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

Cooperative Energy Trading in CoMP Systems Powered by Smart Grids

This paper studies the energy management in the coordinated multi-point (CoMP) systems powered by smart grids, where each base station (BS) with local renewable energy generation is allowed to implement the two-way energy trading with the grid. Due to the uneven renewable energy supply and communication energy demand over distributed BSs as well as the difference in the prices for their buying/selling energy from/to the gird, it is beneficial for the cooperative BSs to jointly manage their energy trading with the grid and energy consumption in CoMP based communication for reducing the total energy cost. Specifically, we consider the downlink transmission in one CoMP cluster by jointly optimizing the BSs' purchased/sold energy units from/to the grid and their cooperative transmit precoding, so as to minimize the total energy cost subject to the given quality of service (QoS) constraints for the users. First, we obtain the optimal solution to this problem by developing an algorithm based on techniques from convex optimization and the uplink-downlink duality. Next, we propose a sub-optimal solution of lower complexity than the optimal solution, where zero-forcing (ZF) based precoding is implemented at the BSs. Finally, through extensive simulations, we show the performance gain achieved by our proposed joint energy trading and communication cooperation schemes in terms of energy cost reduction, as compared to conventional schemes that separately design communication cooperation and energy trading

A New Look at Physical Layer Security, Caching, and Wireless Energy Harvesting for Heterogeneous Ultra-dense Networks

Heterogeneous ultra-dense networks enable ultra-high data rates and ultra-low latency through the use of dense sub-6 GHz and millimeter wave (mmWave) small cells with different antenna configurations. Existing work has widely studied spectral and energy efficiency in such networks and shown that high spectral and energy efficiency can be achieved. This article investigates the benefits of heterogeneous ultra-dense network architecture from the perspectives of three promising technologies, i.e., physical layer security, caching, and wireless energy harvesting, and provides enthusiastic outlook towards application of these technologies in heterogeneous ultra-dense networks. Based on the rationale of each technology, opportunities and challenges are identified to advance the research in this emerging network.Comment: Accepted to appear in IEEE Communications Magazin

Cost-Aware Green Cellular Networks with Energy and Communication Cooperation

Energy cost of cellular networks is ever-increasing to match the surge of wireless data traffic, and the saving of this cost is important to reduce the operational expenditure (OPEX) of wireless operators in future. The recent advancements of renewable energy integration and two-way energy flow in smart grid provide potential new solutions to save the cost. However, they also impose challenges, especially on how to use the stochastically and spatially distributed renewable energy harvested at cellular base stations (BSs) to reliably supply time- and space-varying wireless traffic over cellular networks. To overcome these challenges, in this article we present three approaches, namely, {\emph{energy cooperation, communication cooperation, and joint energy and communication cooperation}}, in which different BSs bidirectionally trade or share energy via the aggregator in smart grid, and/or share wireless resources and shift loads with each other to reduce the total energy cost.Comment: Submitted for possible publicatio

Minimum Throughput Maximization for Multi-UAV Enabled WPCN: A Deep Reinforcement Learning Method

This paper investigates joint unmanned aerial vehicle (UAV) trajectory planning and time resource allocation for minimum throughput maximization in a multiple UAV-enabled wireless powered communication network (WPCN). In particular, the UAVs perform as base stations (BS) to broadcast energy signals in the downlink to charge IoT devices, while the IoT devices send their independent information in the uplink by utilizing the collected energy. The formulated throughput optimization problem which involves joint optimization of 3D path design and channel resource assignment with the constraint of flight speed of UAVs and uplink transmit power of IoT devices, is not convex and thus is extremely difficult to solve directly. We take advantage of the multi-agent deep Q learning (DQL) strategy and propose a novel algorithm to tackle this problem. Simulation results indicate that the proposed DQL-based algorithm significantly improve performance gain in terms of minimum throughput maximization compared with the conventional WPCN scheme