Simplicity-based recovery of finite-alphabet signals for large-scale MIMO systems

2018 IEEE Wireless Communications and Networking Conference (WCNC)

2018

Self Cite

In this paper, we address the problem of large MIMO detection assuming QAM constellations. We show that the QAM signal becomes a simple signal (that is to say a bounded signal with extreme elements equal to the inferior and superior bounds [1]) after a real transformation. Based on this property, we present a low complexity detection algorithm which significantly outperforms classic algorithms such as zero forcing (ZF) and minimum mean square error (MMSE) algorithms. The proposed detection technique is based on a quadratic programming criterion whose constraints ensure that the detected vector is simple. We implement it successfully in an underdetermined MIMO system (the number of observations is less than the number of sources) and we show the necessary conditions of success detection. Then we consider an outer forward error correcting (FEC) code and we propose a turbo detection scheme. Based on the investigation of the output detector statistics in [2], we propose a symbol to binary converter (SBC) which can feed the FEC decoder with reliable output. On the other side, from the second iteration, the detection scheme resorts to a regularized quadratic criterion so that the searched vector draws near to the estimate resulting from the FEC decoder output. Simulation results show the efficiency of the proposed scheme.

Section: A Proposed Methods Definition and Theoretical Analysismentioning

confidence: 99%

Turbo detection based on signal simplicity and compressed sensing for massive MIMO transmission

Hajji

Amis

2018 IEEE Wireless Communications and Networking Conference (WCNC)

2018

Self Cite

“…1) FAS detection: Let us describe the first detection iteration which corresponds to the FAS algorithm introduced in [13]. The data vector x, for all t-th column with t = T p + 1, .…”

Section: Turbo Detection Assuming Perfect Channel Estimationmentioning

confidence: 99%

“…The problem (5) can be solved by the simplex [13] or the interior point methods [14]. In this paper, we consider interior point methods.…”

Section: Turbo Detection Assuming Perfect Channel Estimationmentioning

confidence: 99%

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Joint channel estimation and simplicity-based detection for large-scale MIMO FEC-coded systems

Hajji

Amis

2018 IEEE 10th International Symposium on Turbo Codes &Amp; Iterative Information Processing (ISTC)

2018

Self Cite

In this paper, we address the problem of channel estimation and signal detection in large MIMO FEC-coded systems assuming finite alphabet modulations. We consider a semi-blind iterative expectation maximization algorithm which relies on a limited number of pilot sequences to initialize the estimation process. We propose to include the estimation process within a turbo finite-alphabet simplicity (FAS)-based detection receiver. To that purpose we define two estimation updates from the FEC decoder output. Simulations carried out in both determined and undetermined configurations show that the resulting scheme outperforms the state-of-the-art receiver which uses an MMSE estimation criterion and that it reaches the maximum-likelihood lower-bound.

“…Among them, linear detectors such as Minimum Mean Square Error (MMSE), Sphere Decoder (SD)-based detectors, e.g. [1], the Layered Tabu Search Algorithm (LTSA) in [2], detectors based on the finite alphabet-sparse recovery approach [3] such as [4], and the simplicity-based detector [5], to just name few.…”

Section: Introductionmentioning

confidence: 99%

Improved sparse error recovery approach for detecting QAM signals in overloaded massive MIMO systems

Meslem

2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)

Djeddou

2020

With a convenient concatenation of a convex relaxation-based detector and a simple greedy algorithm, we propose an improved Post Detection Sparse error Recovery (PDSR) approach for massive Multiple Input Multiple Output (m-MIMO) systems that, in particular, transmit QAM signals. The proposed PDSR approach can perform well in situations, where the classical one, either acts poorly or completely fails. We further propose an Alternating Direction Method of Multipliers (ADMM)-based solver for the convex detector, which is advantageous in maintaining an affordable complexity to the overall proposed detection scheme. Numerical experiments show the efficiency of our approach, especially when applied to overloaded m-MIMO systems.