Ultra Dense Small Cell Networks: Turning Density Into Energy Efficiency

Samarakoon, Sumudu,Bennis, Mehdi,Saad, Walid,Debbah, Merouane,Latva-aho, Matti IEEE 2016 IEEE journal on selected areas in communications Vol.34 No.5

<P>In this paper, a novel approach for joint power control and user scheduling is proposed for optimizing energy efficiency (EE), in terms of bits per unit energy, in ultra dense small cell networks (UDNs). Due to severe coupling in interference, this problem is formulated as a dynamic stochastic game (DSG) between small cell base stations (SBSs). This game enables capturing the dynamics of both the queues and channel states of the system. To solve this game, assuming a large homogeneous UDN deployment, the problem is cast as a mean-field game (MFG) in which the MFG equilibrium is analyzed with the aid of low-complexity tractable partial differential equations. Exploiting the stochastic nature of the problem, user scheduling is formulated as a stochastic optimization problem and solved using the drift plus penalty (DPP) approach in the framework of Lyapunov optimization. Remarkably, it is shown that by weaving notions from Lyapunov optimization and mean-field theory, the proposed solution yields an equilibrium control policy per SBS, which maximizes the network utility while ensuring users' quality-of-service. Simulation results show that the proposed approach achieves up to 70.7% gains in EE and 99.5% reductions in the network's outage probabilities compared to a baseline model, which focuses on improving EE while attempting to satisfy the users' instantaneous quality-of-service requirements.</P>

Dynamic Clustering and <small>on</small>/<small>off</small> Strategies for Wireless Small Cell Networks

Samarakoon, Sumudu,Bennis, Mehdi,Saad, Walid,Latva-aho, Matti IEEE 2016 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS Vol.15 No.3

<P>In this paper, a novel cluster-based approach for maximizing the energy efficiency of wireless small cell networks is proposed. A dynamic mechanism is proposed to locally group coupled small cell base stations (SBSs) into clusters based on location and traffic load. Within each formed cluster, SBSs coordinate their transmission parameters to minimize a cost function, which captures the tradeoffs between energy efficiency and flow level performance, while satisfying their users' quality-of-service requirements. Due to the lack of intercluster communications, clusters compete with one another to improve the overall network's energy efficiency. This intercluster competition is formulated as a noncooperative game between clusters that seek to minimize their respective cost functions. To solve this game, a distributed learning algorithm is proposed using which clusters autonomously choose their optimal transmission strategies based on local information. It is shown that the proposed algorithm converges to a stationary mixed-strategy distribution, which constitutes an epsilon-coarse correlated equilibrium for the studied game. Simulation results show that the proposed approach yields significant performance gains reaching up to 36% of reduced energy expenditures and upto 41% of reduced fractional transfer time compared to conventional approaches.</P>

Joint Load Balancing and Interference Mitigation in 5G Heterogeneous Networks

Vu, Trung Kien,Bennis, Mehdi,Samarakoon, Sumudu,Debbah, Merouane,Latva-aho, Matti IEEE 2017 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS Vol.16 No.9

<P>We study the problem of joint load balancing and interference mitigation in heterogeneous networks in which massive multiple-input multiple-output macro cell base station (BS) equipped with a large number of antennas, overlaid with wireless self-backhauled small cells (SCs), is assumed. Self-backhauled SC BSs with full-duplex communication employing regular antenna arrays serve both macro users and SC users by using the wireless backhaul from macro BS in the same frequency band. We formulate the joint load balancing and interference mitigation problem as a network utility maximization subject to wireless backhaul constraints. Subsequently, leveraging the framework of stochastic optimization, the problem is decoupled into dynamic scheduling of macro cell users, backhaul provisioning of SCs, and offloading macro cell users to SCs as a function of interference and backhaul links. Via numerical results, we show the performance gains of our proposed framework under the impact of SCs density, number of BS antennas, and transmit power levels at low and high frequency bands. It is shown that our proposed approach achieves a 5.6 times gain in terms of cell-edge performance as compared with the closed-access baseline in ultra-dense networks with 350 SC BSs per km(2).</P>

저궤도 위성 네트워크를 위한 심층 강화학습 기반 랜덤액세스 기법

이주형(Lee Ju Hyung),Mehdi Bennis,고영채(Ko Young Chai) 한국통신학회 2022 한국통신학회 학술대회논문집 Vol.2022 No.2

