This paper proposes a scheme for reducing pilot interference in cell-free massive multiple-input multiple-output (MIMO) systems through scalable access point (AP) selection and efficient pilot allocation using the Grey Wolf Optimizer (GWO). Specifically, we introduce a bidirectional large-scale fading-based (B-LSFB) AP selection method that builds high-quality connections benefiting both APs and UEs. Then, we limit the number of UEs that each AP can serve and encourage competition among UEs to improve the scalability of this approach. Additionally, we propose a grey wolf optimization based pilot allocation (GWOPA) scheme to minimize pilot contamination. Specifically, we first define a fitness function to quantify the level of pilot interference between UEs, and then construct dynamic interference relationships between any UE and its serving AP sets using a weighted fitness function to minimize pilot interference. The simulation results shows that the B-LSFB strategy achieves scalability with performance similar to large-scale fading-based (LSFB) AP selection. Furthermore, the grey wolf optimization-based pilot allocation scheme significantly improves per-user net throughput with low complexity compared to four existing schemes.

With the development of drone technology, concerns have arisen about the possibility of drones being equipped with threat payloads for terrorism and other crimes. A drone detection system that can detect drones carrying payloads is needed. A drone’s propeller rotation frequency increases with payload weight. Therefore, a method for estimating propeller rotation frequency will effectively detect the presence or absence of a payload and its weight. In this paper, we propose a method for classifying the payload weight of a drone by estimating its propeller rotation frequency from radar images obtained using a millimeter-wave fast-chirp-modulation multiple-input and multiple-output (MIMO) radar. For each drone model, the proposed method requires a pre-prepared reference dataset that establishes the relationships between the payload weight and propeller rotation frequency. Two experimental measurement cases were conducted to investigate the effectiveness of our proposal. In case 1, we assessed four drones (DJI Matrice 600, DJI Phantom 3, DJI Mavic Pro, and DJI Mavic Mini) to determine whether the propeller rotation frequency of any drone could be correctly estimated. In case 2, experiments were conducted on a hovering Phantom 3 drone with several payloads in a stable position for calculating the accuracy of the payload weight classification. The experimental results indicated that the proposed method could estimate the propeller rotation frequency of any drone and classify payloads in a 250 g step with high accuracy.

The very high path loss caused by molecular absorption becomes the biggest problem in Terahertz (THz) wireless communications. Recently, the multi-band ultra-massive multi-input multi-output (UM-MIMO) system has been proposed to overcome the distance problem. In UM-MIMO systems, the impact of mutual coupling among antennas on the system performance is unable to be ignored because of the dense array. In this letter, a channel model of UM-MIMO communication system is developed which considers coupling effect. The effect of mutual coupling in the subarray on the functionality of the system has been investigated through simulation studies, and reliable results have been derived.

The combination of multiple-input multiple-output (MIMO) technology and sparse code multiple access (SCMA) can significantly enhance the spectral efficiency of future wireless communication networks. However, the receiver design for downlink MIMO-SCMA systems faces challenges in developing multi-user detection (MUD) schemes that achieve both low latency and low bit error rate (BER). The separated detection scheme in the MIMO-SCMA system involves performing MIMO detection first to obtain estimated signals, followed by SCMA decoding. We propose an enhanced separated detection scheme based on lightweight graph neural networks (GNNs). In this scheme, we raise the concept of coordinate point relay and full-category training, which allow for the substitution of the conventional message passing algorithm (MPA) in SCMA decoding with image classification techniques based on deep learning (DL). The features of the images used for training encompass crucial information such as the amplitude and phase of estimated signals, as well as channel characteristics they have encountered. Furthermore, various types of images demonstrate distinct directional trends, contributing additional features that enhance the precision of classification by GNNs. Simulation results demonstrate that the enhanced separated detection scheme outperforms existing separated and joint detection schemes in terms of computational complexity, while having a better BER performance than the joint detection schemes at high Eb/N0 (energy per bit to noise power spectral density ratio) values.

