Hybrid Guided Search Detector for MIMO Systems (original) (raw)
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Guided Search MIMO Detectors Aided by Lattice Reduction
Under MIMO channels, the matched filter detection becomes inefficient to deal with high data throughput demanding systems. The performance or system capacity under conventional detection will be substantially degraded when the spatial diversity provided by multiple antennas can not be fully exploited and the detection process is unable to efficiently separate the signal from each antenna. The solution discussed in this paper seeks to establish more efficient detectors for MIMO systems with the aid of the lattice reduction (LR) technique. These detectors use information from the interfering signals in a way to improve the signal detection in the antenna of interest, thus providing advantages over the conventional system, at the expense of increasing complexity. The focus of this paper consists in comparing the characteristics of three representative sub-optimal detectors based on the maximum-likelihood function as well as on the guided search principle, previously analyzed in . In this way, the complexity × performance trade-off for the sphere detector (SD), the QR decomposition-based detector (QRD) the greedy search detector (GSD) and its variants, all of them aided (or not) by the LR technique are analyzed and its potential of use in MIMO systems is put in perspective.
Guided Search MIMO Detectors aided by Lattice Reduction under Correlated Channels
IEEE Latin America Transactions, 2015
Under MIMO channels, the matched filter detection becomes inefficient to deal with high data throughput demanding systems. The performance or system capacity under conventional detection will be substantially degraded when the spatial diversity provided by multiple antennas can not be fully exploited and the detection process is unable to efficiently separate the signal from each antenna. The solution discussed in this paper seeks to establish more efficient detectors for MIMO systems with the aid of the lattice reduction (LR) technique. These detectors use information from the interfering signals in a way to improve the signal detection in the antenna of interest, thus providing advantages over the conventional system, at the expense of increasing complexity. The focus of this paper consists in comparing the characteristics of three representative sub-optimal detectors based on the maximum-likelihood function as well as on the guided search principle, previously analyzed in [1]. In this way, the complexity × performance trade-off for the sphere detector (SD), the QR decomposition-based detector (QRD) the greedy search detector (GSD) and its variants, all of them aided (or not) by the LR technique are analyzed and its potential of use in MIMO systems is put in perspective.
Complexity and Analysis of Sphere Decoding and QRD-M Detection for MIMO Communication System
2013
MIMO seems to be the ultimate solution to increase the system capacity without requiring the need to additional spectral resources. In MIMO, multiple signals are transmitted instantaneously via enough spaced antennas. At the receiver side, the main challenge resides in designing signal processing techniques, i.e., detection techniques, capable of separating those Transmitted signals with acceptable complexity and achieved performance. Motivated by the importance of the detection techniques as an important factor in determining both the feasibility and performance of the MIMO systems, this study only considers the receiver structure for the MIMO techniques. The study includes a detailed performance analysis of detection algorithms. Also, deep understandings of the affecting factors on the MIMO performance are covered including, the number of transmit and receive antennas, constellation size. The tree-search based detection techniques; i.e., SD and QRD-M with the two promising approac...
Comparative Performance Analysis of Efficient MIMO Detection Approaches
International Journal of Advanced Computer Science and Applications, 2018
The promising massive level MIMO (multipleinput-multiple-output) systems based on extremely huge antenna collections have turned into a sizzling theme of wireless communication systems. This paper assesses the performance of the quasi optimal MIMO detection approach based on semidefinite programming (SDP). This study also investigates the gain obtained when using SDP detector by comparing Bit Error Rate (BER) performance with linear detectors. The near optimal Zero Forcing Maximum Likelihood (ZFML) is also implemented and the comparison is evaluated. The ZFML detector reduces exhaustive ML searching using multi-step reduced constellation (MSRC) detection technique. The detector efficiently combines linear processing with local ML search. The complexity is bounded by maintaining small search areas, while performance is maximized by relaxing this constraint and increasing the cardinality of the search space. The near optimality of SDP is analyzed through BER performance with different antenna configurations using 16-QAM signal constellation operating in a flat fading channel. Simulation results indicate that the SDP detector acquired better BER performance, in addition to a significant decrease in computational complexity using different system/antenna configurations.
2020
MIMO system is used in new communication systems to improve the bit error rate (BER), capacity, and the cochannel interference. In this paper, new hybrid detection techniques based on a combination between the sphere decoder (SD) and linear/non-linear detection techniques such as zero forcing (ZF), minimum mean square error (MMSE), Vertical Bell Lab Layered Space Time (V-BLAST), and lattice reduction are introduced. These hybrid techniques are intended to improve the BER performance of MIMO system. The proposed techniques are mainly based on dividing the received signal matrix into two equal size halves. The first half of the received symbols is detected using the selected linear or non-linear detector and the second half is detected using SD as the first scenario. For the second scenario, the first half of the received symbols is detected using SD and the second half is detected using the selected linear or non-linear detector. Several simulations are carried out to verify the effi...
Guided Local Search in High Performance Detectors for MIMO Systems
2010
This work analyzes efficient non-spreading (a)synchronous MIMO detection topologies under realistic channels which results in high throughput and good performance × complexity trade-off. In this sense, we look for nearoptimum efficient MIMO detections suitable for (un)coding schemes. Main system and channel parameters are analyzed, such as increasing number for transmitter and receiver antennas, number of iterations for convergence under AWGN and Rayleigh fading channels. Two heuristic local search MIMO detectors are compared with other near-optimum detectors, specifically SDR (semidefinite relaxation), expectationmaximization (EM), and linear multiuser detectors. Besides, the MIMO detectors performances under large MIMO systems (high number of transmitter and/or receiver antennas) are analyzed. The performance × complexity tradeoff results have indicated promising features for the guided local search (GLS) procedures in high capacity MIMO detectors. Keywords-MIMO system, heuristic ...
