In this paper, we consider the problem of finite-alphabet source separation in both determined and underdetermined large-scale systems. First, we address the noiseless case and we propose a linear criterion based on 1 -minimization combined with box constraints. We investigate also the system conditions that ensure successful recovery. Next, we apply the approach to the noisy massive MIMO transmission and we propose a quadratic criterion-based detector. Simulation results show the efficiency of the proposed detection methods for various QAM modulations and MIMO configurations. We mention that there is no change in the computational complexity when the constellation size increases. Moreover, the proposed method outperforms the classical Minimum Mean Square Error (MMSE)-based detection algorithms.
Abstract-This paper focuses on low-complexity detection for large scale multiple-input multiple-output (MIMO) systems involving tens to hundreds of transmit/receive antennas. Due to the exponential increase of its processing complexity with the data signal dimensions (antenna number, modulation order), a maximum likelihood detection is infeasible in practice. To overcome this drawback, authors in [1] proposed a lowcomplexity detection based on a sparse decomposition of the information vector. It is proved that this decomposition is mainly adpated to underdetermined systems and leads to a significant reduction on the processing complexity. As an extension to the work investigated in [1], we propose in this paper a new decomposition that makes the computation cost less dependent on the modulation alphabet cardinality, thus reducing theoretically the complexity by 50% for 4-QAM and by 72% for 16-QAM compared to the previous detector in [1], while achieving the same error rate performance.
In this paper, we consider large-scale MIMO systems and we define iterative receivers which use the simplicity-based detection algorithm referred to as Finite Alphabet Simplicity (FAS) algorithm. First, we focus on uncoded systems and we propose a novel successive interference cancellation algorithm with an iterative processing based on the shadow area principle and we optimize its parameters by exploiting the theoretical analysis of the detector output. Secondly, we assume FEC-encoded systems and we propose an iterative receiver based on a maximum likelihood-like detection with restricted candidate subset defined by the FAS algorithm output. We also introduce another receiver based on FAS detection whose criterion is penalized with the mean absolute error function. Simulations results show the efficiency of all proposed iterative receivers compared to the state-of-the-art methods.
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.
In this paper, we consider large-scale MIMO systems and we address the channel estimation problem. We propose an iterative receiver consisting of the cascade of a semi-blind leastsquares channel estimation algorithm with a simplicity-based detection algorithm for finite-alphabet signals (FAS and FAS-SAC). A minimum number of pilot sequences is used to get an initial channel estimation. The detection algorithm outputs are then used to refine it gradually. Two feeding methods are studied. The first one uses raw detection outputs. The second one is based on hard decisions and enables better performance. Theoretical MSEs are calculated in both cases. Simulations assess the efficiency of the proposed iterative procedure compared to the state-of-the-art methods and show that it performs very close to the ideal scenario where all the communication frame sequences are known.
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.
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