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 or Transmission: Causal Cognitive Radio Strategies with Censorship

This paper introduces a novel opportunistic transmission strategy for cognitive radios (CRs). The primary user (PU) is assumed to transmit in a time-slotted manner according to a two-state Markov model, and the CR is either sensing, that is, obtaining a causal, noisy observation of a primary user (PU) state, or transmitting, but not both at the same time. In other words, the CR observations of the PU are censored whenever the CR is transmitting. The objective of the CR transmission strategy is to maximize the utilization ratio (UR), i.e., the relative number of the PU-idle slots that are used by the CR, subject to that the interference ratio (IR), i.e., the relative number of the PU-active slots that are used by the CR, is below a certain level. We introduce an a-posteriori LLR-based CR transmission strategy, called CLAPP, and evaluate this strategy in terms of the achievable UR for different PU model parameters and received signal-to-noise ratios (SNRs). The performance of CLAPP is compared with a simple censored energy detection scheme. Simulation results show that CLAPP has 52% gain in UR over the best censored energy detection scheme for a maximum IR level of 10% and an SNR of -2dB. \ua9 2002-2012 IEEE

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

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.Comment: 46 pages, 22 fig

Implementation of cognitive radio networks to evaluate spectrum management strategies in real-time

This paper illustrates a Universal Software Radio Peripheral (USRP)-based real-time testbed that is able to evaluate different spectrum management solutions that exploit the Cognitive Radio (CR) paradigm. The main objective of this testbed is to provide an accurate and realistic platform by which the performance of innovative spectrum management solutions for a wide set of scenarios and use cases in the context of Opportunistic Networks (ONs) and Cognitive Radio Networks (CRNs) can be entirely validated and assessed before their implementation in real systems. Real-time platforms are essential to carry out significant studies and to accurately assess the performance of innovative solutions before their implementation in the real world. This work provides a comprehensive description of the testbed, highlighting many interesting implementation details and illustrating its applicability for different studies that rely on the CR paradigm. Then, a particular application in a realistic Digital Home (DH) scenario is also illustrated, which allows demonstrating the effectiveness of the real-time testbed and assessing its practicality in terms of user-perceived end-to-end Quality of Experience (QoE) in a realistic environment.Peer ReviewedPostprint (author's final draft

Cognitive Radio Systems: Performance Analysis and Optimal Resource Allocation

Rapid growth in the use of wireless services coupled with inefficient utilization of scarce spectrum resources has led to the analysis and development of cognitive radio systems. Cognitive radio systems provide dynamic and more efficient utilization of the available spectrum by allowing unlicensed users (i.e., cognitive or secondary users) to access the frequency bands allocated to the licensed users (i.e., primary users) without causing harmful interference to the primary user transmissions. The central goal of this thesis is to conduct a performance analysis and obtain throughput- and energy-efficient optimal resource allocation strategies for cognitive radio systems. Cognitive radio systems, which employ spectrum sensing mechanisms to learn the channel occupancy by primary users, generally operate under sensing uncertainty arising due to false alarms and miss-detections. This thesis analyzes the performance of cognitive radio systems in a practical setting with imperfect spectrum sensing. In the first part of the thesis, optimal power adaptation schemes that maximize the achievable rates of cognitive users with arbitrary input distributions in underlay cognitive radio systems subject to transmit and interference power constraints are studied. Simpler approximations of optimal power control policies in the low-power regime are determined. Low-complexity optimal power control algorithms are proposed. Next, energy efficiency is considered as the performance metric and power allocation strategies that maximize the energy efficiency of cognitive users in the presence of time-slotted primary users are identified. The impact of different levels of channel knowledge regarding the transmission link between the secondary transmitter and secondary receiver, and the interference link between the secondary transmitter and primary receiver on the optimal power allocation is addressed. In practice, the primary user may change its status during the transmission phase of the secondary users. In such cases, the assumption of time-slotted primary user transmission no longer holds. With this motivation, the spectral and energy efficiency in cognitive radio systems with unslotted primary users are analyzed and the optimal frame duration and energy-efficient optimal power control schemes subject to a collision constraint are jointly determined. The second line of research in this thesis focuses on symbol error rate performance of cognitive radio transmissions in the presence of imperfect sensing decisions. General formulations for the optimal decision rule and error probabilities for arbitrary modulation schemes are provided. The optimal decision rule for rectangular quadrature amplitude modulation (QAM) is characterized, and closed-form expressions for the average symbol error probability attained with the optimal detector under both transmit power and interference constraints are derived. Furthermore, throughput of cognitive radio systems for both fixed-rate and variable-rate transmissions in the finite-blocklength regime is studied. The maximum constant arrival rates that the cognitive radio channel can support with finite blocklength codes while satisfying statistical quality of service (QoS) constraints imposed as limitations on the buffer violation probability are characterized. In the final part of the thesis, performance analysis in the presence of QoS requirements is extended to general wireless systems, and energy efficiency and throughput optimization with arbitrary input signaling are studied when statistical QoS constraints are imposed as limitations on the buffer violation probability. Effective capacity is chosen as the performance metric to characterize the maximum throughput subject to such buffer constraints by capturing the asymptotic decay-rate of buffer occupancy. Initially, constant-rate source is considered and subsequently random arrivals are taken into account

Qualité de service dans des environnements réseaux mobiles, contraints et hétérogènes

Les télécommunications sans fil ont connu ces dernières années un immense succès à tel point que le spectre des fréquences est désormais surchargé et nécessite la disponibilité de nouvelles ressources. Pour répondre à ce besoin, des techniques de réutilisation dynamique du spectre ont alors vu le jour sous la dénomination de radio cognitive. Elles consistent à partager de manière opportuniste et efficace certaines fréquences ayant été initialement allouées à d'autres systèmes. Cette thèse se place dans le contexte de réseaux sans fil tactiques hétérogènes comportant des segments de radios cognitives. La difficulté provient alors de la garantie de qualité de service de bout en bout : respect du débit négocié, du délai et de la gigue. Nous nous sommes tout d'abord intéressés au contrôle d'admission dans ce type de réseaux en proposant une méthode de calcul de bande passante résiduelle de bout en bout s'appuyant sur un algorithme de complexité polynomiale et pouvant être implanté de manière distribuée. Nous nous sommes ensuite concentrés sur le routage en proposant une nouvelle métrique tenant compte des particularités de ce type de réseaux. Enfin, nous nous focalisons sur la thématique du routage à contraintes multiples en étudiant et implantant en environnement réel des algorithmes d'approximation proposés dans la littérature. ABSTRACT : The unprecedented success of wireless telecommunication systems has resulted in the wireless spectrum becoming a scarce resource. Cognitive Radio systems have been proposed as the enabling technology allowing unlicensed equipments to opportunistically access the licensed spectrum when not in use by the licensed users. The focus of this thesis is on heterogeneous tactical networks deploying cognitive radios in parts or in their entirety. Such networks can be organized in multiple sub-networks, each characterized by a specific topology, medium access scheme and spectrum access policy. As a result, providing end-to-end Quality of Service guarantees in terms of bandwidth, delay and jitter, emerges as a key challenge. We first address the admission control in multi-hop cognitive radio networks and propose a polynomial time algorithm that can be implemented in a distributed fashion for estimating the end-to-end bandwidth. Then, we focus on routing and propose a new metric that takes into account the specifics of such networks. Finally, as quality of service requirements can be expressed using multiple metrics, we turn our attention to multi-constrained routing and implement on a real testbed low complexity approximation algorithms