Low-Complexity Half-Sparse Decomposition-based Detection for massive MIMO Transmission (original) (raw)
Related papers
New Iterative Detector of MIMO Transmission Using Sparse Decomposition
IEEE Transactions on Vehicular Technology, 2014
This paper addresses the problem of decoding in large scale MIMO systems. In this case, the optimal maximum likelihood detector becomes impractical due to an exponential increase of the complexity with the signal and the constellation dimensions. Our work introduces an iterative decoding strategy with a tolerable complexity order. We consider a MIMO system with finite constellation and model it as a system with sparse signal sources. We propose an ML relaxed detector that minimizes the euclidean distance with the received signal while preserving a constant ℓ1-norm of the decoded signal. We also show that the detection problem is equivalent to a convex optimization problem which is solvable in polynomial time. Two applications are proposed, and simulation results illustrate the efficiency of the proposed detector.
A Novel Detector based on Compressive Sensing for Uplink Massive MIMO Systems
Journal of Information Systems and Telecommunication (JIST)
Massive multiple-input multiple-output is a promising technology in future communication networks where a large number of antennas are used. It provides huge advantages to the future communication systems in data rate, the quality of services, energy efficiency, and spectral efficiency. Linear detection algorithms can achieve a near-optimal performance in largescale MIMO systems, due to the asymptotic orthogonal channel property. But, the performance of linear MIMO detectors degrades when the number of transmit antennas is close to the number of receive antennas (loaded scenario). Therefore, this paper proposes a series of detectors for large MIMO systems, which is capable of achieving promising performance in loaded scenarios. The main idea is to improve the performance of the detector by finding the hidden sparsity in the residual error of the received signal. At the first step, the conventional MIMO model is converted into the sparse model via the symbol error vector obtained from a linear detector. With the aid of the compressive sensing methods, the incorrectly detected symbols are recovered and performance improvement in the detector output is obtained. Different sparse recovery algorithms have been considered to reconstruct the sparse error signal. This study reveals that error recovery by imposing sparse constraint would decrease the bit error rate of the MIMO detector. Simulation results show that the iteratively reweighted least squares method achieves the best performance among other sparse recovery methods.
Large-Scale MIMO Receiver Based on Finite-Alphabet Sparse Detection and Concave-Convex Optimization
2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2020
In this paper, we propose a new receiver for detecting signals in large-scale Spatially Multiplexed (SP) Multiple-Input-Multiple-Output (MIMO) systems that may have fewer receive antennas than transmitted symbols (overloaded case). Relying on the idea of Finite-Alphabet Sparse (FAS) detection, we formulate the Maximum Likelihood (ML) criterion as a Difference-of-Convex (DC) programming problem that can be simply and efficiently solved using the Concave-Convex Procedure (CCP) technique. Since, the considered problem is nonconvex, we theoretically discuss the behavior of the derived algorithm. Numerical experiments confirm the superiority of the proposed detection scheme, when compared with recent detection methods based on convex optimization, in a variety of large-scale MIMO transmission scenarios including the overloaded case.
A Novel MIMO Detection Scheme with Linear Complexity
2007 IEEE Wireless Communications and Networking Conference, 2007
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.
New decoding strategy for underdetermined mimo transmission sparse decomposition
In this paper we address the problem of large dimension decoding in MIMO systems. The complexity of the optimal maximum likelihood detection makes it unfeasible in practice when the number of antennas, the channel impulse response length or the source constellation size become too high. We consider a MIMO system with finite constellation and model it as a system with sparse signal sources. We formulate the decoding problem as an underdetermined sparse source recovering problem and apply the ℓ 1 -minimization to solve it. The resulting decoding scheme is applied to large MIMO systems and to frequency selective channel . We also review the computational cost of some ℓ 1 -minimization algorithms. Simulation results show significant improvement compared to other existing receivers.
Detailed Study on Low Complexity Detection Techniques for Large MIMO System: A Review
International Journal for Research in Applied Science and Engineering Technology, 2017
The MIMO technology playing the vital role in the deployment of 3G and 4G communication, and further its advancement like 5G communication is yet to working upon. The bigger the communication network the more number of antenna we need to send the data in an efficient way, so that we could achieve the high spectral efficiency. The major concern in this area is the detection complexity at the receiver section, it increases with the number of antenna increases. To nullify the effect of the detection complexity there are some detection algorithm by which we can diminish the effect of complexity. Three major section of detection algorithm are 1. optimal detection, 2. suboptimal detection, 3. Near optimal detection. It consists the ML detection in the optimal detection. There are two part of the suboptimal detection, which is linear and non-linear detection techniques containing ZF, MMSE RTS, the third one contains LAS, K-neighborhood.
Large MIMO Detection: A Low-Complexity Detector at High Spectral Efficiencies
2008
We consider large MIMO systems, where by 'large' we mean number of transmit and receive antennas of the order of tens to hundreds. Such large MIMO systems will be of immense interest because of the very high spectral efficiencies possible in such systems. We present a low-complexity detector which achieves uncoded near-exponential diversity performance for hundreds of antennas (i.e., achieves near SISO AWGN performance in a large MIMO fading environment) with an average per-bit complexity of just O(NtNr), where Nt and Nr denote the number of transmit and receive antennas, respectively. With an outer turbo code, the proposed detector achieves good coded bit error performance as well. For example, in a 600 transmit and 600 receive antennas V-BLAST system with a high spectral efficiency of 200 bps/Hz (using BPSK and rate-1/3 turbo code), our simulation results show that the proposed detector performs close to within about 4.6 dB from theoretical capacity. We also adopt the proposed detector for the lowcomplexity decoding of high-rate non-orthogonal space-time block codes (STBC) from division algebras (DA). For example, we have decoded the 16 × 16 full-rate non-orthogonal STBC from DA using the proposed detector and show that it performs close to within about 5.5 dB of the capacity using 4-QAM and rate-3/4 turbo code at a spectral efficiency of 24 bps/Hz. The practical feasibility of the proposed high-performance lowcomplexity detector could potentially trigger wide interest in the implementation of large MIMO systems. We also illustrate the applicability of the proposed detector in the low-complexity detection of large multicarrier CDMA (MC-CDMA) systems. In large MC-CDMA systems with hundreds of users, the proposed detector is shown to achieve near single-user performance at an average per-bit complexity linear in number of users, which is quite appealing for its use in practical CDMA systems.
IEEE Wireless Communications Letters, 2019
In this paper, we propose an algorithm based on the Alternating Minimization technique to solve the uplink massive MIMO detection problem. The proposed algorithm is specifically designed to avoid any matrix inversion and any computations of the Gram matrix at the receiver. The algorithm provides a lower complexity compared to the conventional MMSE detection technique, especially when the total number of user equipment (UE) antennas (across all users) is close to the number of base station (BS) antennas. The idea is that the algorithm reformulates the maximum likelihood (ML) detection problem as a sum of convex functions based on decomposing the received vector into multiple vectors. Each vector represents the contribution of one of the transmitted symbols in the received vector. Alternating Minimization is used to solve the new formulated problem in an iterative manner with a closed form solution update in every iteration. Simulation results demonstrate the efficacy of the proposed algorithm in the uplink massive MIMO setting for both coded and uncoded cases.
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.