Optimal power allocation for OFDM-based cognitive radio with new primary transmission protection criteria
TL;DR: This paper considers a spectrum underlay network, where an OFDM-based cognitive radio (CR) system is allowed to share the subcarriers of an OFDMA-based primary system for simultaneous transmission, and shows that the CR system can achieve a significant rate gain under RLC as compared to IPC.
Abstract: This paper considers a spectrum underlay network, where an OFDM-based cognitive radio (CR) system is allowed to share the subcarriers of an OFDMA-based primary system for simultaneous transmission. Instead of using the conventional interference power constraint (IPC) to protect the primary users (PUs) in the primary system, a new criterion referred to as rate loss constraint (RLC), in the form of an upper bound on the maximum rate loss of each PU due to the CR transmission, is proposed for primary transmission protection. Assuming the channel state information (CSI) of the PU link, the CR link, and their mutual interference links is available to the CR, the optimal power allocation strategy to maximize the achievable rate of the CR system is derived under RLC together with CR?s transmit power constraint. It is shown that the CR system can achieve a significant rate gain under RLC as compared to IPC. Furthermore, the relationship between RLC and IPC is investigated, and it is shown that the rate gain is obtained by exploiting the additional CSI of the PU link. A more general case referred to as hybrid protection to PUs is then studied, by taking into account that some PU links? CSI is not available at CR.
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TL;DR: An overview of recent research achievements of including spectrum sensing, sharing techniques and the applications of CR systems is provided.
Abstract: Cognitive radio (CR) can successfully deal with the growing demand and scarcity of the wireless spectrum. To exploit limited spectrum efficiently, CR technology allows unlicensed users to access licensed spectrum bands. Since licensed users have priorities to use the bands, the unlicensed users need to continuously monitor the licensed users' activities to avoid interference and collisions. How to obtain reliable results of the licensed users' activities is the main task for spectrum sensing. Based on the sensing results, the unlicensed users should adapt their transmit powers and access strategies to protect the licensed communications. The requirement naturally presents challenges to the implementation of CR. In this article, we provide an overview of recent research achievements of including spectrum sensing, sharing techniques and the applications of CR systems.
259 citations
Cites background from "Optimal power allocation for OFDM-b..."
...Under RLC, the transmission efficiency of the SU system increases [87]....
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TL;DR: This paper provides an overview of cognitive radio (CR) networks, with focus on the recent advances in resource allocation techniques and the CR networks architectural design.
Abstract: This paper provides an overview of cognitive radio (CR) networks, with focus on the recent advances in resource allocation techniques and the CR networks architectural design. The contribution of this work is threefold. First, a systematic way to study the resource allocation problem is presented; various design approaches are introduced, such as signal-to-interference-and-noise ratio (SINR) or transmission power-based, and centralized or distributed methods. Second, CR optimization methods are presented, accompanied by a comprehensive study of the resource allocation problem formulations. Furthermore, quality of service criteria of the physical or/and the medium access control layers are investigated. Third, challenges in spectrum assignment are discussed, focusing on dynamic spectrum allocation, spectrum aggregation and frequency mobility. Such approaches constitute an emerging trend in efficient spectrum sharing and affect the performance of resource allocation techniques. The open issues for future research in this area are finally discussed, including adaptability-reconfigurability, dual accessibility, and energy efficiency.
194 citations
Cites methods from "Optimal power allocation for OFDM-b..."
...To quantify this performance degradation, several loss functions have been proposed in the literature [97], [100]– [102]....
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TL;DR: An overview on robust design for power control and beamforming in cognitive radio networks (CRNs) is given, modeling methods for parametric uncertainties are analyzed, various design methodologies are introduced, and robust algorithms that have appeared in the literatures are presented.
