Chance Constrained Robust Downlink Beamforming in Multicell Networks

We introduce a downlink robust optimization approach that minimizes a combination of total transmit power by a multiple antenna base station (BS) within a cell and the resulting aggregate inter-cell interference (ICI) power on the users of the other cells. This optimization is constrained to assure that a set of signal-to-interference-plus-noise ratio (SINR) targets are met at user terminals with certain outage probabilities. The outages are due to the uncertainties that naturally emerge in the estimation of channel covariance matrices between a BS and its intra-cell local users as well as the other users of the other cells. We model these uncertainties using random matrices, analyze their statistical behaviour and formulate a tractable probabilistic approach to the design of optimal robust downlink beamforming vectors. The proposed approach reformulates the original intractable non-convex problem in a semidefinite programming (SDP) form with linear matrix inequality (LMI) constraints. The resulting SDP formulation is convex and numerically tractable under the standard rank relaxation. We compare the proposed chance-constrained approach against two different robust design schemes as well as the worst-case robustness. The simulation results confirm better power efficiency and higher resilience against channel uncertainties of the proposed approach in realistic scenarios

Chance constrained robust downlink beamforming in multicell networks

We introduce a downlink robust optimization approach that minimizes a combination of total transmit power by a multiple antenna base station (BS) within a cell and the resulting aggregate inter-cell interference (ICI) power on the users of the other cells. This optimization is constrained to assure that a set of signal-to-interference-plus-noise ratio (SINR) targets are met at user terminals with certain outage probabilities. The outages are due to the uncertainties that naturally emerge in the estimation of channel covariance matrices between a BS and its intra-cell local users as well as the other users of the other cells. We model these uncertainties using random matrices, analyze their statistical behaviour and formulate a tractable probabilistic approach to the design of optimal robust downlink beamforming vectors. The proposed approach reformulates the original intractable non-convex problem in a semidefinite programming (SDP) form with linear matrix inequality (LMI) constraints. The resulting SDP formulation is convex and numerically tractable under the standard rank relaxation. We compare the proposed chance-constrained approach against two different robust design schemes as well as the worst-case robustness. The simulation results confirm better power efficiency and higher resilience against channel uncertainties of the proposed approach in realistic scenarios

Transmit Beamforming in Dense Networks-A Review

Communication technology has prospered in manifolds over the last decade. The scarcity of spectrum as well as the demand for higher data rates and increase in capacity has become a matter of concern. Newer technologies have evolved time and again, the latest of which is Long Term Evolution (LTE) and Long Term Evolution Advanced (LTE-A) systems more commonly known as 4G technology. The striking feature of LTE/LTE-A is the deployment of smaller cells (femto cells) in the vicinity of a large macro cells resulting in a dense network. As a result the data rate as well as capacity has increased in manifolds but the detrimental factor is the issue of interference between the various cells. Beamforming provides a solution in removing the issues of interference in dense networks. This paper focuses on the interference scenario in LTE dense networks and gives an overview of different beamforming methods that can provide a solution to the interference problem. Further, a review of several such methods so far proposed in available literature has been presented in this paper.Keywords:LTE/LTE-A, Dense Network, Interference,Beamformin

Self-Dimensioning and Planning of Small Cell Capacity in Multitenant 5G Networks

An important concept in the fifth generation of mobile networks is multitenancy, which allows diverse operators sharing the same wireless infrastructure. To support this feature in conjunction with the challenging performance requirements of future networks, more automated and faster planning of the required radio capacity is needed. Likewise, installing small cells is an effective resource to provide greater performance and capacity to both indoor and outdoor places. This paper proposes a new framework for automated cell planning in multitenant small cell networks. In particular, taking advantage of the available network data, a set of detailed planning specifications over time and space domains are generated in order to meet the contracted capacity by each tenant. Then, the network infrastructure and configuration are updated according to an algorithm that considers different actions such as adding/removing channels and adding or relocating small cells. The simulation results show the effectiveness of various methods to derive the planning specifications depending on the correlation between the tenant's and network's traffic demands

