Jointly optimized rate and outer loop power control with single- and multi-user detection
TL;DR: In this article, a joint optimization of outer loop power control (OLPC) and rate control using variable spreading factors (VSFs) is proposed to enhance the achievable spectral efficiency of multiuser CDMA fading channels.
Abstract: We propose a technique for enhancing the achievable spectral efficiency of multiuser direct-sequence code-division multiple-access (DS-CDMA) fading channels in the presence of additive white Gaussian noise (AWGN) and multiple access interference (MAI). The proposed scheme involves the joint optimization of outer loop power control (OLPC) and rate control using variable spreading factors (VSFs). The optimality is in the sense of average spectral efficiency maximization. The optimum outer loop target signal-to-noise ratio (SNR-target) and the corresponding spreading factor are derived jointly, in terms of the number of active users. Along with transmit power adaptation in the inner loop, this leads to maximized average spectral efficiency. Total and truncated channel inversion strategies are used in the inner loop. The average spectral efficiency of the joint optimization scheme is derived for the conventional matched-filter and the multiuser decorrelating detectors. Average transmit power and instantaneous bit error rate (BER) constraints are considered and the performance is evaluated over Nakagami-m flat-fading channels. In low SNRs, the proposed scheme can provide a considerable gain in bits/s/Hz for either of the detectors, compared to a VSF-assisted system that does not exploit OLPC and thus the optimum SNR-target.
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TL;DR: Simulation results show that the proposed scheme achieves up to 33\% and 68\% gains in terms of the energy efficiency in both single-user and multi-user cases compared to the conventional RIS scheme and amplify-and-forward relay scheme, respectively.
Abstract: This paper investigates the problem of resource allocation for a wireless communication network with distributed reconfigurable intelligent surfaces (RISs). In this network, multiple RISs are spatially distributed to serve wireless users and the energy efficiency of the network is maximized by dynamically controlling the on-off status of each RIS as well as optimizing the reflection coefficients matrix of the RISs. This problem is posed as a joint optimization problem of transmit beamforming and RIS control, whose goal is to maximize the energy efficiency under minimum rate constraints of the users. To solve this problem, two iterative algorithms are proposed for the single-user case and multi-user case. For the single-user case, the phase optimization problem is solved by using a successive convex approximation method, which admits a closed-form solution at each step. Moreover, the optimal RIS on-off status is obtained by using the dual method. For the multi-user case, a low-complexity greedy searching method is proposed to solve the RIS on-off optimization problem. Simulation results show that the proposed scheme achieves up to 33\% and 68\% gains in terms of the energy efficiency in both single-user and multi-user cases compared to the conventional RIS scheme and amplify-and-forward relay scheme, respectively.
125 citations
01 Nov 1995
75 citations
TL;DR: A system where the number of users in a cell is modeled by a one-dimensional discrete Markov chain, and the adaptive continuous power and rate mechanism for the worst case packet error rate (PER) condition is proposed.
Abstract: In CDMA systems, outer loop power control (OLPC) determines the target value of SNR at the receiver, mostly by using look-up tables to map bit error rates (BERs) to SNR-targets. In this contribution, transmission delay and packet loss rate constraints in the data link layer (DLL) are invoked in order to determine the optimum outer loop SNR-target setpoint analytically, according to the number of active users in cell. Optimality is, in this sense, the maximization of system throughput. Using the optimum SNR-target, the optimal spreading factor is determined. Subsequently, the joint optimization of outer loop SNR-target and variable spreading factor (VSF), at the physical(PHY)-layer, with truncated automatic repeat request (ARQ) error control mechanism at the data link layer are proposed. Hence, we show that quality of service (QoS) requirements at these layers can be simultaneously satisfied while maximizing throughput. Total and truncated channel inversion strategies are employed in the inner loop to adapt transmit power to short-time channel variations. We propose a system where the number of users in a cell is modeled by a one-dimensional discrete Markov chain, and design the adaptive continuous power and rate mechanism for the worst case packet error rate (PER) condition. The corresponding theoretical throughput, which can be regarded as upper-bound for discrete spreading factor case, is obtained numerically for various settings of system parameters. We have also provided simulation results for a practical channel condition. Our scheme is compared with "constant SNR-target" and "PHY-layer based variable SNR-target" cases under continuous power and rate variation to show the achievable gain through the coupling of physical and data link layers parameters.
