Efficient Channel-Adaptive MIMO Detection Using Just-Acceptable Error Rate

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.

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.

In this paper, a new signal detection scheme for overloaded multiple-input multiple output (MIMO) systems is proposed. In MIMO signal detection with belief propagation (BP), a factor graph consists of many loops that arise high complexity and cause performance degradation due to outliers. Various schemes have been proposed to overcome these issues. In BP with maximal ratio combining (MRC) and minimum mean square error (MMSE) pre-cancellation, a conventional scheme applies MRC on received signals in advance to obtain diversity gain to improve bit error rate (BER) performance as compared with BP using only MMSE pre-cancellation. To achieve further performance improvement, BP using a combined channel matrix is proposed in this paper. In the proposed scheme, the number of observation nodes is increased by combining a part of an original channel matrix to the channel matrix with MRC and MMSE pre-cancellation to enhance diversity gain. Numerical results obtained through computer simulation show that the BER performance of the proposed scheme is superior to that of BP or BP with MRC and MMSE pre-cancellation. The proposed scheme improves BER performance by about 3.1 dB at a BER of 10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> as compared with the BP withMRC and MMSE pre-cancellation. The computational complexity of the proposed scheme increases as the number of rows of the additional channel submatrix. It is in the same order of magnitude as that of the original BP based MIMO detection scheme.

In this paper, we address the ordering of transmit antennas according to reliability for unequal error protection (UEP) in spatially multiplexed (SM) multiple-input multiple-output (MIMO) systems with joint detection at the receiver. When zero-forcing (ZF) detection is adopted, the reliabilities of transmit antennas are explicitly expressed as postequalization signal-to-noise ratios (SNRs). Thus, multiantenna UEP can be implemented by assigning data of high priority to transmit antennas with a high postequalization SNR. Unfortunately, the overall error performance is generally unsatisfactory. Various joint signal detection techniques that achieve maximum likelihood (ML) or near-ML performance have been developed as alternatives to ZF, but it is not obvious how to discriminate the reliabilities of the transmit antennas or the jointly detected symbols. Recently, an ordering technique for antenna reliabilities was proposed that exploits the structure of the previous near-optimal QR-decomposition-based least-reliable-layer joint detection. In this paper, we divide the log-likelihood ratio of each symbol into collaborative and individual components, assuming joint ML detection. We derive a statistical connection between the magnitude order of each component of the multiple symbols (or the corresponding transmit antennas) and the post-ZF-equalization SNRs. Based on the statistical connections, we propose the use of post-ZF-equalization SNR as a criterion for antenna reliability ordering of SM MIMO with various near-optimal, as well as optimal, joint detections. The differentiated error performance of transmit antennas is also shown to be mainly attributed to the difference of individual components, and the tendency becomes stronger as the constellation size increases. Simulations demonstrate that the proposed ordering technique better meets the UEP requirement with lower computational complexity than the conventional ordering. Assuming $N_t \times N_t$ MIMO systems, the previous ordering and the proposed ordering require $\mathcal{O}(N_t^3)+\sum_{N=2}^{N_t-1}\mathcal{O}(NN_t^2+N^3)$ and $\mathcal{O}(N_t^3)$ , respectively.

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.

In this paper, we present the error probability expressions for a wideband coherent spatially correlated Multiple Input Multiple Output (MIMO) channel in low signal to noise ratio (SNR) regimes. Prior works have depicted a decrease in performance due to spatial correlation in high SNR regimes. This work proves that in a power limited broadband MIMO system, spatial correlation across the channel fading matrix elements improves the probability of error performance. In other words, spatial correlation impacts the MIMO performance differently in high SNR compared to low SNR (wideband) regimes.

In this paper, linear precoding (LP) algorithm has been studied to improve the bit error rate (BER) performance of large scale multiple input multiple output (MIMO) system. A new technique of enhancing the BER performance of linear precoding algorithm without complexity in hardware and mathematical calculations is proposed and it is used on a traditional LP algorithms like a Maximum Ratio Transmission (MRT), a Zero-Forcing (ZF), a Regularized Zero-Forcing (RZF), and a Minimum Mean Square Error (MMSE) for Millimeter wave MU-MIMO communication system. A new technique is also used to mitigate the BER gab problem between (ZF, MMSE) and (RZF, MMSE) linear precoding algorithms. The result shows that with using the new technique, the BER performance of modified LP significantly outperform their traditional LP by 10−5 when using 4 element of BS antenna, 10−6 when using 16 element of BS antenna and mitigate the BER gab problem at different signal to noise ratio (SNR).

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.