This paper proposes the periodical successive over-relaxation (PSOR)-Jacobi algorithm for minimum mean squared error (MMSE) detection of multiple-input multiple-output (MIMO) signals. The proposed algorithm has the advantages of two conventional methods. One is the Jacobi method, which is an iterative method for solving linear equations and is suitable for parallel implementation. The Jacobi method is thus a promising candidate for high-speed simultaneous linear equation solvers for the MMSE detector. The other is the Chebyshev PSOR method, which has recently been shown to accelerate the convergence speed of linear fixed-point iterations. We compare the convergence performance of the PSOR-Jacobi algorithm with that of conventional algorithms via computer simulation. The results show that the PSOR-Jacobi algorithm achieves faster convergence without increasing computational complexity, and higher detection performance for a fixed number of iterations. This paper also proposes an efficient computation method of inverse matrices using the PSOR-Jacobi algorithm. The results of computer simulation show that the PSOR-Jacobi algorithm also accelerates the computation of inverse matrix.

This paper proposes a non-linear overloaded MIMO detector that outperforms the conventional soft-input maximum likelihood detector (MLD) with less computational complexity. We propose iterative log-likelihood ratio (LLR) estimation and multi stage LLR estimation for the proposed detector to achieve such superior performance. While the iterative LLR estimation achieves better BER performance, the multi stage LLR estimation makes the detector less complex than the conventional soft-input maximum likelihood detector (MLD). The computer simulation reveals that the proposed detector achieves about 0.6dB better BER performance than the soft-input MLD with about half of the soft-input MLD's complexity in a 6×3 overloaded MIMO OFDM system.

In this paper, we investigate optimal design of millimeter-wave (mmWave) multiuser line-of-sight multiple-input-multiple-output (LOS MIMO) systems using hybrid arrays of subarrays based on hybrid block diagonalization (BD) precoding and combining scheme. By introducing a general 3D geometric channel model, the optimal subarray separation products of the transmitter and receiver for maximizing sum-rate is designed in terms of two regular configurations of adjacent subarrays and interleaved subarrays for different users, respectively. We analyze the sensitivity of the optimal design parameters on performance in terms of a deviation factor, and derive expressions for the eigenvalues of the multiuser equivalent LOS MIMO channel matrix, which are also valid for non-optimal design. Simulation results show that the interleaved subarrays can support longer distance communication than the adjacent subarrays given the appropriate fixed subarray deployment.

Analog beamforming with broadband large-scale antenna arrays in millimeter wave (mmWave) multiple input multiple output (MIMO) systems faces the beam squint problem. In this paper, we first investigate the sensitivity of analog beamforming to subarray spatial separations in wideband mmWave systems using hybrid arrays, and propose optimized subarray separations. We then design improved analog beamforming after phase compensation based on Zadoff-Chu (ZC) sequence to flatten the frequency response of radio frequency (RF) equivalent channel. Considering a single-carrier frequency-domain equalization (SC-FDE) scheme at the receiver, we derive low-complexity linear zero-forcing (ZF) and minimum mean squared error (MMSE) equalizers in terms of output signal-to-noise ratio (SNR) after equalization. Simulation results show that the improved analog beamforming can effectively remove frequency-selective deep fading caused by beam squint, and achieve better bit-error-rate performance compared with the conventional analog beamforming.

This study investigates the improvement of the channel capacity of 5-GHz-band multiple-input multiple-output (MIMO) communication using microwave-guided modes propagating along a polyvinyl chloride (PVC) pipe wall for a buried pipe inspection robot. We design a planar Yagi-Uda antenna to reduce transmission losses in communication with PVC pipe walls as propagation paths. Coupling efficiency between the antenna and a PVC pipe is improved by attaching a PVC adapter with the same curvature as the PVC pipe's inner wall to the Yagi-Uda antenna to eliminate any gap between the antenna and the inner wall of the PVC pipe. The use of a planar Yagi-Uda antenna with a PVC adaptor decreases the transmission loss of a 5-GHz-band microwave signal propagating along a 1-m-lomg straight PVC pipe wall by 7dB compared to a dipole antenna. The channel capacity of a 2×2 MIMO system using planar Yagi-Uda antennas is more than twice that of the system using dipole antennas.

In this paper, belief propagation (BP) multi-input multi-output (MIMO) detection with maximum ratio combining (MRC) and minimum mean square error (MMSE) pre-cancellation is proposed for overload MIMO. The proposed scheme applies MRC before MMSE pre-cancellation. The BP MIMO detection with MMSE pre-cancellation leads to a reduction in diversity gain due to the decreased number of connections between variable nodes and observation nodes in a factor graph. MRC increases the diversity gain and contributes to improve bit error rate (BER) performance. Numerical results obtained through computer simulation show that the BERs of the proposed BP MIMO detection with MRC and MMSE pre-cancellation yields bit error rates (BERs) that are approximately 0.5dB better than those of conventional BP MIMO detection with MMSE pre-cancellation at a BER of 10-3.