Abstract: Traditional spectrum allocation policies may result in temporarily unused radio spectrum. Cognitive radio (CR) has emerged as a promising technology to exploit the radio spectrum in a more efficient manner by allowing spectrum sharing between secondary users (SUs) and primary users (PUs). Power control and beamforming are two key techniques in CR design used to maximize the benefits of SUs, yet to maintain the quality of service of PUs. In practice, the available system parameters (e.g., channel state information and interference power) to enable power control and beamforming could be uncertain due to various factors such as estimation error and/or measurement error, thus the robustness of the designed algorithms should be considered in order to overcome the effects of parametric uncertainties. The objective of this paper is to give an overview on robust design for power control and beamforming in cognitive radio networks (CRNs). We will analyze modeling methods for parametric uncertainties, introduce various design methodologies, and present robust algorithms that have appeared in the literatures. Finally, some potential issues and future research directions in this field will be presented.
125 citations
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..., [7], [8]; 3) cognitive relay networks, see e....
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TL;DR: An opportunistic spectrum sharing protocol that exploits the situation when the primary system is incapable of supporting its target transmission rate and helps the secondary system to achieve its target rate via two-phase cooperative OFDM relaying, as well as the win-win solution for the primary and secondary systems.
Abstract: In this paper, we propose an opportunistic spectrum sharing protocol that exploits the situation when the primary system is incapable of supporting its target transmission rate. Specifically, the secondary system tries to help the primary system to achieve its target rate via two-phase cooperative OFDM relaying, where the secondary system acts as an amplify-and-forward relay for the primary system by allocating a fraction of its subcarriers to forward the primary signal. At the same time, the secondary system uses the remaining subcarriers to transmit its own signal, and thus gaining opportunistic spectrum access. As a part of the protocol, if the primary system finds that outage will occur even when the secondary system serves as a pure relay, the primary system will cease transmission and the secondary system will be granted access to the primary spectrum. We study the joint optimization of the set of subcarriers used for cooperation, subcarrier pairing, and subcarrier power allocation such that the transmission rate of the secondary system is maximized, while helping the primary system, as a higher priority, to achieve its target rate. Simulation results demonstrate the performance of the proposed spectrum sharing protocol as well as the win-win solution for the primary and secondary systems.
93 citations
Cites background from "Optimal power allocation for OFDM-b..."
...The optimal power allocation strategy that maximizes the achievable rate of the secondary system has been considered in [8] and [18] subject to the interference power constraint at the primary receiver, and in [9] subject to the rate loss constraint, in order to protect the primary transmission....
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...The secondary system may also be allowed to share the primary spectrum through simultaneous transmission provided that the resultant interference at the primary system is below a prescribed threshold in order to protect the primary transmission [8], [9]....
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...As mentioned earlier in Section I, this ‘capable primary’ scenario has already been exploited to provide secondary access in the literature such as [8] and [9] by allowing simultaneous secondary transmission as long as the primary user can still achieve its target rate....
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TL;DR: An optimal power allocation algorithm for the orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) systems with different statistical interference constraints imposed by different primary users (PUs) is developed and the performance has been investigated.
Abstract: In this letter, we develop an optimal power allocation algorithm for the orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) systems with different statistical interference constraints imposed by different primary users (PUs). Given the fact that the interference constraints are met in a statistical manner, the CR transmitter does not require the instantaneous channel quality feedback from the PU receivers. A suboptimal algorithm with reduced complexity has been proposed and the performance has been investigated. Presented numerical results show that with our proposed optimal power allocation algorithm CR user can achieve significantly higher transmission capacity for given statistical interference constraints and a given power budget compared to the classical power allocation algorithms namely, uniform and water-filling power allocation algorithms. The suboptimal algorithm outperforms both water-filling algorithm and uniform power loading algorithm. The proposed suboptimal algorithm give an option of using a low complexity power allocation algorithm where complexity is an issue with a certain amount of transmission rate degradation.
87 citations
Cites background from "Optimal power allocation for OFDM-b..."
...Several other resource allocation schemes for OFDM-based CR systems have been proposed in [7], [8], [9], [10]....
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References
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01 Jan 1991TL;DR: The author examines the role of entropy, inequality, and randomness in the design of codes and the construction of codes in the rapidly changing environment.
