Euclidean Distances in Quantized Spaces with Pre-stored Components for MIMO Detection

Monteiro, Francisco A.; Wassell, I.J.

doi:10.1109/ecwt.2007.4403968

Cited by 4 publications

(1 citation statement)

References 8 publications

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“…Based on the expression in Equation ( 16), the convergence of the iterations can be measured by the difference between xk d and xk+1 . Specifically, the Euclidean distance, which is one of the widely-used approaches for measuring the distance between two vectors [30], is defined as:…”

Section: Modified Ide-based Detection Algorithm With Self-update Dampingmentioning

confidence: 99%

A Novel Iterative Discrete Estimation Algorithm for Low-Complexity Signal Detection in Uplink Massive MIMO Systems

Feng

Zhao

et al. 2019

Electronics

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In this paper, a novel iterative discrete estimation (IDE) algorithm, which is called the modified IDE (MIDE), is proposed to reduce the computational complexity in MIMO detection in uplink massive MIMO systems. MIDE is a revision of the alternating direction method of multipliers (ADMM)-based algorithm, in which a self-updating method is designed with the damping factor estimated and updated at each iteration based on the Euclidean distance between the iterative solutions of the IDE-based algorithm in order to accelerate the algorithm’s convergence. Compared to the existing ADMM-based detection algorithm, the overall computational complexity of the proposed MIDE algorithm is reduced from O N t 3 + O N r N t 2 to O N t 2 + O N r N t in terms of the number of complex-valued multiplications, where Ntand Nr are the number of users and the number of receiving antennas at the base station (BS), respectively. Simulation results show that the proposed MIDE algorithm performs better in terms of the bit error rate (BER) than some recently-proposed approximation algorithms in MIMO detection of uplink massive MIMO systems.

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Section: Modified Ide-based Detection Algorithm With Self-update Dampingmentioning

confidence: 99%

A Novel Iterative Discrete Estimation Algorithm for Low-Complexity Signal Detection in Uplink Massive MIMO Systems

Feng

Zhao

et al. 2019

Electronics

View full text Add to dashboard Cite

show abstract

Segmental analysis of the transmission in CSK systems based on the Euclidean distance

2015

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Progressive Hypercube Decoding

Monteiro

Wassell

2007

2007 4th International Symposium on Wireless Communication Systems

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This paper presents a detection technique for parallel to the ML curves, though typically degraded by 3 to 5 multiple-input multiple-output (MIMO) spatial multiplexing dB). systems that is based in a quantized received lattice. This permits Sphere decoding (SD) [6], [7] (originally devised for lattice the search to be focused into successive ordered subspaces. The decoding [8]) attains the performance of ML with a much proposal greatly reduces the number Euclidian distances need to be calculated replacing it by memory usage for back tracing seri number opr n [2], [3], and hasinedseveal candidate vectors. Thus, this tool enables further pruning the derivations, e.g., [9], [10], [11]. For N-dimensional search tree when using a sphere decoder. The receiver starts by hyperspheres, the number of operations is O(N ) to o(N3) in quantizing both the received vector and the lattice points and the high SNR regime; at low SNR it still requires a then defines a neighbourhood around the cell containing the exponential number of operations when N grows linearly [9] received vector. If needed, the neighbourhood is extended to a (details in [2]). The idea behind sphere decoding is that only a larger Manhattan distance. Conversely, dense clusters of lattice reduced number of signals which yield a received point inside points are resolved by increasing the quantization bits per a hypersphere centred in the received signal will be dimension. The paper describes the probability density functions considered instead of performing an exhaustive search over all (pdf) of the lattice components and presents numerical results for possible combinations of transmitted of symbols. The the pdf of the number of lattice points that are candidates to be ple co ns of speed of symboriTh evaluated in a subsequent block based on Euclidean distances. complexity (and consequently the speed) of the SD algorithm All the results are for the frequency flat fast fading channel. is very dependent on the radius of the considered sphere (which value is selected heuristically [2], [3], and gives raise I. INTRODUCTIONto some problems regarding the ordering of the searched In many wireless propagation scenarios there is a points [10] (and references therein). sufficiently rich scattering environment giving raise to Many different metrics to quantify the detection complexity multipath. This phenomenon can be exploited in order to can be found in the literature; for example, complexity can be increase the diversity of the link and consequently reduce the measured by the number of flops required [7], by estimating symbol error rate (SER), or, on the other hand, increase the the average number of expanded nodes [10], by comparing the overall data rate. The later type of MIMO systems [1], computation time of the algorithms in a computer [9] or by perform spatial multiplexing by transmitting several data counting the total number of multiplications required by the streams in parallel and therefore increasing the spectral detection algorithm [11]. efficiency of the ph...

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Euclidean Distances in Quantized Spaces with Pre-stored Components for MIMO Detection

Cited by 4 publications

References 8 publications

A Novel Iterative Discrete Estimation Algorithm for Low-Complexity Signal Detection in Uplink Massive MIMO Systems

A Novel Iterative Discrete Estimation Algorithm for Low-Complexity Signal Detection in Uplink Massive MIMO Systems

Segmental analysis of the transmission in CSK systems based on the Euclidean distance

Progressive Hypercube Decoding

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