J. Netw. Comput. Appli 2013, 36: 5865.View ArticleGoogle Scholar
 Sundaresan K, Rangarajan S: Efficient resource management in OFDMA Femto cells. In Proceedings of MobiHoc 2009. Louisiana: ACM; May 2009.Google Scholar
 Lee HC, Oh DC, Lee YH: Mitigation of interfemtocell interference with adaptive fractional frequency reuse. In Proceedings of ICC 2010. Cape Town: IEEE; May 2010.Google Scholar
 Novlan T, Andrews JG, Sohn I, Ganti RK: Comparison of fractional frequency reuse approaches in the OFDMA cellular downlink. In Proceedings of GLOBECOM 2010. Miami: IEEE; December 2010.Google Scholar
 Zhu J, Yang HC: Interference control with beamforming coordination for twotier femtocell networks and its performance analysis. In Proceedings of ICC 2011. Kyoto: IEEE; June 2011.Google Scholar
 Cheng SM, Ao WC, Tseng FM, Chen KC: Design and analysis of downlink spectrum sharing in twotier cognitive femto networks. IEEE Trans. Vehicular Tech 2012, 61: 21942207.View ArticleGoogle Scholar
 Yun JH, Shin KG: Adaptive interference management of OFDMA femtocells for cochannel deployment. IEEE J. Selected Areas Commun 2011, 29: 12251241.View ArticleGoogle Scholar
 Bennis M, Niyato D: A Qlearning based approach to interference avoidance in selforganized femtocell networks. In Proceedings of GC Wkshps 2010. Miami: IEEE; December 2010.Google Scholar
 Chandrasekhar V, Andrews J, Muharemovic T, Shen Z, Gatherer A: Power control in twotier femtocell networks. IEEE Trans. Wireless Commun 2009, 8: 43164328.View ArticleGoogle Scholar
 Xiang J, Zhang Y, Skeie T, Xie L: Downlink spectrum sharing for cognitive radio femtocell networks. IEEE Syst. J 2010, 4: 524534.View ArticleGoogle Scholar
 Li L, Xu C, Tao M: Resource allocation in open access OFDMA femtocell networks. IEEE Wireless Commun. Lett 2012, 1: 625628.View ArticleGoogle Scholar
 Gupta NK, Banerjee A: Power and subcarrier allocation for OFDMA femtocell based underlay cognitive radio in a twotier network. In Proceedings of IMSAA 2011. Bangalore: IEEE; December 2011.Google Scholar
 FCC: Second report and order and memorandum opinion and order, ET Docket No. 08260. Washington: FCC; 2008.Google Scholar
 Zhao Z, Schellmann M, Boulaaba H, Schulz E: Interference study for cognitive LTEFemtocell in TV white spaces. In Proceedings of ITU WT 2011. Geneva; October 2011.Google Scholar
 Wu Q, Ding G, Wang J, Yao YD: Spatialtemporal opportunity detection for spectrumheterogeneous cognitive radio networks: twodimensional sensing. IEEE Trans. Wireless Commun 2013, 12: 516526.View ArticleGoogle Scholar
 Ding G, Wu Q, Song F, Wang J: Decentralized sensor selection for cooperative spectrum sensing using unsupervised learning. In Proceedings of ICC 2012. Ottawa: IEEE; June 2012.Google Scholar
 Tariq F, Dooley LS, Poulton AS: Virtual clustering for resource management in cognitive femtocell networks. In Proceedings of ICUMT 2011. Budapest; October 2011.Google Scholar
 Kysti P, et al.: WINNER II channel models. D1.1.2 v 1.1, Sep. 2007. Accessed September 2012 http://www.istwinner.org/deliverables.htmlGoogle Scholar
 Shaat M, Bader F: A twostep resource allocation algorithm in Multicarrier based cognitive radio systems. In Proceedings of WCNC 2010. Sydney: IEEE; April 2010.Google Scholar
 Boyd S, Vandenberghe L: Convex Optimization. Cambridge: Cambridge University Press; 2004.View ArticleGoogle Scholar
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