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

Mode selection in device-to-device communications

Device-to-Device (D2D) communication refers to a technology that enables devices to communicate directly with each other, without sending data to the base station and the core network. This technology has the potential to improve system performance, enhance the user experience, increase spectral efficiency, reduce the terminal transmitting power, reduce the burden of the cellular network, and expand cellular applications. In D2D communication UEs are enabled to select among different Transmission Modes (TM)s which are defined based on the frequency resource sharing. Dedicated mode where the D2D communication is direct and data is transmitted through the D2D link by the orthogonal frequency resources to the cellular users so there is not any interference. Reuse mode where data is transmitted through the D2D link by reusing the same frequency resources that are considered for a cellular user or another D2D link so reused mode causes interference at receivers however, the system spectrum efficiency and user access rate may be increased. Cellular mode where the D2D communication is relayed via eNB and it is treated as cellular users. In this work, we aim to reach the optimal mode selection policy, and we use the Markov Decision Process (MDP) method with the objective of maximizing the total expected reward per connection. We present and analyse optimal mode selection policy for several scenarios with different rewards and cost for cellular, dedicated and reused mode. In our study of mode selection issues in D2D enabled network we propose an algorithm for the case when the cellular UE moves in the network. We use QoS parameters, mobility parameters and Analytic Hierarchy Process (AHP) method to define new mobility based mode selection algorithm. To evaluate our proposed algorithm, we considered SNR and delay

Self-Adapting Handover Parameters Optimization for SDN-Enabled UDN

Increasing the deployment density of small base stations (SBS) is a key method designed to satisfy high data traffic in 5th generation mobile network (5G). However, a large number of SBSs in such ultra-dense network (UDN) may cause ping-pong handovers (HOs), accompanied by increased delay and HO failure. In addition, because of the separation of control and data signaling in 5G, the HO procedure must be performed in both layers. In this paper, we introduce an SDN-based intelligent dynamic HO parameter optimization strategy to minimize both HO failures and ping-pong HOs together. The goal of the proposed strategy is to reduce the HO failure rate and redundant HO (i.e. ping-pong HO) while enabling user equipment (UE) to make full use of the benefits of dense deployment of BSs. Simulation results present that the method proposed in this paper effectively suppresses the ping-pong effect and keeps it at a low level in all of the investigated scenes. In addition, compared with the other algorithms, the HO failure rate is significantly reduced and the throughput of UE is greatly increased, especially in the case of high BS density. Therefore, the benefits of intensive BS deployment are retained