This letter provides an efficient massive multiple-input multiple-output (MIMO) detector based on quasi-newton methods to speed up the convergence performance under realistic scenarios, such as high user load and spatially correlated channels. The proposed method leverages the information of the Hessian matrix by merging Barzilai-Borwein method and Limited Memory-BFGS method. In addition, an efficient initial solution based on constellation mapping is proposed. The simulation results demonstrate that the proposed method diminishes performance loss to 0.7dB at the bit-error-rate of 10-2 at 128×32 antenna configuration with low complexity, which surpasses the state-of-the-art (SOTA) algorithms.

We evaluated the transmission performance, including received power and transmission throughput characteristics, in 4×4 single-user multiple-input multiple-output (SU-MIMO) transmission for synchronous time division duplex (TDD) and downlink data channels in comparison with single-input single-output (SISO) transmission in an environment where a local 5G wireless base station was installed on the roof of a research building at our university. Accordingly, for the received power characteristics, the difference between the simulation value, which was based on the ray tracing method, and the experimental value at 32 points in the area was within a maximum difference of approximately 10 dB, and sufficient compliance was obtained. Regarding the transmission throughput versus received power characteristics, after showing a simulation method for evaluating throughput characteristics in MIMO, we compared the results with experimental results. The cumulative distribution function (CDF) of the transmission throughput shows that, at a CDF of 50%, in SISO transmission, the simulated value is approximately 115Mbps, and the experimental value is 105Mbps, within a difference of approximately 10Mbps. By contrast, in MIMO transmission, the simulation value is 380Mbps, and the experimental value is approximately 420Mbps, which is a difference of approximately 40Mbps. It was shown that the received power and transmission throughput characteristics can be predicted with sufficient accuracy by obtaining the delay profile and the system model at each reception point using the both ray tracing and MIMO simulation methods in actual environments.

Non-orthogonal multipe access based multiple-input multiple-output system (MIMO-NOMA) has been widely used in improving user's achievable rate of millimeter wave (mmWave) communication. To meet different requirements of each user in multi-user beams, this paper proposes a power allocation algorithm to satisfy the quality of service (QoS) of head user while maximizing the minimum rate of edge users from the perspective of max-min fairness. Suppose that the user who is closest to the base station (BS) is the head user and the other users are the edge users in each beam in this paper. Then, an optimization problem model of max-min fairness criterion is developed under the constraints of users' minimum rate requirements and the total transmitting power of the BS. The bisection method and Karush-Kuhn-Tucher (KKT) conditions are used to solve this complex non-convex problem, and simulation results show that both the minimum achievable rates of edge users and the average rate of all users are greatly improved significantly compared with the traditional MIMO-NOMA, which only consider max-min fairness of users.

This paper proposes and evaluates machine learning (ML)-based compensation methods for the transmit (Tx) weight matrices of actual singular value decomposition (SVD)-multiple-input and multiple-output (MIMO) transmissions. These methods train ML models and compensate the Tx weight matrices by using a large amount of training data created from statistical distributions. Moreover, this paper proposes simplified channel metrics based on the channel quality of actual SVD-MIMO transmissions to evaluate compensation performance. The optimal parameters are determined from many ML parameters by using the metrics, and the metrics for this determination are evaluated. Finally, a comprehensive computer simulation shows that the optimal parameters improve performance by up to 7.0dB compared with the conventional method.

This paper proposes an adaptive mixing probability scheme for mixed Gibbs sampling (MGS) or MGS with maximum ratio combining (MRC) in multiple-input multiple-output (MIMO) demodulation. In the conventional MGS algorithm, the mixing probability is fixed. Thus, if a search point is captured by a local minimum, it takes a larger number of samples to escape. In the proposed scheme, the mixing probability is increased when a candidate transmit symbol vector is captured by a local minimum. Using the adaptive mixing probability, the numbers of candidate transmit symbol vectors searched by demodulation algorithms increase. The proposed scheme in MGS as well as MGS with MRC reduces an error floor level as compared with the conventional scheme. Numerical results obtained through computer simulation show that the bit error rates of the MGS as well as the MGS with MRC reduces by about 1/100 when the number of iterations is 100 in a 64×64 MIMO system.

