Robust conjugate-gradient based LAS detector for massive MIMO systems

Solanki, Mitesh; Gupta, Shilpi

doi:10.1080/00207217.2021.1941292

Cited by 3 publications

(6 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using the Fletcher-Reeves method [21], conjugate directions are formed and developed. Algorithm 1 [23] describes the CGLAS signal identification algorithm.…”

Section: Optimization Approachmentioning

confidence: 99%

A Novel Robust Detector for Cell-Free Massive MIMO System

Solanki,

Gupta,

Mashru

2023

Preprint

Self Cite

View full text Add to dashboard Cite

Massive MIMO increases spectral efficiency and energy efficiency in 5G by using beamforming and spatial multiplexing. It employs a centralized solution to densify a network, resulting in intercellular interference because of its cell-centric design. Massive MIMO systems enable user-centric transmission in cellular networks to overcome inter-cell interference limitations and provide macro-diversity. Massive MIMO distinguishes itself from coordinated, distributed wireless systems with its scalability and cell-free design. This article investigates the immense potential of our proposed detector, this promising technology, and practical deployment issues due to signal coprocessing's increased backhauling overhead. We propose a conjugate gradient (CG)-based detector that enables fast-er convergence and smaller approximated errors for cell-free massive MIMO systems. An analytical approach describes the asymptotic performance of a con-jugate gradient-based likelihood ascent search detector with massive antennas. A joint channel estimation and signal detection (JCD) method is also used for real-world scenarios with imperfect channel state information (CSI). As a result of simulations, our proposed detection scheme outperforms conventional detection schemes.

show abstract

“…Using the Fletcher-Reeves method [21], conjugate directions are formed and developed. Algorithm 1 [23] describes the CGLAS signal identification algorithm.…”

Section: Optimization Approachmentioning

confidence: 99%

A Novel Robust Detector for Cell-Free Massive MIMO System

Solanki,

Gupta,

Mashru

2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Before calculating v 2kÀ1 and v 2k , the partial solution vector x2kþ1:2N t ¼ ½x 2kþ1 , …, x2N t is already available. It is clear from (11) and ( 12) that v 2kÀ1 depends on x2k , which can be obtained from v 2k . Therefore, in this case, v 2k and v 2kÀ1 cannot be determined simultaneously.…”

Section: Reducing the Initialization Complexity Of 1-las/rtsmentioning

confidence: 99%

“…The ð2k À 1, 2kÞ th element of R matrix is equal to zero, that is, r 2kÀ1,2k ¼ 0 for k ¼ 1, 2, …, N t [30]. Thus, (11) can be modified as follows:…”

Section: Reducing the Initialization Complexity Of 1-las/rtsmentioning

confidence: 99%

“…Many detection methods have been reported to address the complexity and performance issues associated with large MIMO systems. These algorithms can be classified as neighborhood search [7][8][9][10][11][12], expectation propagation (EP) [13], approximate message passing (AMP) [14], graph-based algorithms [15], tree search-based algorithms [16][17][18][19][20], and other algorithms [21].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Low complexity hybrid layered tabu‐likelihood ascent search for large MIMO detection with perfect and estimated channel state information

2022

View full text Add to dashboard Cite

In this work, we proposed a low-complexity hybrid layered tabu-likelihood ascent search (LTLAS) algorithm for large multiple-input multiple-output (MIMO) system. The conventional layered tabu search (LTS) approach involves many partial reactive tabu searches (RTSs), and each RTS requires an initialization and searching phase. In the proposed algorithm, we restricted the upper limit of the number of RTS operations. Once RTS operations exceed the limit, RTS will be replaced by low-complexity likelihood ascent search (LAS) operations. The block-based detection approach is considered to maintain a higher signal-to-noise ratio (SNR) detection performance. An efficient precomputation technique is derived, which can suppress redundant computations. The simulation results show that the bit error rate (BER) performance of the proposed detection method is close to the conventional LTS method. The complexity analysis shows that the proposed method has significantly lower computational complexity than conventional methods. Also, the proposed method can reduce almost 50% of real operations to achieve a BER of 10 À3 .

show abstract

“…Further, performance improvement can be achieved by adding a symbol correction stage after an MMSE detector. Several such correction algorithms are used in application in large‐scale MIMO detection such as reactive tabu search (RTS), 24 layered tabu search (LTS), 25 likelihood ascent search (LAS), 26 sequential and global likelihood ascent search 27 conjugate gradient LAS (CGLAS), 28 and so on. Among these algorithms, we have considered LAS 26 method for the massive MIMO detection due to its lower computational complexity and terminates faster than other algorithms.…”

Section: Introductionmentioning

confidence: 99%

Likelihood ascent search‐aided low complexity improved performance massive MIMO detection in perfect and imperfect channel state information

Chakraborty

Sinha²,

Mitra

2022

Int J Communication

View full text Add to dashboard Cite

Summary Massive multiple‐input multiple‐output (MIMO) systems improve spectral efficiency and link reliability. Linear minimum mean‐squared error (MMSE) detectors can achieve optimal performance in massive MIMO detection but require large dimension matrix inversion, which is computationally intensive. Therefore, low complexity iterative detection schemes are proposed in the literature as an alternative to the exact MMSE method. However, the performance of these schemes is greatly influenced by the choice of the initial solution. Therefore, to improve the detection performance in this paper, we proposed three hybrid detection schemes, which are Newton–Schultz–Richardson (NS‐RI), Newton–Schultz–Chebyshev (NS‐Cheby), and Newton–Schultz–Gauss–Seidel (NS‐GS). The proposed hybrid schemes show significant performance improvement and a higher convergence rate compared to their original counterpart. The performance of the proposed detectors is further improved by the likelihood ascent search (LAS) stage, which corrects the detected symbols obtained from iterative MMSE methods through a neighborhood search. However, the complexity of the LAS algorithm primarily depends on the initialization step. In this work, we introduce an efficient Gram matrix computation in the real domain. Additionally, we have applied a band approximation of the Gram matrix for the LAS initialization, which reduces the order of computational complexity of the Gram matrix from Ofalse(NT2NRfalse) to O(ωNTNR) where ω < <2NT.

show abstract

Robust conjugate-gradient based LAS detector for massive MIMO systems

Cited by 3 publications

References 28 publications

A Novel Robust Detector for Cell-Free Massive MIMO System

A Novel Robust Detector for Cell-Free Massive MIMO System

Low complexity hybrid layered tabu‐likelihood ascent search for large MIMO detection with perfect and estimated channel state information

Likelihood ascent search‐aided low complexity improved performance massive MIMO detection in perfect and imperfect channel state information

Contact Info

Product

Resources

About