저궤도 (low earth orbit; LEO) 위성 네트워크는 커버리지 성능을 확보하는 글로벌 네트워크 망으로 주목받고 있다. 본 연구에서는 저궤도 위성 네트워크 시나리오에 적합한 랜덤액세스 기법을 제안한다. 해당 액세스 기법은 심층강화학습 방식을 기반으로 지상의 다수의 위성 안테나를 충돌과 지연시간 성능을 고려하여 액세스 시도를 결정할 수 학습시킨다. 학습 결과 기존 LTE, NR 방식에서 사용되는 RACH (random access channel) 방식에 비하여 약 2 배 가량 빠른 접속 지연 성능 확보할 수 있음을 확인하였다.

Big Data Meets Telcos: A Proactive Caching Perspective

Ejder Ba¸stu˘g,Mehdi Bennis,Engin Zeydan,Manhal Abdel Kader,Ilyas Alper Karatepe,Ahmet Salih Er,Mérouane Debbah 한국통신학회 2015 Journal of communications and networks Vol.17 No.6

Mobile cellular networks are becoming increasingly complex to manage while classical deployment/optimization techniques and current solutions (i.e., cell densification, acquiring more spectrum, etc.) are cost-ineffective and thus seen as stopgaps. This calls for development of novel approaches that leverage recent advances in storage/memory, context-awareness, edge/cloud computing, and falls into framework of big data. However, the big data by itself is yet another complex phenomena to handle and comes with its notorious 4V: Velocity, voracity, volume, and variety. In this work, we address these issues in optimization of 5G wireless networks via the notion of proactive caching at the base stations. In particular, we investigate the gains of proactive caching in terms of backhaul offloadings and request satisfactions, while tackling the large-amount of available data for content popularity estimation. In order to estimate the content popularity, we first collect users’ mobile traffic data from a Turkish telecom operator from several base stations in hours of time interval. Then, an analysis is carried out locally on a big data platformand the gains of proactive caching at the base stations are investigated via numerical simulations. It turns out that several gains are possible depending on the level of available information and storage size. For instance, with 10% of content ratings and 15.4Gbyte of storage size (87%of total catalog size), proactive caching achieves 100% of request satisfaction and offloads 98% of the backhaul when considering 16 base stations.

Dynamic Coalition Formation for Network MIMO in Small Cell Networks

Guruacharya, Sudarshan,Niyato, Dusit,Bennis, Mehdi,Dong In Kim IEEE 2013 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS Vol.12 No.10

<P>In this paper, we apply the concepts of network multiple-input-multiple-output (MIMO) to small cell networks. To do so, the issue of imperfect channel state information (CSI) at the transmitter is considered when frequency-division duplexing is used, for which the feedback channel is limited. We first introduce a regret based learning approach to optimize the transmit beamforming parameters for the cases when the feedback channel is temporarily unavailable during deep fades. We then propose a coalition formation game model to cluster the small cell base stations so that they can perform cluster-wise joint beamforming. We take the \tit{recursive core} as the solution concept of the coalition formation game. To obtain the recursive core, we first consider a typical merge-split algorithm. However, we show that this algorithm can be unstable. Alternatively, we adopt the merge-only algorithm which guarantees the formation stability and show that its outcome belongs to the recursive core. Finally, we analyze the average number and the average size of coalitions that can form during such a coalition formation process. Numerical simulations are given to illustrate the behavior of the coalition formation among small cell base stations.</P>

Matching theory for future wireless networks: fundamentals and applications

Yunan Gu,Saad, Walid,Bennis, Mehdi,Debbah, Merouane,Zhu Han Institute of Electrical and Electronics Engineers 2015 IEEE communications magazine Vol.53 No.5

<P>The emergence of novel wireless networking paradigms such as small cell and cognitive radio networks has forever transformed the way in which wireless systems are operated. In particular, the need for self-organizing solutions to manage the scarce spectral resources has become a prevalent theme in many emerging wireless systems. In this article, the first comprehensive tutorial on the use of matching theory, a Nobel Prize winning framework, for resource management in wireless networks is developed. To cater for the unique features of emerging wireless networks, a novel, wireless-oriented classification of matching theory is proposed. Then the key solution concepts and algorithmic implementations of this framework are exposed. The developed concepts are applied in three important wireless networking areas in order to demonstrate the usefulness of this analytical tool. Results show how matching theory can effectively improve the performance of resource allocation in all three applications discussed.</P>