Abstract: Preface to the Second Edition. Preface to the First Edition. Acknowledgments for the Second Edition. Acknowledgments for the First Edition. 1. Introduction and Preview. 1.1 Preview of the Book. 2. Entropy, Relative Entropy, and Mutual Information. 2.1 Entropy. 2.2 Joint Entropy and Conditional Entropy. 2.3 Relative Entropy and Mutual Information. 2.4 Relationship Between Entropy and Mutual Information. 2.5 Chain Rules for Entropy, Relative Entropy, and Mutual Information. 2.6 Jensen's Inequality and Its Consequences. 2.7 Log Sum Inequality and Its Applications. 2.8 Data-Processing Inequality. 2.9 Sufficient Statistics. 2.10 Fano's Inequality. Summary. Problems. Historical Notes. 3. Asymptotic Equipartition Property. 3.1 Asymptotic Equipartition Property Theorem. 3.2 Consequences of the AEP: Data Compression. 3.3 High-Probability Sets and the Typical Set. Summary. Problems. Historical Notes. 4. Entropy Rates of a Stochastic Process. 4.1 Markov Chains. 4.2 Entropy Rate. 4.3 Example: Entropy Rate of a Random Walk on a Weighted Graph. 4.4 Second Law of Thermodynamics. 4.5 Functions of Markov Chains. Summary. Problems. Historical Notes. 5. Data Compression. 5.1 Examples of Codes. 5.2 Kraft Inequality. 5.3 Optimal Codes. 5.4 Bounds on the Optimal Code Length. 5.5 Kraft Inequality for Uniquely Decodable Codes. 5.6 Huffman Codes. 5.7 Some Comments on Huffman Codes. 5.8 Optimality of Huffman Codes. 5.9 Shannon-Fano-Elias Coding. 5.10 Competitive Optimality of the Shannon Code. 5.11 Generation of Discrete Distributions from Fair Coins. Summary. Problems. Historical Notes. 6. Gambling and Data Compression. 6.1 The Horse Race. 6.2 Gambling and Side Information. 6.3 Dependent Horse Races and Entropy Rate. 6.4 The Entropy of English. 6.5 Data Compression and Gambling. 6.6 Gambling Estimate of the Entropy of English. Summary. Problems. Historical Notes. 7. Channel Capacity. 7.1 Examples of Channel Capacity. 7.2 Symmetric Channels. 7.3 Properties of Channel Capacity. 7.4 Preview of the Channel Coding Theorem. 7.5 Definitions. 7.6 Jointly Typical Sequences. 7.7 Channel Coding Theorem. 7.8 Zero-Error Codes. 7.9 Fano's Inequality and the Converse to the Coding Theorem. 7.10 Equality in the Converse to the Channel Coding Theorem. 7.11 Hamming Codes. 7.12 Feedback Capacity. 7.13 Source-Channel Separation Theorem. Summary. Problems. Historical Notes. 8. Differential Entropy. 8.1 Definitions. 8.2 AEP for Continuous Random Variables. 8.3 Relation of Differential Entropy to Discrete Entropy. 8.4 Joint and Conditional Differential Entropy. 8.5 Relative Entropy and Mutual Information. 8.6 Properties of Differential Entropy, Relative Entropy, and Mutual Information. Summary. Problems. Historical Notes. 9. Gaussian Channel. 9.1 Gaussian Channel: Definitions. 9.2 Converse to the Coding Theorem for Gaussian Channels. 9.3 Bandlimited Channels. 9.4 Parallel Gaussian Channels. 9.5 Channels with Colored Gaussian Noise. 9.6 Gaussian Channels with Feedback. Summary. Problems. Historical Notes. 10. Rate Distortion Theory. 10.1 Quantization. 10.2 Definitions. 10.3 Calculation of the Rate Distortion Function. 10.4 Converse to the Rate Distortion Theorem. 10.5 Achievability of the Rate Distortion Function. 10.6 Strongly Typical Sequences and Rate Distortion. 10.7 Characterization of the Rate Distortion Function. 10.8 Computation of Channel Capacity and the Rate Distortion Function. Summary. Problems. Historical Notes. 11. Information Theory and Statistics. 