Iterative Soft Detection Research Articles

Deep Learning-Based Soft Iterative-Detection of Channel-Coded Compressed Sensing-Aided Multi-Dimensional Index Modulation

The concept of Index Modulation (IM) has been actively researched as a benefit of its flexible trade-off between performance, achievable rate, energy efficiency, hardware cost and complexity. In order to fully exploit its the degrees of freedom, the concept of Multi-dimensional IM (MIM) has been developed in literature, where Compressed Sensing (CS) is often utilized to exploit the sparsity of the multi-dimensional transmitted signals. However, this flexibility and performance gains are attained at the cost of a substantially increased detection complexity. In this paper, we propose Deep Learning (DL) based detection for CS-aided MIM (CS-MIM), where both Hard-Decision (HD) as well as Soft-Decision (SD) detection combined with iterative decoding are conceived. More explicitly, firstly, we propose learning aided hard and soft detection for CS-MIM. Secondly, two novel neural network aided methods are proposed for Iterative Soft Detection (ISD), where iterations are carried out between the CS-MIM detector and a channel decoder. In contrast to the conventional detection of CS-MIM system, which critically relies on the knowledge of Channel State Information (CSI) at the receiver, the proposed learning-aided methods are capable of eliminating the overhead and complexity of Channel Estimation (CE), which results in an improved transmission rate. Explicitly, we develop an advanced DL architecture for blind-detection-aided MIM for the first time in the open literature, where the HD and SD CS algorithms are implemented by learning without the need for CE. Our simulation results demonstrate that the proposed learning techniques conceived for SD CS-MIM combined with iterative detection are capable of achieving near-capacity performance at a reduced complexity compared to the conventional model-based SD relying on CSI acquisition.

Evaluation of MMSE-Based Iterative Soft Detection Schemes for Coded Massive MIMO System

There have been a number of researches on soft iterative detection and decoding scheme for coded multiple-input-multiple-output (MIMO) systems. Minimum mean square error (MMSE)-based detection schemes were often considered for massive MIMO systems, due to their reasonable complexity and performance tradeoff. This paper evaluates a number of MMSE-based soft iterative detection schemes for massive MIMO systems, with new proposals to improve the performances and to reduce the complexity. We consider joint iterative detection and decoding schemes for a coded massive MIMO system, and various kinds of MMSE-based soft detection methods proposed in the literatures are investigated. By utilizing the diagonal approximation of the channel matrix, we propose efficient soft MMSE filtering methods in combination with soft interference cancellation techniques and a compact soft bit estimation method. In addition, new matrix inversion-less soft MMSE methods are proposed for joint iterative detections. The simulation results demonstrate that the proposed methods clearly contribute to the reduction of the complexity of the conventional methods, and performance enhancement.

반복 MIMO 검출을 위한 저 복잡도 LSD 기법

논문에서는 부호화된 MIMO 시스템에서 연판정 반복 검출 방식과 결합하여 효율적으로 사용될 수 있는 복잡도가 감소된 리스트 구 복호 (list sphere decoding; LSD) 방식을 제안한다. 기존의 LSD 방식은 고정된 값의 반지름을 이용하여 후보 신호들을 검색하였다. 그러나, 정해진 수 만큼의 후보 리스트들이 모두 채워지면, 이들에 대한 최대 또는 평균 반지름은 줄어들 가능성이 매우 크다. 반지름을 줄이게 되면 복잡도 또한 줄일 수 있게 된다. 본 논문에서는 LSD 기반의 MIMO 검출 시스템에서 효율적으로 사용될 수 있는 매우 간단한 반지름 갱신 방식 및 이를 이용한 연판정 반복 MIMO 검출 기법을 제안한다. 또한 터보 부호를 이용하여, 연판정 입출력을 이용하는 반복 MIMO 검출 방식에 대한 성능 평가 결과를 제시한다. 본 논문에서 제시된 시뮬레이션 결과에 따르면, 제안된 방식은 기존의 LSD 방식을 사용하는 것에 비해 거의 유사한 비트오류율 성능을 유지하면서도 복잡도를 크게 감소시킬 수 있다. This paper proposes a complexity reduced list sphere decoding (LSD) scheme for joint iterative soft detection scheme for coded MIMO system. The conventional LSD scheme is based on searching the candidates with a fixed radius. However, once the candidate list is full, it is highly probable that the radius can be reduced. By reducing the radius, the complexity can be also reduced. We propose a simple and efficient radius update method for complexity reduction of list version sphere decoding and its application to iterative soft MIMO detection. We evaluate the performance of the proposed scheme with a joint soft-input-soft-ouput iterative MIMO detection in combination with turbo codes. Simulation results show that the proposed methods provide substantial complexity reduction while achieving similar bit error rate (BER) performance as the conventional LSD scheme.

Probabilistically bounded soft sphere detection for MIMO-OFDM receivers: algorithm and system architecture

Iterative soft detection and channel decoding for MIMO OFDM downlink receivers is studied in this work. Proposed inner soft sphere detection employs a variable upper bound for number of candidates per transmit antenna and utilizes the breath-first candidate-search algorithm. Upper bounds are based on probability distribution of the number of candidates found inside the spherical region formed around the received symbolvector. Detection accuracy of unbounded breadth-first candidatesearch is preserved while significant reduction of the search latency and area cost is achieved. This probabilistically bounded candidate-search algorithm improves error-rate performance of non-probabilistically bounded soft sphere detection algorithms, while providing smaller detection latency with same hardware resources. Prototype architecture of soft sphere detector is synthesized on Xilinx FPGA and for an ASIC design. Using area-cost of a single soft sphere detector, a level of processing parallelism required to achieve targeted high data rates for future wireless systems (for example, 1 Gbps data rate) is determined.

Iterative soft-QRD-M for turbo coded MIMO-OFDM systems

We propose an iterative soft detection algorithm based on the QR decomposition and M-algorithm (soft-QRD- M) for MIMO-OFDM incorporating error correction coding. The soft-QRD-M step generates approximate a posteriori probabilities (APPs) with significant computational savings over the optimal sum-product algorithm. Simulation results show comparable performance of the soft-QRD-M detector and SPA in a Turbo- coded iterative MIMO-OFDM receiver.