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

In this work, we focus on the transmission of measurements to an estimator over a wireless communication channel with limited capacity. The process it divided into two phases, measurement and transmission. In the first phase, multiple noisy measurements of the sensor value, which is assumed to stay constant over those measurements, are taken. More measurements can improve the accuracy, but also consume more time and energy. These measurements are aggregated into a sum value, which is quantized and transmitted over the communication channel with limited capacity. The measurement and transmission phases share the same time and energy budget, which limits the number of measurements and the number of bits that can be transmitted.At the receiver, the aggregated value is fed into an estimator optimized for a certain Bayes risk distortion function. Since the resources used for measurement and transmission are limited, a trade-off between measurement accuracy and quantization precision for minimum Bayes risk is found. Moreover, the influence of different resource limits for time and energy, as well as different ratios of the resources used by the measuring process and the transmission process are investigated. Both parameters show great influence on the optimum time and energy resource allocation.

Trade-Off Between Measurement Accuracy and Quantization Precision for Minimum Bayes Risk in Wireless Networked Control Systems

We investigate the resource allocation problem for the downlink of a multiple-input multiple-output orthogonal frequency division multiple access (MIMO-OFDMA) system. The sum rate maximization itself cannot cope with fairness among users. Hence, we address this problem in the context of the utility-based resource allocation presented in earlier papers. This resource allocation method allows to enhance the efficiency and guarantee fairness among users by exploiting multiuser diversity, frequency diversity, as well as time diversity. In this paper, we treat the overall utility as the quality of service indicator and design utility functions with respect to the average transmission rate in order to simultaneously provide two services, real-time and best-effort. Since the optimal solutions are extremely computationally complex to obtain, we propose a suboptimal joint subchannel and power control algorithm that converges very fast and simplifies the MIMO resource allocation problem into a single-input single-output resource allocation problem. Simulation results indicate that using the proposed method achieves near-optimum solutions, and the available resources are distributed more fairly among users.

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A cross-layer resource allocation scheme for spatial multiplexing-based MIMO-OFDMA systems

One of the key challenges in wireless communications is handling the ever growing traffic demand. A large fraction of this traffic is induced by popular content. One way to face this challenge is mobile content caching which improves the system performance by caching content closer to the user. The benefit of content caching depends on the applied content delivery strategy. In this paper, we investigate a scenario where multiple destinations are concurrently requesting a content, which is already cached at mobile devices and then delivered over a wireless multihop network. We propose a content delivery framework which jointly exploits content already cached at mobile devices as well as switching between mechanisms at the physical layer and the network layer in order to optimally deliver the content to all destinations under changing network conditions. In our framework, we use a unified graph model to jointly model the network, the cached content and different mechanisms at the lower three layers. From the unified graph model, an optimization problem is formulated, which is used to find the optimal content delivery strategy. In our numerical evaluation, we show the combined gain of caching and the capability of switching between mechanisms by comparing with conventional schemes which either cannot switch between mechanisms or do not exploit caching.

Exploiting caching and cross-layer transitions for content delivery in wireless multihop networks

Traditionally, relays are used for range extension in radio communication networks, i.e., for improving the performance in noise limited scenarios. In contrast to this, we focus on interference limited scenarios, where relays can be used to manipulate the effective channels between the transmitters and the receivers for interference reduction following the ideas of interference alignment. The investigated scenario consists of several pairs of nodes which want to communicate with each other. Especially bidirectional communication is considered. Furthermore, there are several linear, non regenerative relays aiding the communication. Both one-way relaying and two-way relaying protocols are investigated. Going beyond determining the degrees of freedom, questions concerning the required number of relays and relay antennas, the signal processing techniques for the relays, and the precoding at the nodes for achieving interference alignment are answered. Also, numerical algorithms helping the improvement of the performances for not purely interference limited scenarios, but with significant noise are developed.

Interference Alignment Aided by Non-Regenerative Relays

Video streaming in wireless multihop networks is a challenge due to different capabilities of end-user devices and changing network conditions. This challenge is addressed at the application layer with adaptive video streaming schemes like dynamic adaptive streaming over HTTP (DASH), which is widely applied by content providers. DASH copes with diverse end-user device capabilities by storing several representations of the same video such that DASH can offer a video in multiple qualities to users. Nevertheless, adjustments in DASH are solely taking place at the application layer. Especially in wireless multihop networks, adaptions on the lower layers are of particular importance. Therefore, we propose a novel application-aware cross-layer framework which adapts network support structures at the network layer, performs resource allocation at the medium access layer, switches between communication types at the physical layer and takes into account the properties and requirements of DASH at the application layer. Furthermore, we present a unified graph model, which takes into account the application layer, the network layer, the medium access layer and the physical layer jointly. We formulate a binary linear problem which chooses the optimal video representation for each user and finds the best combination of mechanisms on the lower three layers to optimally distribute the video content through the wireless multihop network. We show that our application- aware cross-layer framework which utilizes transitions leads to gains between 15-83 % compared to conventional approaches that do not switch between different mechanisms.

Hussein Al-Shatri

Papers

Trade-Off Between Measurement Accuracy and Quantization Precision for Minimum Bayes Risk in Wireless Networked Control Systems

A cross-layer resource allocation scheme for spatial multiplexing-based MIMO-OFDMA systems

Exploiting caching and cross-layer transitions for content delivery in wireless multihop networks

Interference Alignment Aided by Non-Regenerative Relays

A Network-Centric View on DASH in Wireless Multihop Networks