Sparse Signal Processing Concepts for Efficient 5G System Design

As it becomes increasingly apparent that 4G will not be able to meet the emerging demands of future mobile communication systems, the question what could make up a 5G system, what are the crucial challenges and what are the key drivers is part of intensive, ongoing discussions. Partly due to the advent of compressive sensing, methods that can optimally exploit sparsity in signals have received tremendous attention in recent years. In this paper we will describe a variety of scenarios in which signal sparsity arises naturally in 5G wireless systems. Signal sparsity and the associated rich collection of tools and algorithms will thus be a viable source for innovation in 5G wireless system design. We will discribe applications of this sparse signal processing paradigm in MIMO random access, cloud radio access networks, compressive channel-source network coding, and embedded security. We will also emphasize important open problem that may arise in 5G system design, for which sparsity will potentially play a key role in their solution.Comment: 18 pages, 5 figures, accepted for publication in IEEE Acces

Energy harvesting-aware design of wireless networks

Recent advances in low-power electronics and energy-harvesting (EH) technologies enable the design of self-sustained devices that collect part, or all, of the needed energy from the environment. Several systems can take advantage of EH, ranging from portable devices to wireless sensor networks (WSNs). While conventional design for battery-powered systems is mainly concerned with the battery lifetime, a key advantage of EH is that it enables potential perpetual operation of the devices, without requiring maintenance for battery substitutions. However, the inherent unpredictability regarding the amount of energy that can be collected from the environment might cause temporary energy shortages, which might prevent the devices to operate regularly. This uncertainty calls for the development of energy management techniques that are tailored to the EH dynamics. While most previous work on EH-capable systems has focused on energy management for single devices, the main contributions of this dissertation is the analysis and design of medium access control (MAC) protocols for WSNs operated by EH-capable devices. In particular, the dissertation first considers random access MAC protocols for single-hop EH networks, in which a fusion center collects data from a set of nodes distributed in its surrounding. MAC protocols commonly used in WSNs, such as time division multiple access (TDMA), framed-ALOHA (FA) and dynamic-FA (DFA) are investigated in the presence of EH-capable devices. A new ALOHA-based MAC protocol tailored to EH-networks, referred to as energy group-DFA (EG-DFA), is then proposed. In EG-DFA nodes with similar energy availability are grouped together and access the channel independently from other groups. It is shown that EG-DFA significantly outperforms the DFA protocol. Centralized scheduling-based MAC protocols for single-hop EH-networks with communication resource constraints are considered next. Two main scenarios are addressed, namely: i) nodes exclusively powered via EH; ii) nodes powered by a hybrid energy storage system, which is composed by a non-rechargeable battery and a capacitor charged via EH. For the former case the goal is the maximization of the network throughput, while in the latter the aim is maximizing the lifetime of the non-rechargeable batteries. For both scenarios optimal scheduling policies are derived by assuming different levels of information available at the fusion center about the energy availability at the nodes. When optimal policies are not derived explicitly, suboptimal policies are proposed and compared with performance upper bounds. Energy management policies for single devices have been investigated as well by focusing on radio frequency identification (RFID) systems, when the latter are operated by enhanced RFID tags with energy harvesting capabilities

Practical Evaluation of Low-complexity Medium Access Control Protocols for Wireless Sensor Networks

This thesis studies the potential of a novel approach to ensure more efficient and intelligent assignment of capacity through medium access control (MAC) in practical wireless sensor networks (WSNs), whereby Reinforcement Learning (RL) is employed as an intelligent transmission strategy. RL is applied to framed slotted-ALOHA to provide perfect scheduling. The system converges to a steady state of a unique transmission slot assigned per node in single-hop and multi-hop communication if there is sufficient number of slots available in the network, thereby achieving the optimum performance. The stability of the system against possible changes in the environment and changing channel conditions is studied. A Markov model is provided to represent the learning behaviour, which is also used to predict how the system loses its operation after convergence. Novel schemes are proposed to protect the lifetime of the system when the environment and channel conditions are insufficient to maintain the operation of the system. Taking real sensor platform architectures into consideration, the practicality of MAC protocols for WSNs must be considered based on hardware limitations/constraints. Therefore, the performance of the schemes developed is demonstrated through extensive simulations and evaluations in various test-beds. Practical evaluations show that RL-based schemes provide a high level of flexibility for hardware implementation

大規模多元接続ネットワークのためのグラフ構造に基づくランダムアクセス方式

電気通信大学201

EC-CENTRIC: An Energy- and Context-Centric Perspective on IoT Systems and Protocol Design