11.1 Method of Types. 11.2 Law of Large Numbers. 11.3 Universal Source Coding. 11.4 Large Deviation Theory. 11.5 Examples of Sanov's Theorem. 11.6 Conditional Limit Theorem. 11.7 Hypothesis Testing. 11.8 Chernoff-Stein Lemma. 11.9 Chernoff Information. 11.10 Fisher Information and the Cram-er-Rao Inequality. Summary. Problems. Historical Notes. 12. Maximum Entropy. 12.1 Maximum Entropy Distributions. 12.2 Examples. 12.3 Anomalous Maximum Entropy Problem. 12.4 Spectrum Estimation. 12.5 Entropy Rates of a Gaussian Process. 12.6 Burg's Maximum Entropy Theorem. Summary. Problems. Historical Notes. 13. Universal Source Coding. 13.1 Universal Codes and Channel Capacity. 13.2 Universal Coding for Binary Sequences. 13.3 Arithmetic Coding. 13.4 Lempel-Ziv Coding. 13.5 Optimality of Lempel-Ziv Algorithms. Compression. Summary. Problems. Historical Notes. 14. Kolmogorov Complexity. 14.1 Models of Computation. 14.2 Kolmogorov Complexity: Definitions and Examples. 14.3 Kolmogorov Complexity and Entropy. 14.4 Kolmogorov Complexity of Integers. 14.5 Algorithmically Random and Incompressible Sequences. 14.6 Universal Probability. 14.7 Kolmogorov complexity. 14.9 Universal Gambling. 14.10 Occam's Razor. 14.11 Kolmogorov Complexity and Universal Probability. 14.12 Kolmogorov Sufficient Statistic. 14.13 Minimum Description Length Principle. Summary. Problems. Historical Notes. 15. Network Information Theory. 15.1 Gaussian Multiple-User Channels. 15.2 Jointly Typical Sequences. 15.3 Multiple-Access Channel. 15.4 Encoding of Correlated Sources. 15.5 Duality Between Slepian-Wolf Encoding and Multiple-Access Channels. 15.6 Broadcast Channel. 15.7 Relay Channel. 15.8 Source Coding with Side Information. 15.9 Rate Distortion with Side Information. 15.10 General Multiterminal Networks. Summary. Problems. Historical Notes. 16. Information Theory and Portfolio Theory. 16.1 The Stock Market: Some Definitions. 16.2 Kuhn-Tucker Characterization of the Log-Optimal Portfolio. 16.3 Asymptotic Optimality of the Log-Optimal Portfolio. 16.4 Side Information and the Growth Rate. 16.5 Investment in Stationary Markets. 16.6 Competitive Optimality of the Log-Optimal Portfolio. 16.7 Universal Portfolios. 16.8 Shannon-McMillan-Breiman Theorem (General AEP). Summary. Problems. Historical Notes. 17. Inequalities in Information Theory. 17.1 Basic Inequalities of Information Theory. 17.2 Differential Entropy. 17.3 Bounds on Entropy and Relative Entropy. 17.4 Inequalities for Types. 17.5 Combinatorial Bounds on Entropy. 17.6 Entropy Rates of Subsets. 17.7 Entropy and Fisher Information. 17.8 Entropy Power Inequality and Brunn-Minkowski Inequality. 17.9 Inequalities for Determinants. 17.10 Inequalities for Ratios of Determinants. Summary. Problems. Historical Notes. Bibliography. List of Symbols. Index.
45,034 citations
"Optimal power allocation for OFDM-b..." refers methods in this paper
...From Theorem 1, it is observed that the optimal power allocation given in (27) is similar to the conventional waterfilling solution given in [ 16 ]....
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Book•
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TL;DR: In this article, the focus is on recognizing convex optimization problems and then finding the most appropriate technique for solving them, and a comprehensive introduction to the subject is given. But the focus of this book is not on the optimization problem itself, but on the problem of finding the appropriate technique to solve it.
