Iterative Symbol Detection Research Articles

Restricted Search Space Exploration With Refinement for Symbol Detection in Uplink Massive MIMO

Massive multiple-input multiple-output (MIMO) is a promising technique to overcome the explosive increase in the demand for high-speed data, quality of service and energy efficiency for fifth generation (5 G) and beyond wireless systems. Low-complexity symbol detection in massive MIMO is one of the challenging issues for their practical realization. The existing approximate matrix inversion based and matrix inversion less iterative symbol detection techniques suffer significant performance loss when the number of users increases in the system. Therefore, this article proposes a low-complexity restricted search space exploration based ordered iterative detection (REOD) technique to achieve superior performance in large user massive MIMO systems. The proposed low-complexity REOD technique iteratively prunes the discrepancy term associated with the previous estimation and further explores an improved estimate confined to a predefined search space. Convergence of the proposed REOD technique is analytically justified. An analytical expression for the approximate upper bound on the bit error rate is derived and corroborated by simulations. The obtained results prove the viability of the proposed REOD technique compared to the several existing state-of-the-art uplink massive MIMO detection techniques, both in terms of error performance and computational complexity.

Deep Learning Based Symbol Detection With Natural Redundancy for Non-Uniform Memoryless Sources

In this letter, we consider improving the performance of symbol detection by utilizing the natural redundancy (NR) that widely exists in the transmission sources. As an initial study, the NR considered in this letter is the redundancy inherent in the data caused by the non-uniform distribution of the memoryless transmission sources. We first theoretically analyze the utilization of NR to improve the performance of symbol detection from the perspective of information theory, and then propose a deep learning (DL) based iterative symbol detection algorithm with the source’s prior distribution unknown. Simulation results demonstrate that the performance of symbol detection can be improved by utilizing NR and our proposed DL-based iterative symbol detection algorithm can obtain the theoretically optimal performance even when the source distribution is blind to the detector.

Deep Unfolded Sparse Refinement Network Based Detection in Uplink Massive MIMO

Massive multiple-input multiple-output (mMIMO) is a promising technique to realize the ever-increasing demand for high-speed data, quality of service (QoS), and energy efficiency for 5G and beyond wireless systems. However, the increased number of users in mMIMO systems significantly affects performance of the existing approximate matrix inversion based and matrix inversion less iterative symbol detection techniques. Conventional detection algorithms cannot learn the inter-relations of input-output parameters based on available data without having specific mathematical models of communication scenarios. Moreover, existing deep learning (DL) based symbol detection models lack in-network compression, resulting in large training time and high computational load while expected to be deployed in a low latency communication system. In this article, a sparse refinement architecture is proposed for symbol detection in uplink mMIMO. The proposed DL architecture requires less trainable parameters as compared to a conventional fully connected detection network and refines the estimated symbol vector in each layer. Convergence of the proposed symbol detection technique is analytically justified. An expression for the approximate upper bound on the BER is derived which is supported by simulations. The obtained results prove viability of the proposed symbol detection model as compared to the several existing state-of-art uplink mMIMO detection techniques, in terms of superior the error performance and low computational complexity.

A PSSS Approach for Wireless Industrial Communication Applying Iterative Symbol Detection

Wireless industrial communication requires high reliability and low latencies. In order to fulfill both requirements simultaneously, wireless approaches are being developed. One of these technologies is parallel sequence spread spectrum (PSSS). In comparison to direct sequence spread spectrum approaches, which employ a set of appropriate pseudonoise (PN) sequences, PSSS data symbols are spread based on a single cyclically shifted m -sequence. Each cyclic shift of the chosen m -sequence is used as an individual PN-sequence. This enables a code division multiple access (CDMA) scheme with simple processing and thus, a fast hardware implementation. In order to investigate the impact of an inaccurate time synchronization, this paper presents a theoretical and simulative PSSS evaluation. Moreover, an iterative symbol detection is introduced, which neutralizes the impact of the nonorthogonal m -sequences and mitigates the degradation due to inaccurate synchronization. In addition, several reasonable resource allocation schemes are presented, which emphasize the PSSS systems flexibility.

Data Detection in Cooperative STBC-OFDM Systems With Multiple Frequency Offsets

This paper addresses the problem of data detection in cooperative space-time block coded (STBC) orthogonal frequency division multiplexing (OFDM) systems in the presence of multiple carrier frequency offsets (CFO). An enhanced iterative maximum-likelihood detector (EIMLD) is proposed. This method consists of first removing inter-carrier interference (ICI), and then performing iterative symbol detection and inter-symbol interference reduction. Simulation results show that EIMLD significantly outperforms existing iterative methods. Comparisons with the zero-forcing and minimum-mean square error detectors, which require complex matrix inversion, are also carried out.

Double Window Cancellation and Combining for OFDM in Time-Invariant Large Delay Spread Channel

In a time-invariant wireless channel, the multipath that exceeds the cyclic prefix (CP) or the guard interval (GI) causes orthogonal frequency division multiplexing (OFDM) systems to hardly achieve high data rate transmission due to the inter-symbol interference (ISI) and the inter-carrier interference (ICI). In this paper the new canceller scheme, named as Double Window Cancellation and Combining (DWCC) is proposed. It includes the entire symbol interval, delayed by multipath as a signal processing window and intends to improve the performance by combining the double windows that can be formed by the pre-and post-ISI cancellation and reconstruction to the received OFDM symbol interfered by the multipath exceeding the guard interval. The proposed scheme has two algorithm structures of the DWCC-I and -II which are distinguished by the operational sequence (Symbol-wise or Group-wise) to the OFDM symbols of the received packet and by the selection of the processing window in the iterative decision feedback processing. Since the performance of the canceller is dependant on the equalization, particularly on the initial equalization, the proposed schemes operate with the time and frequency domain equalizer in the initial and the iterative symbol detection, respectively. For the verification of the proposed schemes, each scheme is evaluated in the turbo coded OFDM for low (QPSK) and high level modulation systems (16QAM, 64QAM), and compared with the conventional canceller with respect to the performance and computational complexity. As a result, the proposed schemes do not have an error floor even for 64QAM in a severe frequency selective channel.