Performance Analysis of Network-Assisted Two-Hop D2D Communications

Network-assisted single-hop device-to-device (D2D) communication can increase the spectral and energy efficiency of cellular networks by taking advantage of the proximity, reuse, and hop gains when radio resources are properly managed between the cellular and D2D layers. In this paper we argue that D2D technology can be used to further increase the spectral and energy efficiency if the key D2D radio resource management algorithms are suitably extended to support network assisted multi-hop D2D communications. Specifically, we propose a novel, distributed utility maximizing D2D power control (PC) scheme that is able to balance spectral and energy efficiency while taking into account mode selection and resource allocation constraints that are important in the integrated cellular-D2D environment. Our analysis and numerical results indicate that multi-hop D2D communications combined with the proposed PC scheme can be useful not only for harvesting the potential gains previously identified in the literature, but also for extending the coverage of cellular networks.Comment: 6 pages and 7 figure

QoS routing in ad-hoc networks using GA and multi-objective optimization

Much work has been done on routing in Ad-hoc networks, but the proposed routing solutions only deal with the best effort data traffic. Connections with Quality of Service (QoS) requirements, such as voice channels with delay and bandwidth constraints, are not supported. The QoS routing has been receiving increasingly intensive attention, but searching for the shortest path with many metrics is an NP-complete problem. For this reason, approximated solutions and heuristic algorithms should be developed for multi-path constraints QoS routing. Also, the routing methods should be adaptive, flexible, and intelligent. In this paper, we use Genetic Algorithms (GAs) and multi-objective optimization for QoS routing in Ad-hoc Networks. In order to reduce the search space of GA, we implemented a search space reduction algorithm, which reduces the search space for GAMAN (GA-based routing algorithm for Mobile Ad-hoc Networks) to find a new route. We evaluate the performance of GAMAN by computer simulations and show that GAMAN has better behaviour than GLBR (Genetic Load Balancing Routing).Peer ReviewedPostprint (published version

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