22 citations
TL;DR: A novel cross-layer design based on priority-based multiple access (PBMA) based on prioritised messaging between different vehicles to enhance the delivery of emergency messages so that accidents can be further avoided.
Abstract: Among all requirements for vehicle-to-everything (V2X) communications, successful delivery of packets with small delay is of the highest significance. Especially, the delivery of a message before a potential accident (i.e. emergency message) should be guaranteed. In this work, we propose a novel cross-layer design to enhance the delivery of emergency messages so that accidents can be further avoided. Particularly, in the PHY layer, imperfect full-duplex (FD) simultaneous transmitting and sensing is analysed and dynamic thresholds for determining the channel status before and during transmissions are mathematically formulated. Then a novel FD MAC protocol, named priority-based multiple access (PBMA), based on prioritised messaging between different vehicles is proposed. Average collision probability, collision duration, waiting time as well as successful delivery rate of the system are formulated too. The delivery performance of emergency messages is also mathematically derived. In addition, comparisons have been made among three different mechanisms. Benchmark is the DSRC standard which uses half-duplex (HD) technology with enhanced distributed channel access (EDCA) protocol. We also compare our proposed protocol with FD EDCA. Simulations have verified the accuracy of our analysis. They have also illustrated the delivery of emergency messages has been enhanced by deploying our proposed design.
7 citations
TL;DR: In this article, a symbol-level selective transmission for full-duplex (FD) relaying networks is proposed to mitigate error propagation effects and improve system spectral efficiency, where the FD relay node can predict the correctly decoded symbols of each frame, based on the generalized square deviation method, and discard the erroneously decoding symbols, resulting in fewer errors being forwarded to the destination node.
Abstract: In this paper, a symbol-level selective transmission for full-duplex (FD) relaying networks is proposed to mitigate error propagation effects and improve system spectral efficiency. The idea is to allow the FD relay node to predict the correctly decoded symbols of each frame, based on the generalized square deviation method, and discard the erroneously decoded symbols, resulting in fewer errors being forwarded to the destination node. Using the capability for simultaneous transmission and reception at the FD relay node, our proposed strategy can improve the transmission efficiency without extra cost of signaling overhead. In addition, targeting on the derived expression for outage probability, we compare it with half-duplex relaying case and provide the transmission power and relay location optimization strategy to further enhance the system performances. The results show that our proposed scheme outperforms the classic relaying protocols, such as cyclic redundancy check-based selective decode-and-forward (S-DF) relaying and threshold-based S-DF relaying in terms of outage probability and bit error rate. Moreover, the performances with optimal power allocation are better than those with equal power allocation, especially when the FD relay node encounters strong self-interference and/or it is close to the destination node.
6 citations
References
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Book•
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
Book•
[...]
TL;DR: Undergraduate and graduate classes in computer networks and wireless communications; undergraduate classes in discrete mathematics, data structures, operating systems and programming languages.
Abstract: Undergraduate and graduate classes in computer networks and wireless communications; undergraduate classes in discrete mathematics, data structures, operating systems and programming languages. Also give lectures to both undergraduate-and graduate-level network classes and mentor undergraduate and graduate students for class projects.
6,991 citations
Book•
[...]
01 Aug 1998
TL;DR: This self-contained and comprehensive book sets out the basic details of multiuser detection, starting with simple examples and progressing to state-of-the-art applications.
Abstract: From the Publisher:
The development of multiuser detection techniques is one of the most important recent advances in communications technology. This self-contained and comprehensive book sets out the basic details of multiuser detection, starting with simple examples and progressing to state-of-the-art applications. The only prerequisites assumed are undergraduate-level probability, linear algebra, and digital communications. The book contains over 240 exercises and will be a suitable textbook for electrical engineering students. It will also be an ideal self-study guide for practicing engineers, as well as a valuable reference volume for researchers in communications, information theory, and signal processing.
5,048 citations