Abstract-To achieve the expected 1000x data rates under the exponential growth of traffic demand, a large number of base stations (BS) or access points (AP) will be deployed in the fifth generation (5G) wireless systems, to support high data rate services and to provide seamless coverage. Although such BSs are expected to be small-scale with lower power, the aggregated energy consumption of all BSs would be remarkable, resulting in increased environmental and economic concerns. In existing cellular networks, turning off the under-utilized BSs is an efficient approach to conserve energy while preserving the quality of service (QoS) of mobile users. However, in 5G systems with new physical layer techniques and the highly heterogeneous network architecture, new challenges arise in the design of BS ON-OFF switching strategies. In this article, we begin with a discussion on the inherent technical challenges of BS ON-OFF switching. We then provide a comprehensive review of recent advances on switching mechanisms in different application scenarios. Finally, we present open research problems and conclude the paper.

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In this paper, we investigate the problem of optimal base station (BS) ON-OFF switching and user association in a heterogeneous network (HetNet) with massive MIMO, with the objective to maximize the system energy efficiency (EE). The joint BS ON-OFF switching and user association problem is formulated as an integer programming problem. We first develop a centralized scheme, in which we relax the integer constraints and employ a series of Lagrangian dual methods that transform the original problem into a standard linear programming (LP) problem. Due to the special structure of the LP, we prove that the optimal solution to the relaxed LP is also feasible and optimal to the original problem. We then propose a distributed scheme by formulating a repeated bidding game for users and BS's, and prove that the game converges to a Nash Equilibrium (NE). Simulation studies demonstrate that the proposed schemes can achieve considerable gains in EE over several benchmark schemes in all the scenarios considered.

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Millimeter Wave (MmWave) communication is one of the key technology of fifth generation (5G) wireless systems to achieve the expected 1000x data rate. With large bandwidth at mmWave band, the link capacity between users and base stations (BS) can be much higher compared to sub-6GHz wireless systems. Meanwhile, due to the high cost of infrastructure upgrade, it would be difficult for operators to drastically enhance the capacity of backhaul links between mmWave BSs and the core network. As a result, the data rate provided by backhaul may not be sufficient to support all mmWave links, the backhaul connection becomes the new bottleneck that limits the system performance. On the other hand, as mmWave channels are subject to random blockage, the data rates of mmWave users significantly vary over time. With limited backhaul capacity and highly dynamic data rates of users, how to allocate backhaul resource to each user remains a challenge for mmWave systems. In this article, we present a deep reinforcement learning (DRL) approach to address this challenge. By learning the blockage pattern, the system dynamics can be captured and predicted, resulting in efficient utilization of backhaul resource. We begin with a discussion on DRL and its application in wireless systems. We then investigate the problem backhaul resource allocation and present the DRL based solution. Finally, we discuss open problems for future research and conclude this article.

To provide ubiquitous Internet access under the explosive increase of applications and data traffic, the current network architecture has become highly heterogeneous and complex, making network management a challenging task. To this end, software-defined networking (SDN) has been proposed as a promising solution. In the SDN architecture, the control plane and the data plane are decoupled, and the network infrastructures are abstracted and managed by a centralized controller. With SDN, efficient and flexible network control can be achieved, which potentially enhances network performance. To harvest the benefits of SDN in wireless networks, the software-defined wireless network (SDWN) architecture has been recently considered. In this paper, we first analyze the applications of SDN to different types of wireless networks. We then discuss several important technical aspects of performance enhancement in SDN-based wireless networks. Finally, we present possible future research directions of SDWN.

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