An efficient MIMO detection algorithm employed in imperfect noise estimation

This paper investigates the performance of IEEE 802.11ac physical layer (PHY) when the following multiple input multiple output (MIMO) detectors are implemented: Sphere Decoding (SD); QRM-Maximum Likelihood (QRM-MLD); List Minimum Mean Squared Error Successive Interference Cancellation (List MMSE-SIC); List Zero-Forcing SIC (List ZF-SIC); Lattice Reduction MMSE-SIC (LR MMSE-SIC) and List LR MMSE-SIC. The conclusions are founded on simulation results, validated using first order techniques, over TGac channel models. The IEEE 802.11ac simulator implements hard-decision Viterbi decoding and realistic synchronization and channel estimation algorithms. The List MMSE-SIC scheme presents power gains around 2 dB in relation to the List ZF-SIC MIMO detector. The QRM-MLD MIMO detector has a highly superior performance regarding to the List MMSE-SIC MIMO detector since this latter scheme cannot overcome the propagation errors that degrade the performance of MMSE-SIC MIMO detectors. However, for moderate list lengths of 4 and 8, the QRM-MLD MIMO detector presents severe power losses in relation to the optimal SD MIMO detector. The LR MMSE-SIC MIMO detector allows power losses of only 1–2 dB in reference to the optimal SD MIMO receiver. There are no noticeable power gains when List detection techniques in MMSE-SIC MIMO detectors are implemented since the diversity gains due to implementation of LR basis change overcome the error propagation in SIC MIMO detectors with original channel basis.

Since H. Yao proposed the lattice reduction (LR)-aided detection algorithm for the MIMO detector, one can exploit the diversity gain provided by the LR method to achieve performance comparable to the maximum likelihood (ML) algorithm but with complexity close to the simple linear detection algorithms such as zero forcing (ZF), minimum mean squared error, and successive interference cancellation, etc. In this paper, in order to reduce the processing time of the LR-aided detector, a graphics processing unit (GPU) has been proposed as the main modem processor in such a way that the detections can be performed in parallel using multiple threads in the GPU. A 2X2 multiple input multiple output (MIMO) WiMAX system has been implemented using a GPU to verify that various MIMO detection algorithms such as ZF, ML, and LR-aided methods can be processed in real-time. From the experimental results, we show that GPUs can realize a 2X2 WiMAX MIMO system adopting an LR-aided detector in real-time. We achieve a processing time of 2.75 ms which meets the downlink duration specification of 3 ms. BER performance of experimental tests also indicates that the LR-aided MIMO detector can fully exploit diversity gain as well as ML detector.

A near maximum likelihood performance modified firefly algorithm for large MIMO detection

Multiple-input multiple-output (MIMO) detection algorithms have received considerable research interest in recent years, as a result of the increasing need for high data-rate communications. Detection techniques range from the low-complexity linear detectors to the maximum likelihood detector, which scales exponentially with the number of transmit antennas. In between these two extremes are the tree search (TS) algorithms, such as the popular sphere decoder, which have emerged as attractive choices for implementing MIMO detection, due to their excellent performance-complexity trade-offs. In this paper, we survey some of the state-of-the-art VLSI implementations of TS algorithms and compare their results using various metrics such as the throughput and power consumption. We also present notable contributions that have been made in the last three decades in implementing TS algorithms for MIMO detection, especially with respect to achieving low-complexity, high-throughput designs. Finally, a number of design considerations and trade-offs for implementing MIMO detectors in hardware are presented.

Multiple input multiple output (MIMO) communication systems have emerged as a key technol-ogy to enhance spectral efficiency and reliability in wireless communications. In recent years, deep neural network (DNN)-based approaches have shown promise in addressing the challenges of MIMO signal detection. Among these approaches, the Transformer architecture, known for its effectiveness in capturing long-range dependencies in sequential data, has gained significant attention. Therefore, this paper proposes a revolutionary DNN-based MIMO signal detection scheme using the Transformer-based architecture. This novel scheme leverages the multi-head self-attention mechanism inherent in Transformer architectures, which enables the model to capture both spatial and temporal dependencies in MIMO channels, thereby improving symbol detection accuracy and robustness under varying channel conditions. The proposed scheme's bit error rate (BER) performance is compared with traditional methods through simulations. The results show that the proposed method achieves a signal-to-noise ratio (SNR) gain of nearly 1.5 ​dB against the traditional detection methods, with the optimal maximum likelihood detector (MLD) only outperforming it ​by ​< ​0.5 ​dB. • Transformer-based MIMO signal detection. • Deep learning for wireless communications. • Open access journals on signal processing. • Next-generation wireless communication systems.