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

This paper proposes a new kind of satellite anti-interception communication system based on weighted-type fractional Fourier transform (WFRFT) and Multiple Input Multiple Output (MIMO) technology to improve the safety and anti-interception of satellite communication system. This system adopts multiple-layer WFRFT to modulate the signal. The number of WFRFT layers and the transmit antennas is the same. The modulation parameter of each WFRFT layer is different. After WFRFT modulation, the original signal has the characteristic of time and frequency domain, which can effectively resist the parameter scanning. MIMO can effectively improve the spectrum utilization and system capacity. By theoretical analysis, the WFRFT-MIMO communication system can be used to restore the original signal efficiently, and the eavesdropper can’t intercept the signal. Taking two transmit and one receive antennas for example, the results of the contrast of bit error rate (BER) performance between legal receiver and eavesdropper and relationship between receiving performance and WFRFT order deviation are simulated respectively, when the modulation order error of the eavesdropper is 0.1, and the signal-to-noise ratio is 20dB, the BER of eavesdropper is $10^{-3}$, which is more than the legal receiver 4dB. The performance between different receiving performance and transmitting antennas groups is simulated, when the signal to noise ratio (SNR) is 10dB, the performance of the three transmit four receive antennas is higher than that of the two transmit one receive antennas, which increases the 4. 5dB.

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

Wireless network is one of the most important things in the development of telecommunication. However, the existing wireless technology has not been able to efficiently create a very high data rate because it was very sensitive to fading. Therefore, Orthogonal Frequency Division Multiplexing (OFDM) technology combined with MIMO (Multiple Input Multiple Output) transceiver system was used to gain more diversity and bandwidth efficiency. The propagation performed on the OFDM MIMO system was multipath propagation. To reduce Intersymbol Interference (ISI) was used Zero Forcing (ZF) equalizer which works by combining channel response and equalizer response itself to eliminate ISI. This study aims to determine the effect of Zero Forcing Equalizer on OFDM MIMO system using rayleigh fading channel and compared the result with OFDM MIMO STBC system. The research method used was simulation using MatLab R2015a. The simulation results showed that the performance of OFDM MIMO ZF system was worse than OFDM MIMO STBC system i.e BER vs EbNo simulation, eye diagram simulation, and constellation diagram simulation. The OFDM MIMO ZF system was required an Eb / No value more than 25 dB to achieve BER 10-4 while the OFDM MIMO STBC system only required an Eb / No value of 10.5 dB to achieve BER 10-4. The eye pattern generated by the OFDM MIMO ZF system was more closed and the dispersion of constellation signals away from the ideal point while OFDM MIMO STBC system displayed a more open eye pattern and the dispersion of its constellation signal closer to the ideal point. It indicated more ISI occurs in the OFDM MIMO ZF system than that in OFDM MIMO STBC system.

Maximum-likelihood (ML) decoding is a very computational-intensive task for multiple-input multiple-output (MIMO) wireless channel detection. This paper presents a new graph based algorithm to achieve near ML performance for soft MIMO detection. Instead of using the traditional tree search based structure, we represent the search space of the MIMO signals with a directed graph and a greedy algorithm is applied to compute the a posteriori probability (APP) for each transmitted bit. The proposed detector has two advantages: 1) it keeps a fixed throughput and has a regular and parallel datapath structure which makes it amenable to high speed VLSI implementation, and 2) it attempts to maximize the a posteriori probability by making the locally optimum choice at each stage with the hope of finding the global minimum Euclidean distance for every transmitted bit. Compared to the soft K-best detector, the proposed solution significantly reduces the complexity because sorting is not required, while still maintaining good bit error rate (BER) performance. The proposed greedy detection algorithm has been designed and synthesized for a 4 by 4 16-QAM MIMO system in a TSMC 65 nm CMOS technology. The detector achieves a maximum throughput of 600 Mbps with a 0.79 square mm core area.

The Bit Error Rate (BER) performance of linear detection techniques namely, direct pseudo-inverse and Zero Forcing (ZF) deteriorates for massive Multiple Input and Multiple Output (mMIMO) system with increase in load factor and number of transmitting antennas that results in a significant performance difference in Average BER (ABER) between Minimum Mean Square Error (MMSE) and direct pseudo-inverse or ZF detection techniques. Therefore, Truncated Singular Value Decomposition (TSVD) is employed in direct pseudo-inverse and ZF detection algorithms to enhance the ABER performance of mMIMO systems over correlated Rician fading channel by employing dynamic threshold. Further, the singularity threshold equations (ɛthZF and ɛthdi) for TSVD-based direct pseudo-inverse and ZF detection techniques are obtained by analyzing their respective Post-Detection Signal-to-Interference-Noise Ratio (PDSINR) expressions. Moreover, the detection complexity of TSVD-based direct pseudo-inverse and ZF detection techniques is obtained as O(NT2 log(t)+NT t2) and O(NT2 N_R+NT2 log(t)+NT t2), respectively.

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.