A near-ML performance hybrid dijkstra and firefly algorithm for large MIMO detection
01 Jul 2017-pp 1-6
TL;DR: Simulation results reveal that the proposed hybrid algorithm outperforms the conventional zero forcing, minimum mean square error and successive interference cancellation based MIMO detection techniques in terms of bit error rate (BER) performance and achieves near maximum likelihood BER performance.
Abstract: To meet the ever growing demand of high data rates, employing large number of antennas at the transmitter and receiver is a key feature of future advanced wireless systems. Multiple-input multiple-output (MIMO) systems can provide high data rates with high spectral efficiency and have opened a new gateway in wireless systems. However, design of an efficient, robust and non-erroneous detection algorithm is a huge challenge in MIMO systems. In this paper, a hybrid algorithm has been proposed for large scale MIMO detection. The proposed algorithm is motivated by the popular firefly algorithm and dijkstra's shortest path algorithm. Simulation results reveal that the proposed hybrid algorithm outperforms the conventional zero forcing, minimum mean square error and successive interference cancellation based MIMO detection techniques in terms of bit error rate (BER) performance and achieves near maximum likelihood BER performance. This makes the proposed algorithm an appropriate candidate for reliable detection in large-MIMO systems.
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01 Jan 2020
TL;DR: This chapter introduced one of the key applications of these algorithms, that is, to solve the combinatorial optimization problem of symbol detection in large-scale MIMO systems, and compared the BER performance of different bio-inspired algorithms with the traditional low-complexity detection techniques such as zero forcing and minimum mean squared error detectors.
Abstract: Large-scale multiple-input multiple-output (MIMO) system plays a vital role in realizing the ever-increasing demand for high-speed data in 5G and beyond wireless communication systems. MIMO systems employ multiple antennas at both the transmitter and receiver. These systems can achieve both the spatial diversity and the spatial multiplexing gain, which are required for enhancing the quality of service (QoS) and the capacity of wireless systems, respectively. Howbeit, reliable detection of the transmitted data streams is challenging due to the presence of inter-channel interference and inter-user interference. To address the above symbol detection issues, maximum likelihood (ML) (Van Trees, Detection, estimation, and modulation theory, part I: detection, estimation, and linear modulation theory, 2004, [34]) detection performs an exhaustive search over all the possible transmitted information symbols and achieves optimal bit error rate (BER) performance. However, being an NP-Hard problem, ML detection is practically unfeasible for large MIMO systems. Therefore, alternate low-complexity robust detection techniques are being devised for near-optimal detection in large MIMO systems. Nature-inspired algorithms have been an emerging choice to obtain a better solution for combinatorial optimization problems. Recently, nature-inspired algorithms has attracted the attention of researchers from wireless communication community, due to its simple implementation and low-complexity behaviour in solving research problems in communication. In this chapter, we have discussed some of the promising bio-inspired techniques such as ant colony optimization and social spider optimization, and introduced one of the key applications of these algorithms, that is, to solve the combinatorial optimization problem of symbol detection in large-scale MIMO systems. We have also compared the BER performance of different bio-inspired algorithms with the traditional low-complexity detection techniques such as zero forcing and minimum mean squared error detectors.
References
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TL;DR: A tree is a graph with one and only one path between every two nodes, where at least one path exists between any two nodes and the length of each branch is given.
Abstract: We consider n points (nodes), some or all pairs of which are connected by a branch; the length of each branch is given. We restrict ourselves to the case where at least one path exists between any two nodes. We now consider two problems. Problem 1. Constrnct the tree of minimum total length between the n nodes. (A tree is a graph with one and only one path between every two nodes.) In the course of the construction that we present here, the branches are subdivided into three sets: I. the branches definitely assignec~ to the tree under construction (they will form a subtree) ; II. the branches from which the next branch to be added to set I, will be selected ; III. the remaining branches (rejected or not yet considered). The nodes are subdivided into two sets: A. the nodes connected by the branches of set I, B. the remaining nodes (one and only one branch of set II will lead to each of these nodes), We start the construction by choosing an arbitrary node as the only member of set A, and by placing all branches that end in this node in set II. To start with, set I is empty. From then onwards we perform the following two steps repeatedly. Step 1. The shortest branch of set II is removed from this set and added to
22,704 citations
"A near-ML performance hybrid dijkst..." refers background in this paper
...DSPA [38] finds the shortest path possible from the source vertex s to all the vertices in the Graph G....
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04 Oct 1995
TL;DR: The optimization of nonlinear functions using particle swarm methodology is described and implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm.
Abstract: The optimization of nonlinear functions using particle swarm methodology is described. Implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm. Benchmark testing of both paradigms is described, and applications, including neural network training and robot task learning, are proposed. Relationships between particle swarm optimization and both artificial life and evolutionary computation are reviewed.