Abstract: Convex optimization problems arise frequently in many different fields. A comprehensive introduction to the subject, this book shows in detail how such problems can be solved numerically with great efficiency. The focus is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. The text contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance, and economics.
33,341 citations
TL;DR: Following the discussion of interference temperature as a new metric for the quantification and management of interference, the paper addresses three fundamental cognitive tasks: radio-scene analysis, channel-state estimation and predictive modeling, and the emergent behavior of cognitive radio.
Abstract: Cognitive radio is viewed as a novel approach for improving the utilization of a precious natural resource: the radio electromagnetic spectrum. The cognitive radio, built on a software-defined radio, is defined as an intelligent wireless communication system that is aware of its environment and uses the methodology of understanding-by-building to learn from the environment and adapt to statistical variations in the input stimuli, with two primary objectives in mind: /spl middot/ highly reliable communication whenever and wherever needed; /spl middot/ efficient utilization of the radio spectrum. Following the discussion of interference temperature as a new metric for the quantification and management of interference, the paper addresses three fundamental cognitive tasks. 1) Radio-scene analysis. 2) Channel-state estimation and predictive modeling. 3) Transmit-power control and dynamic spectrum management. This work also discusses the emergent behavior of cognitive radio.
12,172 citations
"Optimal power allocation for OFDM-b..." refers background in this paper
...Recently, as proposed by many researchers, SU is allowed to transmit with the PU over the same spectrum band simultaneously on condition that the resultant interference at the PU receiver is below a prescribed threshold, known as spectrum underlay in [2] or spectrum sharing in [3]....
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TL;DR: With RKRL, cognitive radio agents may actively manipulate the protocol stack to adapt known etiquettes to better satisfy the user's needs and transforms radio nodes from blind executors of predefined protocols to radio-domain-aware intelligent agents that search out ways to deliver the services the user wants even if that user does not know how to obtain them.
Abstract: Software radios are emerging as platforms for multiband multimode personal communications systems. Radio etiquette is the set of RF bands, air interfaces, protocols, and spatial and temporal patterns that moderate the use of the radio spectrum. Cognitive radio extends the software radio with radio-domain model-based reasoning about such etiquettes. Cognitive radio enhances the flexibility of personal services through a radio knowledge representation language. This language represents knowledge of radio etiquette, devices, software modules, propagation, networks, user needs, and application scenarios in a way that supports automated reasoning about the needs of the user. This empowers software radios to conduct expressive negotiations among peers about the use of radio spectrum across fluents of space, time, and user context. With RKRL, cognitive radio agents may actively manipulate the protocol stack to adapt known etiquettes to better satisfy the user's needs. This transforms radio nodes from blind executors of predefined protocols to radio-domain-aware intelligent agents that search out ways to deliver the services the user wants even if that user does not know how to obtain them. Software radio provides an ideal platform for the realization of cognitive radio.
9,238 citations
"Optimal power allocation for OFDM-b..." refers background in this paper
...This motivates the advent of cognitive radio (CR) [2], which makes use of spectrum flexibly, efficiently, and reliably....
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...Recently, as proposed by many researchers, SU is allowed to transmit with the PU over the same spectrum band simultaneously on condition that the resultant interference at the PU receiver is below a prescribed threshold, known as spectrum underlay in [2] or spectrum sharing in [3]....
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Book•
01 Jan 2005TL;DR: In this paper, the authors propose a multiuser communication architecture for point-to-point wireless networks with additive Gaussian noise detection and estimation in the context of MIMO networks.
Abstract: 1. Introduction 2. The wireless channel 3. Point-to-point communication: detection, diversity and channel uncertainty 4. Cellular systems: multiple access and interference management 5. Capacity of wireless channels 6. Multiuser capacity and opportunistic communication 7. MIMO I: spatial multiplexing and channel modeling 8. MIMO II: capacity and multiplexing architectures 9. MIMO III: diversity-multiplexing tradeoff and universal space-time codes 10. MIMO IV: multiuser communication A. Detection and estimation in additive Gaussian noise B. Information theory background.
8,084 citations