After a few decades’ evolution of wireless communication systems, to ensure reliable highspeed communication over unreliable wireless channels is still one of the major challenges facing researchers and engineers. The use of multiple antennas at transmitter and receiver, known as multiple-input multiple-output (MIMO) communications, is one promising technology delivering desired wireless services. The main goal of this thesis is to study two important issues in wireless MIMO communication systems: receiver design for coded MIMO systems, and diversity-multiplexing tradeoff analysis in general fading channels. In the first part of this thesis, we decompose the receiver design problem into two subproblems: MIMO channel estimation and MIMO detection. For the MIMO channel estimation, we develop an expectation-maximization (EM) based semi-blind channel and noise covariance matrix estimation algorithm for space-time coding systems under spatially correlated noise. By incorporating the proposed channel estimator into the iterative receiver structure, both the channel estimation and the error-control decoding are improved significantly. We also derive the modified Cramer-Rao bounds (MCRB) for the unknown parameters as the channel estimation performance metric, and demonstrate that the proposed channel estimation algorithm can achieve the MCRB after several iterations. For the MIMO detection, we propose a novel lowcomplexity MIMO detection algorithm, which has only cubic order computational complexity, but with near-optimal performance. For a 4×4 turbo-coded system, we show that the proposed detector had the same performance as the maximum a posteriori (MAP) detector for BPSK modulation, and 0.1 dB advantage over the approximated MAP detector (list sphere decoding algorithm) for 16-QAM modulation at BER = 10−4. In the second part of this thesis, we derive the optimal diversity-multiplexing tradeoff for

Design and implementation of a highly optimized MIMO (multiple-input multiple-output) detector requires cooptimization of the algorithm with the underlying hardware architecture. Special attention must be paid to application requirements such as throughput, latency, and resource constraints. In this work, we focus on a highly optimized matrix inversion free 4×4 MMSE (minimum mean square error) MIMO detector implementation. The work has resulted in a real-time field-programmable gate array-based implementation (FPGA-) on a Xilinx Virtex-2 6000 using only 9003 logic slices, 66 multipliers, and 24 Block RAMs (less than 33% of the overall resources of this part). The design delivers over 420 Mbps sustained throughput with a small 2.77-microsecond latency. The designed 4×4 linear MMSE MIMO detector is capable of complying with the proposed IEEE 802.11n standard.

Multiple-input multiple-output (MIMO) significantly increases the throughput of a communication system by employing multiple antennas at the transmitter and the receiver. To extract maximum performance from a MIMO system, a computationally intensive search based detector is needed. To meet the challenge of MIMO detection, typical suboptimal MIMO detectors are ASIC or FPGA designs. We aim to show that a MIMO detector on Graphic processor unit (GPU), a low-cost parallel programmable co-processor, can achieve high throughput and can serve as an alternative to ASIC/FPGA designs. However, careful architecture aware software design is needed to leverage the performance offered by GPU. We propose a novel soft MIMO detection algorithm, multi-pass trellis traversal (MTT), and show that we can achieve ASIC/FPGA-like performance and handle different configurations in software on GPU. The proposed design can be used to accelerate wireless physical layer simulations and to offload MIMO detection processing in wireless testbed platforms.

This paper considers the design of a practical low-density parity check (LDPC)-coded multiple-input multiple-output (MIMO) system composed of M transmit and N receive antennas operating in a flat-fading environment where channel state information (CSI) is assumed to be unavailable both to the transmitter and the receiver. A soft iterative receiver structure is developed, which consists of three main blocks: a soft MIMO detector and two LDPC component soft decoders. We first propose at the component level several soft-input soft-output MIMO detectors whose performances are much better than the conventional minimal mean square error (MMSE)-based detectors. In particular, one optimal soft MIMO detector and two simplified suboptimal detectors are developed that do not require an explicit channel estimate and offer an effective tradeoff between complexity and performance. In addition, a modified expectation maximization (EM)-based MIMO detector is developed that completely removes positive feedback between input and output extrinsic information and provides much better performance compared with the direct EM-based detector that has strong correlations especially in fast-fading channels. At the structural level, the LDPC-coded MIMO receiver is constructed in an unconventional manner where the soft MIMO detector and LDPC variable node decoder form one super soft-decoding unit, and the LDPC check node decoder forms the other component of the iterative decoding scheme. By exploiting the proposed receiver structure, tractable extrinsic information transfer functions of the component soft decoders are obtained, which further lead to a simple and efficient LDPC code degree profile optimization algorithm with proven global optimality and guaranteed convergence from any initialization. Finally, numerical and simulation results are provided to confirm the advantages of the proposed design approach for the coded MIMO system.