14,477 citations
"A near-ML performance hybrid dijkst..." refers background in this paper
...Some of these bio-inspired algorithms are genetic algorithm (GA) [13], particle swarm optimization (PSO) [14], ant colony optimization (ACO) [15] and firefly algorithm (FA) [16]....
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Book•
01 Jan 1996TL;DR: An Introduction to Genetic Algorithms focuses in depth on a small set of important and interesting topics -- particularly in machine learning, scientific modeling, and artificial life -- and reviews a broad span of research, including the work of Mitchell and her colleagues.
Abstract: From the Publisher:
"This is the best general book on Genetic Algorithms written to date. It covers background, history, and motivation; it selects important, informative examples of applications and discusses the use of Genetic Algorithms in scientific models; and it gives a good account of the status of the theory of Genetic Algorithms. Best of all the book presents its material in clear, straightforward, felicitous prose, accessible to anyone with a college-level scientific background. If you want a broad, solid understanding of Genetic Algorithms -- where they came from, what's being done with them, and where they are going -- this is the book.
-- John H. Holland, Professor, Computer Science and Engineering, and Professor of Psychology, The University of Michigan; External Professor, the Santa Fe Institute.
Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics -- particularly in machine learning, scientific modeling, and artificial life -- and reviews a broad span of research, including the work of Mitchell and her colleagues.
The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting "general purpose" nature of genetic algorithms as search methods that can be employed across disciplines.
An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text.
The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation.
9,933 citations
TL;DR: An Introduction to Genetic Algorithms as discussed by the authors is one of the rare examples of a book in which every single page is worth reading, and the author, Melanie Mitchell, manages to describe in depth many fascinating examples as well as important theoretical issues.
Abstract: An Introduction to Genetic Algorithms is one of the rare examples of a book in which every single page is worth reading. The author, Melanie Mitchell, manages to describe in depth many fascinating examples as well as important theoretical issues, yet the book is concise (200 pages) and readable. Although Mitchell explicitly states that her aim is not a complete survey, the essentials of genetic algorithms (GAs) are contained: theory and practice, problem solving and scientific models, a \"Brief History\" and \"Future Directions.\" Her book is both an introduction for novices interested in GAs and a collection of recent research, including hot topics such as coevolution (interspecies and intraspecies), diploidy and dominance, encapsulation, hierarchical regulation, adaptive encoding, interactions of learning and evolution, self-adapting GAs, and more. Nevertheless, the book focused more on machine learning, artificial life, and modeling evolution than on optimization and engineering.
7,098 citations
"A near-ML performance hybrid dijkst..." refers background in this paper
...Some of these bio-inspired algorithms are genetic algorithm (GA) [13], particle swarm optimization (PSO) [14], ant colony optimization (ACO) [15] and firefly algorithm (FA) [16]....
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TL;DR: While massive MIMO renders many traditional research problems irrelevant, it uncovers entirely new problems that urgently need attention: the challenge of making many low-cost low-precision components that work effectively together, acquisition and synchronization for newly joined terminals, the exploitation of extra degrees of freedom provided by the excess of service antennas, reducing internal power consumption to achieve total energy efficiency reductions, and finding new deployment scenarios.
Abstract: Multi-user MIMO offers big advantages over conventional point-to-point MIMO: it works with cheap single-antenna terminals, a rich scattering environment is not required, and resource allocation is simplified because every active terminal utilizes all of the time-frequency bins. However, multi-user MIMO, as originally envisioned, with roughly equal numbers of service antennas and terminals and frequency-division duplex operation, is not a scalable technology. Massive MIMO (also known as large-scale antenna systems, very large MIMO, hyper MIMO, full-dimension MIMO, and ARGOS) makes a clean break with current practice through the use of a large excess of service antennas over active terminals and time-division duplex operation. Extra antennas help by focusing energy into ever smaller regions of space to bring huge improvements in throughput and radiated energy efficiency. Other benefits of massive MIMO include extensive use of inexpensive low-power components, reduced latency, simplification of the MAC layer, and robustness against intentional jamming. The anticipated throughput depends on the propagation environment providing asymptotically orthogonal channels to the terminals, but so far experiments have not disclosed any limitations in this regard. While massive MIMO renders many traditional research problems irrelevant, it uncovers entirely new problems that urgently need attention: the challenge of making many low-cost low-precision components that work effectively together, acquisition and synchronization for newly joined terminals, the exploitation of extra degrees of freedom provided by the excess of service antennas, reducing internal power consumption to achieve total energy efficiency reductions, and finding new deployment scenarios. This article presents an overview of the massive MIMO concept and contemporary research on the topic.
6,184 citations
"A near-ML performance hybrid dijkst..." refers background in this paper
...High capacity with large number of transmit and receive antennas makes large multiple-input multiple-output (MIMO) wireless communication systems a suitable technique for future 5G wireless systems [1]....
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