MIMO Detection for High-Order QAM Based on a Gaussian Tree Approximation

Goldberger, Jacob; Leshem, Amir

doi:10.1109/tit.2011.2159037

Cited by 84 publications

(87 citation statements)

References 33 publications

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“…All these algorithm, including EP, have a complexity O(m 3 ). Besides, we do not include comparison with respect MMSE-SIC since its tends to overlap with GTA in most of the SNR range [5]. In Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Indeed, the MMSE solution can be cast as a soft-output detector since it computes the mode of an approximate to the posterior probability p(u|y) where the discrete uniform prior p(u) is replaced by a zero-mean and E s -variance independent multivariate Gaussian distribution. However, it is well known that MMSE solution provides poor performance [5]. Other alternatives also compute the marginal probability distribution based on MMSE and then use an importance sampling algorithm to correct the distribution [6].…”

Section: Introductionmentioning

confidence: 99%

“…Other alternatives also compute the marginal probability distribution based on MMSE and then use an importance sampling algorithm to correct the distribution [6]. The Gaussian tree approximation (GTA) algorithm ignores the discrete nature of the prior p(u) and computes the Gaussian tree approximation that minimizes the KL divergence with respect to p(y|u) [5]. The hard-decision performance of GTA can be significantly outperformed by implementing a successive interference cancellation (SIC) procedure [7].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improved performance of LDPC-coded MIMO systems with EP-based soft-decisions

Cespedes

Olmos

Sanchez-Fernandez

et al. 2014

2014 IEEE International Symposium on Information Theory

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Abstract-Modern communications systems use efficient encoding schemes, multiple-input multiple-output (MIMO) and high-order QAM constellations for maximizing spectral efficiency. However, as the dimensions of the system grow, the design of efficient and low-complexity MIMO receivers possesses technical challenges. Symbol detection can no longer rely on conventional approaches for posterior probability computation due to complexity. Marginalization of this posterior to obtain per-antenna soft-bit probabilities to be fed to a channel decoder is computationally challenging when realistic signaling is used. In this work, we propose to use Expectation Propagation (EP) algorithm to provide an accurate low-complexity Gaussian approximation to the posterior, easily solving the posterior marginalization problem. EP soft-bit probabilities are used in an LDPC-coded MIMO system, achieving outstanding performance improvement compared to similar approaches in the literature for low-complexity LDPC MIMO decoding.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improved performance of LDPC-coded MIMO systems with EP-based soft-decisions

Cespedes

Olmos

Sanchez-Fernandez

et al. 2014

2014 IEEE International Symposium on Information Theory

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show abstract

“…The complexity (in number of real operations) required to compute (11), (12) and (16) is of order . The complexities of computing and are of orders and , respectively.…”

Section: Complexity Comparison Between Mpd and Mmsementioning

confidence: 99%

“…In the recent years, several low complexity detection algorithms which achieve near-optimal performance in large dimensions using complexities comparable to that of MMSE detection have been proposed [1], [2], [6]- [16]. These algorithms are based on local search (e.g., likelihood ascent search (LAS) algorithm and variants in [1], [2], [6], [7]), meta-heuristics (e.g., reactive tabu search (RTS) and variants in [8], [9]), message passing techniques (e.g., belief propagation (BP) based algorithms in [11], [12]), lattice reduction techniques (e.g., lattice reduction (LR) aided detectors in [13], [14]), and Monte-Carlo sampling techniques (e.g., Markov chain Monte Carlo (MCMC) algorithms in [15]). Issues related to channel estimation and low density parity check codes for large-scale MIMO systems are also being addressed [17], [18].…”

mentioning

confidence: 99%

Channel hardening-exploiting message passing (CHEMP) receiver in large MIMO systems

Narasimhan

Chockalingam

2014

2014 IEEE Wireless Communications and Networking Conference (WCNC)

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Abstract-In this paper, we propose a multiple-input multiple-output (MIMO) receiver algorithm that exploits channel hardening that occurs in large MIMO channels. Channel hardening refers to the phenomenon where the off-diagonal terms of the matrix become increasingly weaker compared to the diagonal terms as the size of the channel gain matrix increases. Specifically, we propose a message passing detection (MPD) algorithm which works with the real-valued matched filtered received vector (whose signal term becomes , where is the transmitted vector), and uses a Gaussian approximation on the off-diagonal terms of the matrix. We also propose a simple estimation scheme which directly obtains an estimate of (instead of an estimate of ), which is used as an effective channel estimate in the MPD algorithm. We refer to this receiver as the channel hardening-exploiting message passing (CHEMP) receiver. The proposed CHEMP receiver achieves very good performance in large-scale MIMO systems (e.g., in systems with 16 to 128 uplink users and 128 base station antennas). For the considered large MIMO settings, the complexity of the proposed MPD algorithm is almost the same as or less than that of the minimum mean square error (MMSE) detection. This is because the MPD algorithm does not need a matrix inversion. It also achieves a significantly better performance compared to MMSE and other message passing detection algorithms using MMSE estimate of . Further, we design optimized irregular low density parity check (LDPC) codes specific to the considered large MIMO channel and the CHEMP receiver through EXIT chart matching. The LDPC codes thus obtained achieve improved coded bit error rate performance compared to off-the-shelf irregular LDPC codes.

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Dynamic branch pruning aided low switching fixed complexity sphere decoding for small scale and massive MIMO detection

Chakraborty

Sinha

Mitra

2022

Trans Emerging Tel Tech

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In this article, we have proposed a dynamic branch pruning aided low switching fixed complexity sphere decoding (LSFSD) algorithm that can reduce the number of node visits in conventional fixed‐complexity sphere decoder (FSD) or imbalanced fixed‐complexity sphere decoder (IFSD) detector dynamically based on the channel condition. Further, we propose a low complexity QR‐based FSD ordering scheme in the real domain that requires half of the number of real arithmetic operations in the conventional case. As a case study, we have simulated the proposed algorithm for 4prefix×4$$ 4\times 4 $$ and 6prefix×6$$ 6\times 6 $$ MIMO system with 64‐QAM modulation. The simulation results show that the proposed algorithm can reduce approximately 50% of node visits to achieve a BER of 10prefix−3$$ 1{0}^{-3} $$. Additionally, a low complexity solution of the soft bit information generation method is also proposed. The concept is further extended to massive MIMO applications where the number of receiving antenna to transmit antenna ratio is close to one. A smaller node extension parameter is defined for the proposed detector to reduce the complexity in massive MIMO detection. Simulation results demonstrate that the proposed detection algorithm has significantly lower computational complexity than other recent state‐of‐the‐art detection algorithms for massive MIMO and large MIMO systems.

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MIMO Detection for High-Order QAM Based on a Gaussian Tree Approximation

Cited by 84 publications

References 33 publications

Improved performance of LDPC-coded MIMO systems with EP-based soft-decisions

Improved performance of LDPC-coded MIMO systems with EP-based soft-decisions

Channel hardening-exploiting message passing (CHEMP) receiver in large MIMO systems

Dynamic branch pruning aided low switching fixed complexity sphere decoding for small scale and massive MIMO detection

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