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

A scenario consisting of single antenna source- destination node pairs communicating using multiple single antenna relays is considered. A transmission scheme utilizing two subsequent time-slots assuming a time invariant channel throughout the transmission period is applied to this scenario. In the first time-slot, the source nodes transmit to both the relays and the destination nodes. Both the source nodes and the relays retransmit to the destination nodes in the second time-slot. As the relays can not decode the received signals, amplify and forward relaying is used. By exploiting the direct links between the source and the destination nodes, each destination node observes a two dimensional signal space. In the present paper, the sum mean square error can be written as a convex quadratic function of the relays' gain and part of the temporal filters at the source and the destination nodes. Moreover, closed form solutions for the minimum sum mean square error with interference zero forcing constraints and a total energy constraint are obtained. The results show that the proposed schemes outperform the interference alignment scheme at low and moderate SNRs.

Closed-Form Solutions for Minimizing Sum MSE in Multiuser Relay Networks

Until now, user preferences remained widely unconsidered in the design process of underlay wireless networks. Yet, with new technologies, such as device-to-device (D2D) communications being contingent upon user acceptance and their participation, user preferences are the key ingredient for designing successful products and services. Following this notion, we provide a general framework which elicits users' preferences for underlay networks (UUP) and active roles in multihop networks. Furthermore, we define an interface which translates the technical jargon related to the topic into non-technical terminology and introduce a virtual scenario which is also understandable for users with no technical background. Subsequently, based on a choice-based conjoint study, we derive the corresponding UUPs, translate them back into technical relationships, and assess the system's performance and the user participation by incorporating the elicited UUPs into a suitable D2D scenario.

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Eliciting and Considering Underlay User Preferences for Data-Forwarding in Multihop Wireless Networks

A scenario consisting of several single antenna source-destination node pairs communicating through multiple single antenna relays is considered. A two time-slot transmission scheme is considered. In the first time-slot, the source nodes transmit to both the relays and the destination nodes. Both the source nodes and the relays retransmit to the destination nodes in the second time-slot. As the relays cannot decode the received signals, an amplify and forward relaying strategy is assumed. In the present paper, the sum rate maximization problem is tackled. Due to the two transmissions of the source nodes and the two receptions of the destination nodes, there are temporal transmit and receive filters which can be optimized together with the relays' coefficients aiming at maximizing the sum rate. By partially adapting the filters and by introducing two sets of scaling factors, the sum rate maximization problem is reformulated as a tri-convex optimization problem. An iterative algorithm is proposed which maximizes the sum rate and guarantees a local optimum achievement. The results show that the proposed algorithm outperforms the previously proposed interference alignment scheme in all SNRs.

Multi-convex optimization for sum rate maximization in multiuser relay networks

A scenario consisting of several mobile users, an access point (AP) and a cloud server in a multi-stage hierarchy is considered. Each user has a computation task which can be computed locally or offloaded to the AP or to the cloud server. Considering the shared access channel between users and the AP, the shared computation resources at the AP and the shared backhaul link connection from the AP to the cloud, an energy minimization computation offloading problem with a time constraint is tackled. The time constraint guarantees that the offloading time will not exceed the local computation time. In this paper, we propose a distributed game theoretic algorithm which decomposes the offloading problem into the subproblems of resource allocation and offloading decisions. The algorithm works iteratively between the two subproblems as follows: The AP receives offloading decisions from the users and accordingly optimizes the fractions of the bandwidth on the access channel, the fractions of the backhaul link rate and the fractions of the computation resource at the AP for all offloading users. Based on the assigned resources, each user autonomously decides between local computation or offloading to the AP or to the cloud server and reports its decision to the AP. Our proposed algorithm is shown to require only limited signaling between users and AP and converges in significantly few iterations. Furthermore, the results show that our algorithm performs close to the optimal policy.

A Distributed Algorithm for Multi-Stage Computation Offloading

In this paper, multi-group multi-way relaying is considered. There are L groups with K nodes in each group. Each node wants to share d data streams with all the other nodes in its group. A single MIMO relay assists the communications. The relay does not have enough antennas to spatially separate the data streams. However, the relay assists in performing interference alignment at the receivers. In order to find the interference alignment solution, we generalize the concept of signal and channel alignment developed for the MIMO Y channel and the two-way relay channel to group signal alignment and group channel alignment. In comparison to conventional multi-group multi-way relaying schemes [1, 2], where at least R ≥ LKd − d antennas are required, in our proposed scheme, exploiting the multiple antennas at the nodes, only R ≥ LKd − Ld antennas are needed. The number of antennas required at the nodes to achieve this is also derived. It is shown that the proposed interference alignment based scheme achieves more degrees of freedom than the reference schemes without interference alignment.

Hussein Al-Shatri

Papers

Closed-Form Solutions for Minimizing Sum MSE in Multiuser Relay Networks

Eliciting and Considering Underlay User Preferences for Data-Forwarding in Multihop Wireless Networks

Multi-convex optimization for sum rate maximization in multiuser relay networks

A Distributed Algorithm for Multi-Stage Computation Offloading

Multi-group multi-way relaying with reduced number of relay antennas