In this paper, multi-input multi-output (MIMO) signal detection with random walk along a gradient descent direction using an intermediate search point is presented. As a low complexity MIMO signal detection schemes, a gradient descent algorithm with Metropolis-Hastings (MH) methods has been proposed. Random walk along a gradient descent direction speeds up the MH based search using the gradient of a least-squares cost function. However, the gradient vector may be discarded through QAM constellation quantization in some cases. For further performance improvement, this paper proposes an improved search scheme in which the gradient vector is stored for the next search iteration to generate an intermediate search point. The performance of the proposed scheme improves with higher order modulation symbols as compared with that of a conventional scheme. Numerical results obtained through computer simulation show that a bit error rate (BER) performance improves by 5dB at a BER of 10-3 for 64QAM symbols in a 16×16 MIMO system.

For massive multiple-input multiple-output (MIMO) communication systems, simple linear detectors such as zero forcing (ZF) and minimum mean square error (MMSE) can achieve near-optimal detection performance with reduced computational complexity. However, such linear detectors always involve complicated matrix inversion, which will suffer from high computational overhead in the practical implementation. Due to the massive parallel-processing and efficient hardware-implementation nature, the neural network has become a promising approach to signal processing for the future wireless communications. In this paper, we first propose an efficient neural network to calculate the pseudo-inverses for any type of matrices based on the improved Newton's method, termed as the PINN. Through detailed analysis and derivation, the linear massive MIMO detectors are mapped on PINNs, which can take full advantage of the research achievements of neural networks in both algorithms and hardwares. Furthermore, an improved limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) quasi-Newton method is studied as the learning algorithm of PINNs to achieve a better performance/complexity trade-off. Simulation results finally validate the efficiency of the proposed scheme.

In this paper, a nonlinear quantized precoding scheme for low-resolution digital-analog converters (DACs) in a massive multiple-input multiple-output (MIMO) system is proposed. The nonlinear quantized precoding determines transmit antenna outputs with a transmit symbol and channel state information. In a full-digital massive MIMO system, low-resolution DACs are used to suppress power consumption. Conventional precoding algorithms for low-resolution DACs do not optimize transmit antenna gains individually. Thus, in this paper, a precoding scheme that optimizes individual transmit antenna gains as well as the DAC outputs is proposed. In the proposed scheme, the subarray of massive MIMO antennas is treated virtually as a single antenna element. Numerical results obtained through computer simulation show that the proposed precoding scheme achieves bit error rate performance close to that of the conventional precoding scheme with much smaller antenna gains on a CDL-A channel.

This paper proposes overloaded multiple input multiple output (MIMO) bi-directional communication with physical layer network coding (PLNC) to enhance the transmission speed in heterogeneous wireless multihop networks where the number of antennas on the relay is less than that on the terminals. The proposed overloaded MIMO communication system applies precoding and relay filtering to reduce computational complexity in spite of the transmission speed. An eigenvector-based filter is proposed for the relay filter. Furthermore, we propose a technique to select the best filter among candidates eigenvector-based filters. The performance of the proposed overloaded MIMO bi-directional communication is evaluated by computer simulation in a heterogeneous wireless 2-hop network. The proposed filter selection technique attains a gain of about 1.5dB at the BER of 10-5 in a 2-hop network where 2 antennas and 4 antennas are placed on the relay and the terminal, respectively. This paper shows that 6 stream spatial multiplexing is made possible in the system with 2 antennas on the relay.

In this paper, we describe a wavelength-division multiplexing visible-light communication (VLC) system using two colored light-emitting diodes (LEDs) with similar emission wavelengths. A multi-input multi-output signal-separation method using a neural network is proposed to cancel the optical cross chatter caused by the spectral overlap of LEDs. The experimental results demonstrate that signal separation using neural networks can be achieved in wavelength-multiplexed VLC systems with a bit error rate of less than 3.8×10-3 (forward error correction limit). Furthermore, the simulation results reveal that the carrier-to-noise ratio (CNR) is improved by 2dB for the successive interference canceller (SIC) compared to the zero-forcing method.