Near optimal multipoint receive beamforming with finite samples (original) (raw)
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2010
Multiple Input Multiple Output (MIMO) multiplexing is a promising technology that could greatly increase the channel capacity without additional spectral resources. The challenge is to design low complexity and high performance algorithms that capable of accurately detecting the transmitted signals. In this study, the general model of MIMO communication system was introduced in addition to several MIMO Spatial Multiplexing (SM) detection techniques. The BER performance and computational complexity of the optimal and sub-optimal MIMO detection schemes have been analyzed and compared to each other. For ease of understanding and fair comparison, the MIMO detection techniques are categorized into three main categories; viz., linear schemes, successive interference cancelation, and tree-search techniques. Different aspects have been considered and discussed in this evaluation such as; signal to noise ratio, channel matrix conditionality, number of transmit and receive antennas, and other...
SIGNAL DETECTION FOR SPATIALLY MULTIPLEXED MIMO SYSTEM
In this paper; we are studying techniques for signal detection in Multiple Input Multiple Output (MIMO) spatial multiplexing system. This paper includes techniques like Maximum likelihood (ML). The ML detection achieves the optimal performances with reduced complexity using BPSK modulation. The simulation result of ML decoder shows the Bit error rate (BER) reduces if signal to noise to ratio increases.
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In this contribution, we present a novel multipleinput multiple-output (MIMO) detection scheme for Quadrature Amplitude Modulation (QAM). The proposed method is based on the slowest descent (SD) method and is suitable for both iterative and non-iterative detectors. Optimal MIMO detection entails maximizing or marginalizing a likelihood function over a very large set of vectors. Similar to other low-complexity detection schemes such as sphere decoding, the SD approach restricts the maximization/marginalization to a small set of candidate vectors. As the size of this set scales linearly with the number of transmit antennas, the SD method bears an exceptionally low complexity. Furthermore, simulation results indicate that the method achieves a close-to-optimal performance, provided that the diversity order is sufficiently high.
Transmit beamforming with cooperative base stations
Proceedings. International Symposium on Information Theory, 2005. ISIT 2005., 2005
We consider a cellular network where base stations can cooperate to determine the signals to be transmitted on the downlink. In such a scenario, it would be possible to use "macroscopic" transmit beamforming to improve system performance. The downlink beamformer of interest is generalised from some transmit beamformers that have been shown to meet various optimality criteria in the literature. The particular downlink beamformer structure enables us to recast our downlink beamforming problem as a virtual LMMSE estimation problem. Based on this virtual set up, we exploit the structure of the channel and develop distributed beamforming algorithms using local message passing between neighbouring base stations. Two algorithms are outlined, both of which are based on the Kalman smoothing framework. The first algorithm is a forward-backward algorithm that produces optimal performance, but it has the disadvantage of a delay that grows linearly with array size. The second algorithm, which is a limited extent algorithm, solves the delay problem by using only local information.
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 Performance Comparison of Spatial Multiplexing MIMO Detectors
International Journal of Computer Applications, 2015
Many techniques are used in MIMO for various purposes, such as SM (spatial multiplexing), SD (spatial diversity) and antenna beam forming. Among them spatial multiplexing is used in MIMO for accommodating high data-rates applications. In, SM, independent information sequences called as layers are simultaneously transmitted from independent antennas. So, the overall bit-rate compared to single antenna system is thus largely enhanced without requiring extra bandwidth or extra transmission power. However during transmission through channel, individual layers are overlying with each other and MSI (multi stream interference) will occurs at the receiver. So, it is very difficult to obtain intended symbol from the bunch of streams. To solve out this problem of MSI various approaches have been proposed, which provides efficient approximate solution of the detection problem at receiver. Such as zero forcing (ZF), minimum mean square error (MMSE), successive interference cancellation (SIC), Ordered successive interference cancellation (OSIC). In this paper error performance of these SM detection schemes are investigated. They are compared on the basis of their BER performance.
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The advent of the fifth generation (5G) of mobile networks has introduced several new use cases that are pushing mobile networks in environments that are typically supported by wired technologies. The initial discussions around the sixth generation (6G) of mobile networks signalizes that different approaches are needed to address all contrasting requirements, where multiple-input multipleoutput (MIMO) technique stands as a key technology for most future wireless systems. In this review, we present an introduction on classical linear estimators and coherent detectors along with an innovative and accurate complexity formulation within a common framework, allowing a fair comparison and providing an initial guideline for researchers that are looking for a general view of the main techniques available for spatial multiplexing (SM)-MIMO detection and estimation.
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In this paper, we propose a combined iterative detection and decoding technique that is capable of achieving the maximum diversity of order N T × L × N R over singlecarrier multiple-input-multiple-output (MIMO) frequencyselective Rayleigh fading channels, where N T and N R denote the number of transmit and receive antennas, respectively, and L is the number of multipath components. The so-called spacetime weighted-nonbinary-repeat-accumulate (ST-WNRA) codes are considered in our paper due to their ability to provide a full transmit antenna diversity and their relatively simple encoding and decoding algorithms. Multipath diversity is obtained using a joint-over-antenna turbo-equalization technique based on the minimum-mean-square-error filtering with soft interference cancellation. Computer simulations demonstrate that our proposed turbo-equalized system with ST-WNRA codes is capable of achieving the maximum diversity order with a relatively short codeword length and that the multiuser performance approaches the single-user bound so far as the number of users is smaller than or equal to the number of receive antennas in multiuser MIMO setups. We will also show that by modifying our proposed scheme to an equivalent multilevel coded system, higher bandwidth efficiency can be achieved at the expense of a performance loss while the system still retains the maximum diversity benefit. Index Terms-Iterative decoding, multilevel coding, multipleinput-multiple-output (MIMO) systems, repeat-accumulate codes, turbo equalization. I. INTRODUCTION T HE NOTION of diversity has been widely accepted as one of the most important component for reliable wireless communications. Information theoretic aspects of transmit diversity studied by Telatar [1] and Foschini and Gans [2] have demonstrated that the capacity of multiple-input-multipleoutput (MIMO) systems significantly exceeds that of singleantenna systems in scattering rich fading channels. These promising results prompted the development of several socalled space-time-coding (STC) schemes, notably space-time trellis codes (STTC) [3] and space-time block codes (STBC)
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IEEE Transactions on Signal Processing, 2008
A subspace beamforming method is presented that decomposes a MIMO channel into multiple pairs of subchannels. The pairing is done based on singular values such that similar channel capacity is obtained between different subchannel pairs. This new capacity balancing concept is key to achieving high performance with low complexity. We apply the subspace idea to geometric mean decomposition (GMD) and maximum likelihood (ML) detection. The proposed subspace GMD scheme requires only two layers of detection/decoding, regardless of the total number of subchannels, thus alleviating the latency issue associated with conventional GMD. We also show how the subspace concept makes the optimization of ML beamforming and ML detection itself feasible for any K × K MIMO system. Simulation results show that subspace beamforming performs nearly as well as optimum GMD performance, and to within only a few dB of the Shannon bound.
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