The radio transceiver of an IoT device is often where most of the energy is consumed. For this reason, most research so far has focused on low power circuit and energy efficient physical layer designs, with the goal of reducing the average energy per information bit required for communication. While these efforts are valuable per se, their actual effectiveness can be partially neutralized by ill-designed network, processing and resource management solutions, which can become a primary factor of performance degradation, in terms of throughput, responsiveness and energy efficiency. The objective of this paper is to describe an energy-centric and context-aware optimization framework that accounts for the energy impact of the fundamental functionalities of an IoT system and that proceeds along three main technical thrusts: 1) balancing signal-dependent processing techniques (compression and feature extraction) and communication tasks; 2) jointly designing channel access and routing protocols to maximize the network lifetime; 3) providing self-adaptability to different operating conditions through the adoption of suitable learning architectures and of flexible/reconfigurable algorithms and protocols. After discussing this framework, we present some preliminary results that validate the effectiveness of our proposed line of action, and show how the use of adaptive signal processing and channel access techniques allows an IoT network to dynamically tune lifetime for signal distortion, according to the requirements dictated by the application

Actas da 10ª Conferência sobre Redes de Computadores

Universidade do MinhoCCTCCentro AlgoritmiCisco SystemsIEEE Portugal Sectio

Distributed opportunistic scheduling algorithms for wireless communications.

In this thesis, we propose a number of distributed schemes for wireless communications in the cross layer design context, considering an uplink random access network in which multiple users communicate with a common base station. In addition, we perform a comprehensive study on a splitting based multiuser selection algorithm which is simple, effective, and scales with the network size. First, we investigate a reservation-type protocol in a channel aware ALOHA system. Various Markovian models are used to describe the system and to capture the temporal correlation of the channel evolution. The average throughput of the system is obtained using the Markov Analysis technique and we show that the reservation protocol can achieve better performance than the original channel-aware ALOHA by reducing the collision probability. Second, for better resource utilization in the Opportunistic Multichannel ALOHA scheme, we propose a simple extension to the transmission policy that exploits the idle channels. Performance analysis shows that, theoretically, the maximum system throughput can be improved by up to 63% in the asymptotic case. Through numerical results, it can be seen that a significant gain is achieved even when the system consists of a small number of users. Third, we consider a splitting based multiuser selection algorithm in a probabilistic view. Asymptotic analysis leads to a functional equation, similar to that encountered in the analysis of the collision resolution algorithm. Subject to some conditions, the solution of the functional equation can be obtained, which provides the approximations for the expected number of slots and the expected number of transmissions required by the algorithm in a large system. These results shed light on open design problems in choosing parameters for the algorithm when considering the delay and the overhead jointly. A typical example is to optimize the parameters that minimize the weighted sum of these measures of interest

COOPERATIVE NETWORKING AND RELATED ISSUES: STABILITY, ENERGY HARVESTING, AND NEIGHBOR DISCOVERY

This dissertation deals with various newly emerging topics in the context of cooperative networking. The first part is about the cognitive radio. To guarantee the performance of high priority users, it is important to know the activity of the high priority communication system but the knowledge is usually imperfect due to randomness in the observed signal. In such a context, the stability property of cognitive radio systems in the presence of sensing errors is studied. General guidelines on controlling the operating point of the sensing device over its receiver operating characteristics are also given. We then consider the hybrid of different modes of operation for cognitive radio systems with time-varying connectivity. The random connectivity gives additional chances that can be utilized by the low priority communication system. The second part of this dissertation is about the random access. We are specifically interested in the scenario when the nodes are harvesting energy from the environment. For such a system, we accurately assess the effect of limited, but renewable, energy availability on the stability region. The effect of finite capacity batteries is also studied. We next consider the exploitation of diversity amongst users under random access framework. That is, each user adapts its transmission probability based on the local channel state information in a decentralized manner. The impact of imperfect channel state information on the stability region is investigated. Furthermore, it is compared to the class of stationary scheduling policies that make centralized decisions based on the channel state feedback. The backpressure policy for cross-layer control of wireless multi-hop networks is known to be throughput-optimal for i.i.d. arrivals. The third part of this dissertation is about the backpressure-based control for networks with time-correlated arrivals that may exhibit long-range dependency. It is shown that the original backpressure policy is still throughput-optimal but with increased average network delay. The case when the arrival rate vector is possibly outside the stability region is also studied by augmenting the backpressure policy with the flow control mechanism. Lastly, the problem of neighbor discovery in a wireless sensor network is dealt. We first introduce the realistic effect of physical layer considerations in the evaluation of the performance of logical discovery algorithms by incorporating physical layer parameters. Secondly, given the lack of knowledge of the number of neighbors along with the lack of knowledge of the individual signal parameters, we adopt the viewpoint of random set theory to the problem of detecting the transmitting neighbors. Random set theory is a generalization of standard probability theory by assigning sets, rather than values, to random outcomes and it has been applied to multi-user detection problem when the set of transmitters are unknown and dynamically changing