New Parallel Sphere Detector Algorithm Providing High-Throughput for Optimal MIMO Detection
Procedia Computer Science, 2013
Multiple-input multiple-output (MIMO) detection techniques can vary significantly in complexity and detection performance. Finding the optimal Maximum Likelihood (ML) solution with high throughput was limited by the computational performance. In order to achieve high throughput non-ML algorithms were introduced, having degraded detection performance and lower complexity. In this paper we present a new parallel algorithm, inspired by the Sphere Detector (SD) algorithm, which can efficiently solve the ML detection of the MIMO systems with high throughput on parallel architectures. We also give an overview on how it is possible to map the Parallel Sphere Detector (PSD) onto GP-GPUs, however different parallel architectures are also suitable for adapting the presented algorithm.
Improving MIMO sphere detection through antenna detection order scheduling
2011
This paper proposes a novel scalable Multiple-Input Multiple-Output (MIMO) detector that does not require preprocessing to achieve good bit error rate (BER) performance. MIMO processing is a key technology in broadband wireless technologies such as 3G LTE, WiMAX, and 802.11n. Existing detectors such as Flexsphere use preprocessing before MIMO detection to improve performance. Instead of costly preprocessing, the proposed detector schedules multiple search passes, where each search pass detects the transmit stream with a different permuted detection order. By changing the number of parallel search passes, we show that this scalable detector can achieve comparable performance to Flexsphere with reduced resource requirement, or can eliminate LLR clipping and achieve BER performance within 0.25 dB of exhaustive search with increased resource requirement.
A computationally efficient detector for MIMO systems
International Journal of Electrical and Computer Engineering (IJECE), 2019
A well-prepared abstract enables the reader to identify the basic content of a document quickly and accurately, to determine its relevance to their interests, and thus to decide whether to read the document in its entirety. The Abstract should be informative and completely self-explanatory, provide a clear statement of the problem, the proposed approach or solution, and point out major findings and conclusions. The Abstract should be 100 to 200 words in length. The abstract should be written in the past tense. Standard nomenclature should be used and abbreviations should be avoided. No literature should be cited. The keyword list provides the opportunity to add keywords, used by the indexing and abstracting services, in addition to those already present in the title. Judicious use of keywords may increase the ease with which interested parties can locate our article (9 pt). 1. INTRODUCTION In the last decade, cooperative and multiple-input multiple-output (MIMO) techniques have been extensively studied as their improvements in performance do not require additional power or frequency spectrum [1-13]. In this work, the performance of existing linear and nonlinear decoders [2, 14-20] for MIMO systems is compared with the newly proposed decoder that is particularly suitable for implementation on software-defined-radio architectures. The maximum likelihood (ML) decoder is the optimal detector for MIMO systems [2, 15]. In this decoder, a search over all possible combination of transmitted symbol vectors is performed. The ML detection proves to be optimal, however, at the cost of high complexity which increases exponentially with the increase of the modulation size and the number of transmit antennas [15, 16]. On the other hand, linear detectors such as the zero forcing (ZF) and minimum mean squared error (MMSE) detectors are the simplest and widely used detectors with reasonably lower bit error rate (BER) performances at very low computational complexity [2, 4, 17, 18]. Correspondingly, the vertical Bell laboratories layered space-time (V-BLAST) technique uses an iterative detector that implements the concept of successive interference cancellation (SIC) to find a good trade-off between complexity and performance [2, 18-20]. SIC decoder can be further improved by incorporating appropriate ordering of the symbols, i.e., first decoding the symbols that exhibit small estimation error before detecting the weaker symbols. In this work, we are interested in implementing, developing and evaluating a MIMO detector that provides the optimal trade-off between the decoding complexity and BER performance as compared to the state of the art detectors. Therefore, we introduce a new MIMO decoding technique which i) enjoys a good complexity-performance trade-off, ii) allows fully parameterizable performance configuration, in the sense that, the performance of the MIMO detector can be adaptively adjusted without the requirement of changing the
A New Approach of Detection Algorithm for Reducing Computation Complexity of MIMO systems
Indonesian Journal of Electrical Engineering and Computer Science, 2016
Multiple-Input Multiple-Output (MIMO) technique is a key technology to strengthen and achieve high-speed and high-throughput wireless communications. In recent years, it was observed that frequent detecting techniques could improve the performance (e.g., symbol error rate 'SER') of different modern digital communication systems. But these systems faced a problem of high complexity for the practical implementation. To solve the problem of high complexity, this work proposed Frequent Improve K-best Sphere Decoding (FIKSD) algorithm with stopping rule depending on the Manhattan metric. Manhattan metric is proposed to use with FIKSD in order to achieve the lowest complexity. FIKSD is a powerful tool to achieve a high performance close to the maximum likelihood (ML), with less complexity. The simulation results show a good reduction in computation complexity with a cost of slight performance degradation within 1dB; the proposed FIKSD requires 0% to 94% and 82% to 97% less complexity than Improved Kbest Sphere Decoder (IKSD) and K-best Sphere Decoder (KSD) respectively. This makes the algorithm more suitable for implementation in wireless communication systems.