Based on adaptive theory, an adaptive modulation and coding (AMC) multiple input multiple output (MIMO) system with adaptive Turbo receiver is designed to increase the overall capacity and decrease the processing delay. The scheme sets several factors including channel coding rate, modulation mode and iteration times of Turbo receiver as adjustable elements. To guarantee the superiority of the proposed adaptive Turbo receiver in terms of decreasing processing delay and power consuming, an MMSE based no-ordered parallel soft interference cancellation (NOPSIC) MIMO detector which takes error propagation into account is explored for the non-first iteration. Simulation results reveal that the proposed scheme could significantly raise the system capacity, meanwhile bring down overall times of iteration processing by nearly one third.

Multiple-input multiple-output (MIMO) technology has been widely used in modern communication system for its promising advantages. It has been proved that in a rich scattering environment, a MIMO system processes more capacity and better spectrum utilization. On the other hand, as signals in MIMO system share the same space and time, the receivers is more complex. Thus, in order to promote the development of MIMO related theories and applications, the research on detection problem is of significance. In this paper, some widely used detection algorithms have been discussed to present a basic understanding and conclusion.

Multiple-input multiple-output (MIMO) communication is an important technique to increase the transmission capacity, but the increased antenna number does not necessarily increase the throughput because of more antenna interference and decoding complexity. An iterative detector-and-decoder (IDD) can effectively improve transmission performance by exchanging reliability information such as log likelihood ratio between a MIMO detector and a error-correction-code decoder, but the IDD reduces the throughput because of the iteration loop. Therefore, this paper proposes a lattice-reduction-aided (LRA) soft-output K-best detector to eliminate the iteration loop and devises an effective method to calculate the reliable information. The proposed 8 × 8 LRA soft-output K-best detector achieves a better performance than the 8 × 8 MIMO detectors in the literature. The proposed detector was designed and implemented using TSMC 90nm 1P9M CMOS process. The post-layout results show that the detector chip achieves a throughput of 6.4G LLRs/sec at its maximum frequency of 133.3 MHz with 64-QAM modulation.

This paper proposes a new concept of multiple-input multiple-output (MIMO) detection, aiming at minimizing the average computational cost. The detection methods are adapted according to the estimated channel state information to deliver just-acceptable error rate (JAER) performance. Error rate models for two popular MIMO detection algorithms are derived given the channel matrix. From these models, a channel-adaptive-MIMO (CA-MIMO) receiver with detector-switching strategies is proposed. Simulation results demonstrate that the proposed CA-MIMO detector meets the JAER criterion efficiently. Compared with the sophisticated Sphere Search (SS) MIMO detector, the average saving at moderate signal-to-noise ratio (SNR) is around 58% to 72%, depending on different modulation alphabets. At high SNR, the CA-MIMO detector almost always switch to Zero-Forcing (ZF) detection, where the complexity is several orders lower than the SS detector.

The implementation of Multiple Input Multiple Output (MIMO) on the Sparse Code Multiple Access (SCMA) can enhance performance in 5G future communication. Low complexity detector for the MIMO-SCMA system to achieve near-optimum bit error rate (BER) performance is still hot topic in this research field. This paper is investigate low complexity MIMO-SCMA detector by combining a joint factor graph (JFG) and sphere decoding (SD) algorithm based on message passing algorithm (MPA) called by SD-JFG-MPA. SD-JFG-MPA is combine n-th single factor graph become one virtual factor graph to deal with MIMO detector. SD algorithm is used to reduce high complexity of the JFG-MPA by calculating possible combination codeword within search radius R SD . SD-JFG-MPA is investigated under Rayleigh channel model with overloading factor 150%. The simulation result shows that complexity and BER performance is determined by a selected radius of SD-JFG-MPA. Optimum performance with less processing time for one bit error on single SCMA when $R_{SD} = \sqrt {E_b} \times 3/2 \times r_{max}$ meanwhile for 2x2 MIMO-SCMA when $R_{SD} = \sqrt {E_b} \times N_{mimo} \times r_{max}$. 2×2 MIMO-SCMA performance is better with 5 dB gap at BER = 10−3 compared to single SCMA.

Sphere Decoder (SD) is widely being used in Multiple Input Multiple Output (MIMO) systems to reduce the complexity of the system while obtaining near Maximum Likelihood (ML) performance. The complexity of the system increases with the increase in antenna configuration or the constellation size. Some pre-processing is a fundamental prerequisite in iterative detectors to reduce the system complexity by focusing the received signal energy to reduce the effect of inter-symbol interference. The QR Decomposition (QRD) of communication channel matrices in the pre-processor stage is an important issue to ensure good performance of the subsequent steps of decoding thus a QRD) is commonly used in many MIMO detection algorithms. A sorted QR decomposition (SQRD) is an advanced algorithm that improves the performance of MIMO detection. In this paper the efficiency of QRD and SQRD methods in terms of computational complexity, error rate performance and the FPGA resources utilized is presented. The main contribution of this work is a comparison of hardware implementations of the QRD and SQRD system. QRD for 4x4 MIMO system is implemented on various target FPGA platforms to compare their area utilization.