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

Spectral and Energy Efficiency in Cognitive Radio Systems with Unslotted Primary Users and Sensing Uncertainty

This paper studies energy efficiency (EE) and average throughput maximization for cognitive radio systems in the presence of unslotted primary users. It is assumed that primary user activity follows an ON-OFF alternating renewal process. Secondary users first sense the channel possibly with errors in the form of miss detections and false alarms, and then start the data transmission only if no primary user activity is detected. The secondary user transmission is subject to constraints on collision duration ratio, which is defined as the ratio of average collision duration to transmission duration. In this setting, the optimal power control policy which maximizes the EE of the secondary users or maximizes the average throughput while satisfying a minimum required EE under average/peak transmit power and average interference power constraints are derived. Subsequently, low-complexity algorithms for jointly determining the optimal power level and frame duration are proposed. The impact of probabilities of detection and false alarm, transmit and interference power constraints on the EE, average throughput of the secondary users, optimal transmission power, and the collisions with primary user transmissions are evaluated. In addition, some important properties of the collision duration ratio are investigated. The tradeoff between the EE and average throughput under imperfect sensing decisions and different primary user traffic are further analyzed.Comment: This paper is accepted for publication in IEEE Transactions on Communication

Sensing-Throughput Tradeoff for Interweave Cognitive Radio System: A Deployment-Centric Viewpoint

Secondary access to the licensed spectrum is viable only if interference is avoided at the primary system. In this regard, different paradigms have been conceptualized in the existing literature. Of these, Interweave Systems (ISs) that employ spectrum sensing have been widely investigated. Baseline models investigated in the literature characterize the performance of IS in terms of a sensing-throughput tradeoff, however, this characterization assumes the knowledge of the involved channels at the secondary transmitter, which is unavailable in practice. Motivated by this fact, we establish a novel approach that incorporates channel estimation in the system model, and consequently investigate the impact of imperfect channel estimation on the performance of the IS. More particularly, the variation induced in the detection probability affects the detector's performance at the secondary transmitter, which may result in severe interference at the primary users. In this view, we propose to employ average and outage constraints on the detection probability, in order to capture the performance of the IS. Our analysis reveals that with an appropriate choice of the estimation time determined by the proposed model, the degradation in performance of the IS can be effectively controlled, and subsequently the achievable secondary throughput can be significantly enhanced.Comment: 13 pages, 10 figures, Accepted to be published in IEEE Transactions on Wireless Communication

Algorithms for 5G physical layer

There is a great activity in the research community towards the investigations of the various aspects of 5G at different protocol layers and parts of the network. Among all, physical layer design plays a very important role to satisfy high demands in terms of data rates, latency, reliability and number of connected devices for 5G deployment. This thesis addresses he latest developments in the physical layer algorithms regarding the channel coding, signal detection, frame synchronization and multiple access technique in the light of 5G use cases. These developments are governed by the requirements of the different use case scenarios that are envisioned to be the driving force in 5G. All chapters from chapter 2 to 5 are developed around the need of physical layer algorithms dedicated to 5G use cases. In brief, this thesis focuses on design, analysis, simulation and he advancement of physical layer aspects such as 1. Reliability based decoding of short length Linear Block Codes (LBCs) with very good properties in terms of minimum hamming istance for very small latency requiring applications. In this context, we enlarge the grid of possible candidates by considering, in particular, short length LBCs (especially extended CH codes) with soft-decision decoding; 2. Efficient synchronization of preamble/postamble in a short bursty frame using modified Massey correlator; 3. Detection of Primary User activity using semiblind spectrum sensing algorithms and analysis of such algorithms under practical imperfections; 4. Design of optimal spreading matrix for a Low Density Spreading (LDS) technique in the context of non-orthogonal multiple access. In such spreading matrix, small number of elements in a spreading sequences are non zero allowing each user to spread its data over small number of chips (tones), thus simplifying the decoding procedure using Message Passing Algorithm (MPA)

Multiband Spectrum Access: Great Promises for Future Cognitive Radio Networks

Cognitive radio has been widely considered as one of the prominent solutions to tackle the spectrum scarcity. While the majority of existing research has focused on single-band cognitive radio, multiband cognitive radio represents great promises towards implementing efficient cognitive networks compared to single-based networks. Multiband cognitive radio networks (MB-CRNs) are expected to significantly enhance the network's throughput and provide better channel maintenance by reducing handoff frequency. Nevertheless, the wideband front-end and the multiband spectrum access impose a number of challenges yet to overcome. This paper provides an in-depth analysis on the recent advancements in multiband spectrum sensing techniques, their limitations, and possible future directions to improve them. We study cooperative communications for MB-CRNs to tackle a fundamental limit on diversity and sampling. We also investigate several limits and tradeoffs of various design parameters for MB-CRNs. In addition, we explore the key MB-CRNs performance metrics that differ from the conventional metrics used for single-band based networks.Comment: 22 pages, 13 figures; published in the Proceedings of the IEEE Journal, Special Issue on Future Radio Spectrum Access, March 201

Spectrum sensing algorithms and software-defined radio implementation for cognitive radio system

The scarcity of spectral resources in wireless communications, due to a fixed frequency allocation policy, is a strong limitation to the increasing demand for higher data rates. However, measurements showed that a large part of frequency channels are underutilized or almost unoccupied. The cognitive radio paradigm arises as a tempting solution to the spectral congestion problem. A cognitive radio must be able to identify transmission opportunities in unused channels and to avoid generating harmful interference with the licensed primary users. Its key enabling technology is the spectrum sensing unit, whose ultimate goal consists in providing an indication whether a primary transmission is taking place in the observed channel. Such indication is determined as the result of a binary hypothesis testing experiment wherein null hypothesis (alternate hypothesis) corresponds to the absence (presence) of the primary signal. The first parts of this thesis describes the spectrum sensing problem and presents some of the best performing detection techniques. Energy Detection and multi-antenna Eigenvalue-Based Detection algorithms are considered. Important aspects are taken into account, like the impact of noise estimation or the effect of primary user traffic. The performance of each detector is assessed in terms of false alarm probability and detection probability. In most experimental research, cognitive radio techniques are deployed in software-defined radio systems, radio transceivers that allow operating parameters (like modulation type, bandwidth, output power, etc.) to be set or altered by software.In the second part of the thesis, we introduce the software-defined radio concept. Then, we focus on the implementation of Energy Detection and Eigenvalue-Based Detection algorithms: first, the used software platform, GNU Radio, is described, secondly, the implementation of a parallel energy detector and a multi-antenna eigenbased detector is illustrated and details on the used methodologies are given. Finally, we present the deployed experimental cognitive testbeds and the used radio peripherals. The obtained algorithmic results along with the software-defined radio implementation may offer a set of tools able to create a realistic cognitive radio system with real-time spectrum sensing capabilities