Elements of Information Theory (2nd ed.). Thomas M. Cover and Joy A. Thomas

Functions of One Complex Variable

The use of multiple antennas at base stations is a key component in the design of cellular communication systems that can meet high-capacity demands in the downlink. Under ideal conditions, the gai ...

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Optimal Resource Allocation in Coordinated Multi-Cell Systems

Functions of one complex variable II

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.

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

We study multi-hop broadcast in wireless networks with one source node and multiple receiving nodes. The message flow from the source to the receivers can be modeled as a tree-graph, called broadcast-tree. The problem of finding the minimum-power broadcast-tree (MPBT) is NP-complete. Unlike most of the existing centralized approaches, we propose a decentralized algorithm, based on a non-cooperative cost-sharing game. In this game, every receiving node, as a player, chooses another node of the network as its respective transmitting node for receiving the message. Consequently, a cost is assigned to the receiving node based on the power imposed on its chosen transmitting node. In our model, the total required power at a transmitting node consists of (i) the transmit power and (ii) the circuitry power needed for communication hardware modules. We develop our algorithm using the marginal contribution (MC) cost-sharing scheme and show that the optimum broadcast-tree is always a Nash equilibrium (NE) of the game. Simulation results demonstrate that our proposed algorithm outperforms conventional algorithms for the MPBT problem. Besides, we show that the circuitry power, which is usually ignored by existing algorithms, significantly impacts the energy-efficiency of the network.

Cost Sharing Games for Energy-Efficient Multi-Hop Broadcast in Wireless Networks

A video streaming scenario in a wireless Ad Hoc network is considered. In this scenario, nodes are interested in streaming a video available at a single source node. The video will be disseminated throughout the network in a multi-hop fashion such that some intermediate nodes spend part of their energy for relaying the video. The end-users have different preferences in terms of the amount of energy they prefer to spend for forwarding video. We categorize them into two sets: the high-willingness-to-forward nodes and the low-willingness-toforward nodes. As incentive, the nodes which contribute more in the network receive high video quality while the rest of the nodes receive a basic video quality. The Scalable video coding (SVC) technique is exploited for streaming different video qualities to the nodes based on their energy contributions. We propose using two broadcast trees to connect the nodes based on their contribution level. Constructing the broadcast tree is based on a decentralized game-based algorithm. Simulation results show that by having more nodes with high contribution in the network, the users quality of experience as well as the network efficiency in terms of bits per joule increases.

Incorporating User Willingness for Message Forwarding in Multi-Hop Content Distribution Scenarios

A scenario of multiple agents working together to accomplish a common task is considered. Consensus control facilitates the coordination among the agents over time till they accomplish the task. In this paper, we consider formation control using consensus in which agents coordinate to form a circle. To make the coordination possible, agents need to periodically exchange their positions using orthogonal transmissions through a band-limited wireless channel which encounters different transmission delays on different links. To guarantee over all agents stability and convergence, we optimize the bandwidth allocation for equal rate transmission, which maintains the synchronization among agents. Moreover, we minimize the convergence time by optimizing the weights of the consensus algorithm. An ultra-reliable low latency communication between agents is guaranteed by transmitting short packets. The simulation results show that jointly optimizing the confidence weights and bandwidth allocation greatly reduces the convergence time as compared to conventional schemes.

Hussein Al-Shatri

Papers

Cost Sharing Games for Energy-Efficient Multi-Hop Broadcast in Wireless Networks

Incorporating User Willingness for Message Forwarding in Multi-Hop Content Distribution Scenarios

Ultra-Reliable Low Latency Communication for Consensus Control in Multi-Agent Systems