Separation Framework: An Enabler for Cooperative and D2D Communication for Future 5G Networks

Soaring capacity and coverage demands dictate that future cellular networks need to soon migrate towards ultra-dense networks. However, network densification comes with a host of challenges that include compromised energy efficiency, complex interference management, cumbersome mobility management, burdensome signaling overheads and higher backhaul costs. Interestingly, most of the problems, that beleaguer network densification, stem from legacy networks' one common feature i.e., tight coupling between the control and data planes regardless of their degree of heterogeneity and cell density. Consequently, in wake of 5G, control and data planes separation architecture (SARC) has recently been conceived as a promising paradigm that has potential to address most of aforementioned challenges. In this article, we review various proposals that have been presented in literature so far to enable SARC. More specifically, we analyze how and to what degree various SARC proposals address the four main challenges in network densification namely: energy efficiency, system level capacity maximization, interference management and mobility management. We then focus on two salient features of future cellular networks that have not yet been adapted in legacy networks at wide scale and thus remain a hallmark of 5G, i.e., coordinated multipoint (CoMP), and device-to-device (D2D) communications. After providing necessary background on CoMP and D2D, we analyze how SARC can particularly act as a major enabler for CoMP and D2D in context of 5G. This article thus serves as both a tutorial as well as an up to date survey on SARC, CoMP and D2D. Most importantly, the article provides an extensive outlook of challenges and opportunities that lie at the crossroads of these three mutually entangled emerging technologies.Comment: 28 pages, 11 figures, IEEE Communications Surveys & Tutorials 201

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Clustering algorithm for D2D communication in next generation cellular networks : thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Engineering, Massey University, Auckland, New Zealand

Next generation cellular networks will support many complex services for smartphones, vehicles, and other devices. To accommodate such services, cellular networks need to go beyond the capabilities of their previous generations. Device-to-Device communication (D2D) is a key technology that can help fulfil some of the requirements of future networks. The telecommunication industry expects a significant increase in the density of mobile devices which puts more pressure on centralized schemes and poses risk in terms of outages, poor spectral efficiencies, and low data rates. Recent studies have shown that a large part of the cellular traffic pertains to sharing popular contents. This highlights the need for decentralized and distributive approaches to managing multimedia traffic. Content-sharing via D2D clustered networks has emerged as a popular approach for alleviating the burden on the cellular network. Different studies have established that D2D communication in clusters can improve spectral and energy efficiency, achieve low latency while increasing the capacity of the network. To achieve effective content-sharing among users, appropriate clustering strategies are required. Therefore, the aim is to design and compare clustering approaches for D2D communication targeting content-sharing applications. Currently, most of researched and implemented clustering schemes are centralized or predominantly dependent on Evolved Node B (eNB). This thesis proposes a distributed architecture that supports clustering approaches to incorporate multimedia traffic. A content-sharing network is presented where some D2D User Equipment (DUE) function as content distributors for nearby devices. Two promising techniques are utilized, namely, Content-Centric Networking and Network Virtualization, to propose a distributed architecture, that supports efficient content delivery. We propose to use clustering at the user level for content-distribution. A weighted multi-factor clustering algorithm is proposed for grouping the DUEs sharing a common interest. Various performance parameters such as energy consumption, area spectral efficiency, and throughput have been considered for evaluating the proposed algorithm. The effect of number of clusters on the performance parameters is also discussed. The proposed algorithm has been further modified to allow for a trade-off between fairness and other performance parameters. A comprehensive simulation study is presented that demonstrates that the proposed clustering algorithm is more flexible and outperforms several well-known and state-of-the-art algorithms. The clustering process is subsequently evaluated from an individual user’s perspective for further performance improvement. We believe that some users, sharing common interests, are better off with the eNB rather than being in the clusters. We utilize machine learning algorithms namely, Deep Neural Network, Random Forest, and Support Vector Machine, to identify the users that are better served by the eNB and form clusters for the rest of the users. This proposed user segregation scheme can be used in conjunction with most clustering algorithms including the proposed multi-factor scheme. A comprehensive simulation study demonstrates that with such novel user segregation, the performance of individual users, as well as the whole network, can be significantly improved for throughput, energy consumption, and fairness

A Stochastic Geometric Analysis of Device-to-Device Communications Operating over Generalized Fading Channels