Mobile Unmanned Aerial Vehicles (UAVs) for Energy-Efficient Internet of Things Communications

Mozaffari, Mohammad,Saad, Walid,Bennis, Mehdi,Debbah, Merouane IEEE 2017 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS Vol.16 No.11

<P>In this paper, the efficient deployment and mobility of multiple unmanned aerial vehicles (UAVs), used as aerial base stations to collect data from ground Internet of Things (IoT) devices, are investigated. In particular, to enable reliable uplink communications for the IoT devices with a minimum total transmit power, a novel framework is proposed for jointly optimizing the 3D placement and the mobility of the UAVs, device-UAV association, and uplink power control. First, given the locations of active IoT devices at each time instant, the optimal UAVs’ locations and associations are determined. Next, to dynamically serve the IoT devices in a time-varying network, the optimal mobility patterns of the UAVs are analyzed. To this end, based on the activation process of the IoT devices, the time instances at which the UAVs must update their locations are derived. Moreover, the optimal 3D trajectory of each UAV is obtained in a way that the total energy used for the mobility of the UAVs is minimized while serving the IoT devices. Simulation results show that, using the proposed approach, the total-transmit power of the IoT devices is reduced by 45% compared with a case, in which stationary aerial base stations are deployed. In addition, the proposed approach can yield a maximum of 28% enhanced system reliability compared with the stationary case. The results also reveal an inherent tradeoff between the number of update times, the mobility of the UAVs, and the transmit power of the IoT devices. In essence, a higher number of updates can lead to lower transmit powers for the IoT devices at the cost of an increased mobility for the UAVs.</P>

Learning-Based Small Cell Traffic Balancing Over Licensed and Unlicensed Bands

Sriyananda, M. G. S.,Bennis, Mehdi IEEE 2017 IEEE wireless communications letters Vol.6 No.5

<P>Unlicensed spectrum can be utilized by long term evolution (LTE) cellular systems to satisfy high throughput requirements. In this letter, a regret-based learning aided down-link traffic balancing scheme for licensed and unlicensed bands is proposed while ensuring fair coexistence of LTE-unlicensed (LTE-U) and Wi-Fi devices in the same band. It is further improved with the optimization of energy efficiency (EE) for small cell (SC) and macrocell scenarios followed by an inter-SC interference management mechanism with better performance over the existing literature. Compared to the cases with fixed airtime, up to 8%-10% superior results are shown for the scenarios of EE and rate maximization, respectively.</P>

Dynamic Clustering and User Association in Wireless Small-Cell Networks With Social Considerations

Ashraf, Muhammad Ikram,Bennis, Mehdi,Saad, Walid,Katz, Marcos,Hong, Choong-Seon IEEE 2017 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY Vol.66 No.7

<P>In this paper, a novel social network-aware user association in wireless small cell networks with underlaid device-to-device (D2D) communication is investigated. The proposed approach exploits strategic social relationships between user equipments (UEs) and their physical proximity to optimize the overall network performance. This problem is formulated as a matching game between UEs and their serving nodes (SNs) in which, an SN can be a small cell base station (SCBS) or an important UE with D2D capabilities. The problem is cast as a many-to-one matching game in which UEs and SNs rank one another using preference relations that capture both the wireless aspects (i.e., received signal strength, traffic load, etc.) and users' social ties (e.g., UE proximity and social distance). Due to the combinatorial nature of the network-wide UE-SN matching, the problem is decomposed into a dynamic clustering problem in which SCBSs are grouped into disjoint clusters based on mutual interference. Subsequently, an UE-SN matching game is carried out per cluster. The game under consideration is shown to belong to a class of matching games with externalities arising from interference and peer effects due to users social distance, enabling UEs and SNs to interact with one another until reaching a stable matching. Simulation results show that the proposed social-aware user association approach yields significant performance gains, reaching up to 26%, 24%, and 31% for 5th, 50th, and 95th percentiles for UE throughputs, respectively, as compared to the classical social-unaware baseline.</P>