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

Mobile data and computation offloading in mobile cloud computing

Le trafic mobile augmente considérablement en raison de la popularité des appareils mobiles et des applications mobiles. Le déchargement de données mobiles est une solution permettant de réduire la congestion du réseau cellulaire. Le déchargement de calcul mobile peut déplacer les tâches de calcul d'appareils mobiles vers le cloud. Dans cette thèse, nous étudions d'abord le problème du déchargement de données mobiles dans l'architecture du cloud computing mobile. Afin de minimiser les coûts de transmission des données, nous formulons le processus de déchargement des données sous la forme d'un processus de décision de Markov à horizon fini. Nous proposons deux algorithmes de déchargement des données pour un coût minimal. Ensuite, nous considérons un marché sur lequel un opérateur de réseau mobile peut vendre de la bande passante à des utilisateurs mobiles. Nous formulons ce problème sous la forme d'une enchère comportant plusieurs éléments afin de maximiser les bénéfices de l'opérateur de réseau mobile. Nous proposons un algorithme d'optimisation robuste et deux algorithmes itératifs pour résoudre ce problème. Enfin, nous nous concentrons sur les problèmes d'équilibrage de charge afin de minimiser la latence du déchargement des calculs. Nous formulons ce problème comme un jeu de population. Nous proposons deux algorithmes d'équilibrage de la charge de travail basés sur la dynamique évolutive et des protocoles de révision. Les résultats de la simulation montrent l'efficacité et la robustesse des méthodes proposées.Global mobile traffic is increasing dramatically due to the popularity of smart mobile devices and data hungry mobile applications. Mobile data offloading is considered as a promising solution to alleviate congestion in cellular network. Mobile computation offloading can move computation intensive tasks and large data storage from mobile devices to cloud. In this thesis, we first study mobile data offloading problem under the architecture of mobile cloud computing. In order to minimize the overall cost for data delivery, we formulate the data offloading process, as a finite horizon Markov decision process, and we propose two data offloading algorithms to achieve minimal communication cost. Then, we consider a mobile data offloading market where mobile network operator can sell bandwidth to mobile users. We formulate this problem as a multi-item auction in order to maximize the profit of mobile network operator. We propose one robust optimization algorithm and two iterative algorithms to solve this problem. Finally, we investigate computation offloading problem in mobile edge computing. We focus on workload balancing problems to minimize the transmission latency and computation latency of computation offloading. We formulate this problem as a population game, in order to analyze the aggregate offloading decisions, and we propose two workload balancing algorithms based on evolutionary dynamics and revision protocols. Simulation results show the efficiency and robustness of our proposed methods

Beamforming Techniques for Non-Orthogonal Multiple Access in 5G Cellular Networks

In this paper, we develop various beamforming techniques for downlink transmission for multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) systems. First, a beamforming approach with perfect channel state information (CSI) is investigated to provide the required quality of service (QoS) for all users. Taylor series approximation and semidefinite relaxation (SDR) techniques are employed to reformulate the original non-convex power minimization problem to a tractable one. Further, a fairness-based beamforming approach is proposed through a max-min formulation to maintain fairness between users. Next, we consider a robust scheme by incorporating channel uncertainties, where the transmit power is minimized while satisfying the outage probability requirement at each user. Through exploiting the SDR approach, the original non-convex problem is reformulated in a linear matrix inequality (LMI) form to obtain the optimal solution. Numerical results demonstrate that the robust scheme can achieve better performance compared to the non-robust scheme in terms of the rate satisfaction ratio. Further, simulation results confirm that NOMA consumes a little over half transmit power needed by OMA for the same data rate requirements. Hence, NOMA has the potential to significantly improve the system performance in terms of transmit power consumption in future 5G networks and beyond.Comment: accepted to publish in IEEE Transactions on Vehicular Technolog