Device-to-device (D2D) communications are now considered as an integral part of future 5G networks which will enable direct communication between user equipment (UE) without unnecessary routing via the network infrastructure. This architecture will result in higher throughputs than conventional cellular networks, but with the increased potential for co-channel interference induced by randomly located cellular and D2D UEs. The physical channels which constitute D2D communications can be expected to be complex in nature, experiencing both line-of-sight (LOS) and non-LOS (NLOS) conditions across closely located D2D pairs. As well as this, given the diverse range of operating environments, they may also be subject to clustering of the scattered multipath contribution, i.e., propagation characteristics which are quite dissimilar to conventional Rayeligh fading environments. To address these challenges, we consider two recently proposed generalized fading models, namely $\kappa-\mu$ and $\eta-\mu$, to characterize the fading behavior in D2D communications. Together, these models encompass many of the most widely encountered and utilized fading models in the literature such as Rayleigh, Rice (Nakagami-$n$), Nakagami-$m$, Hoyt (Nakagami-$q$) and One-Sided Gaussian. Using stochastic geometry we evaluate the rate and bit error probability of D2D networks under generalized fading conditions. Based on the analytical results, we present new insights into the trade-offs between the reliability, rate, and mode selection under realistic operating conditions. Our results suggest that D2D mode achieves higher rates over cellular link at the expense of a higher bit error probability. Through numerical evaluations, we also investigate the performance gains of D2D networks and demonstrate their superiority over traditional cellular networks.Comment: Submitted to IEEE Transactions on Wireless Communication

Resource Allocation for Secure Gaussian Parallel Relay Channels with Finite-Length Coding and Discrete Constellations

We investigate the transmission of a secret message from Alice to Bob in the presence of an eavesdropper (Eve) and many of decode-and-forward relay nodes. Each link comprises a set of parallel channels, modeling for example an orthogonal frequency division multiplexing transmission. We consider the impact of discrete constellations and finite-length coding, defining an achievable secrecy rate under a constraint on the equivocation rate at Eve. Then we propose a power and channel allocation algorithm that maximizes the achievable secrecy rate by resorting to two coupled Gale-Shapley algorithms for stable matching problem. We consider the scenarios of both full and partial channel state information at Alice. In the latter case, we only guarantee an outage secrecy rate, i.e., the rate of a message that remains secret with a given probability. Numerical results are provided for Rayleigh fading channels in terms of average outage secrecy rate, showing that practical schemes achieve a performance quite close to that of ideal ones

Power Control for D2D Underlay in Multi-cell Massive MIMO Networks

This paper proposes a new power control and pilot allocation scheme for device-to-device (D2D) communication underlaying a multi-cell massive MIMO system. In this scheme, the cellular users in each cell get orthogonal pilots which are reused with reuse factor one across cells, while the D2D pairs share another set of orthogonal pilots. We derive a closed-form capacity lower bound for the cellular users with different receive processing schemes. In addition, we derive a capacity lower bound for the D2D receivers and a closed-form approximation of it. Then we provide a power control algorithm that maximizes the minimum spectral efficiency (SE) of the users in the network. Finally, we provide a numerical evaluation where we compare our proposed power control algorithm with the maximum transmit power case and the case of conventional multi-cell massive MIMO without D2D communication. Based on the provided results, we conclude that our proposed scheme increases the sum spectral efficiency of multi-cell massive MIMO networks.Comment: 6 Pages, 3 Figures, WSA 201

Device-to-Device Communication in 5G Cellular Networks

Owing to the unprecedented and continuous growth in the number of connected users and networked devices, the next-generation 5G cellular networks are envisaged to support enormous number of simultaneously connected users and devices with access to numerous services and applications by providing networks with highly improved data rate, higher capacity, lower end-to-end latency, improved spectral efficiency, at lower power consumption. D2D communication underlaying cellular networks has been proposed as one of the key components of the 5G technology as a means of providing efficient spectrum reuse for improved spectral efficiency and take advantage of proximity between devices for reduced latency, improved user throughput, and reduced power consumption. Although D2D communication underlaying cellular networks promises lots of potentials, unlike the conventional cellular network architecture, there are new design issues and technical challenges that must be addressed for proper implementation of the technology. These include new device discovery procedures, physical layer architecture and radio resource management schemes. This thesis explores the potentials of D2D communication as an underlay to 5G cellular networks and focuses on efficient interference management solutions through mode selection, resource allocation and power control schemes. In this work, a joint admission control, resource allocation, and power control scheme was implemented for D2D communication underlaying 5G cellular networks. The performance of the system was evaluated, and comparisons were made with similar schemes.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format