Search Results (33)

Hybrid beamforming plays a critical role in evaluating wireless communication technology, particularly for millimeter-wave (mmWave) multiple-input multiple-out (MIMO) communication. Several hybrid beamforming systems are investigated for millimeter-wave multiple-input multiple-output (MIMO) communication. The deployment of huge grant-free transmission in the millimeter-wave (mmWave) band is required due to the growing demands for spectrum resources in upcoming enormous machine-type communication applications. Ultra-high data speed, reduced latency, and improved connection are all promised by the development of 5G mmWave networks. Yet, due to severe route loss and directional communication requirements, there are substantial obstacles to transmission reliability and energy efficiency. To address this limitation in this research we present an intelligent transmission control scheme tailored to 5G mmWave networks. Transport control protocol (TCP) performance over mmWave links can be enhanced for network protocols by utilizing the mmWave scalable (mmS)-TCP. To ensure that users have the stronger average power, we suggest a novel method called row compression two-stage learning-based accurate multi-path processing network with received signal strength indicator-based association strategy (RCTS-AMP-RSSI-AS) for an estimate of both the direct and indirect channels. To change user scenarios and maintain effective communication constantly, we utilize the innovative method known as multi-user scenario-based MATD3 (Mu-MATD3). To improve performance, we introduce the novel method of “digital and analog beam training with long-short term memory (DAH-BT-LSTM)”. Finally, as optimizing network performance requires bottleneck-aware congestion reduction, the low-latency congestion control schemes (LLCCS) are proposed. The overall proposed method improves the performance of 5G mmWave networks. Full article

This paper proposes a source encoding rate control and cross-layer data stream allocation scheme for uplink millimeter-wave (mmWave) multi-user massive MIMO (MU-mMIMO) orthogonal frequency division multiplexing (OFDM) hybrid beamforming video communication systems. Unlike most previous studies that focus on the downlink scenario, our proposed scheme optimizes the uplink transmission while also addressing the limitation of prior works that only consider single-data-stream users. A key distinction of our approach is the integration of cross-layer resource allocation, which jointly considers both the physical layer channel state information (CSI) and the application layer video rate-distortion (RD) function. While traditional methods optimize for spectral efficiency (SE), our proposed method directly maximizes the peak signal-to-noise ratio (PSNR) to enhance video quality, aligning with the growing demand for high-quality video communication. We introduce a novel iterative cross-layer dynamic data stream allocation scheme, where the initial allocation is based on conventional physical-layer data stream allocation, followed by iterative refinement. Through multiple iterations, users with lower PSNR can dynamically contend for data streams, leading to a more balanced and optimized resource allocation. Our approach is a general framework that can incorporate any existing physical-layer data stream allocation as an initialization step before iteration. Simulation results demonstrate that the proposed cross-layer scheme outperforms three conventional physical-layer schemes by 0.4 to 1.14 dB in PSNR for 4–6 users, at the cost of a 1.8 to 2.3× increase in computational complexity (requiring 3.6–5.8 iterations). Full article

In modern (5G) and future Multi-User (MU) wireless communication systems Beyond 5G (B5G) using Multiple-Input Multiple-Output (MIMO) technology, base stations with a large number of antennas communicate with many mobile stations. This technology is becoming especially relevant in modern multi-user wireless sensor networks in various application scenarios. The problem of organizing an MU mode on the downlink has arisen, which can be solved by precoding at the Base Station (BS) without using additional channel frequency–time resources. In order to utilize an efficient precoding algorithm at the base station, full Channel State Information (CSI) is needed for each mobile station. Transmitting this information for massive MIMO systems normally requires the allocation of high-speed channel resources for the feedback. With limited feedback, reduced information (partial CSI) is used, for example, the codeword from the codebook that is closest to the estimated channel vector (or matrix). Incomplete (or inaccurate) CSI causes interference from the signals, transmitted to neighboring mobile stations, that ultimately results in a decrease in the number of active users served. In this paper, we propose a new downlink precoding approach for MU-MIMO systems that also uses codebooks to reduce the information transmitted over a feedback channel. A key aspect of the proposed approach, in contrast to the existing ones, is the transmission of new, uncorrelated information in each cycle, which allows for accumulating CSI with higher accuracy without increasing the feedback overhead. The proposed approach is most effective in systems with dynamic user selection. In such systems, increasing the accuracy of CSI leads to an increase in the number of active users served, which after a few cycles, can reach a maximum value determined by the number of transmit antennas at the BS side. This approach appears to be promising for addressing the challenges associated with current and future massive MIMO systems, as evidenced by our statistical simulation results. Various methods for extracting and transmitting such uncorrelated information over a feedback channel are considered. In many known publications, the precoder, codebooks, CSI estimation methods and other aspects of CSI transmission over a feedback channel are separately optimized, but a comprehensive approach to jointly solving these problems has not yet been developed. In our paper, we propose to fill this gap by combining a new approach of precoding and CSI estimation with CSI accumulation and transmission over a feedback channel. Full article

In the multi-user multiple-input-multiple-output (MU-MIMO) beamforming (BF) training defined by the 802.11ay standard, since a single initiator transmits a significant number of action frames to multiple responders, inefficient configuration of the transmit antenna arrays when sending these action frames increases the signaling and latency overheads of MU-MIMO BF training. To configure appropriate transmit antenna arrays for transmitting action frames, the initiator needs to accurately estimate the signal to noise ratios (SNRs) measured at the responders for each configuration of the transmit antenna arrays. In this paper, we propose a two-phase deep learning approach to improve the accuracy of SNR estimation for multiple concurrent beams by reducing the measurement errors of the SNRs for individual single beams when each action frame is transmitted through multiple concurrent beams. Through simulations, we demonstrated that our proposed scheme enabled more responders to successfully receive action frames during MU-MIMO BF training compared to existing schemes. Full article

This paper proposes a cognitive radio network (CRN)-based hybrid wideband precoding for maximizing spectral efficiency in millimeter-wave relay-assisted multi-user (MU) multiple-input multiple-output (MIMO) systems. The underlying problem is NP-hard and non-convex due to the joint optimization of hybrid processing components and the constant amplitude constraint imposed by the analog beamformer in the radio frequency (RF) domain. Furthermore, the analog beamforming solution common to all sub-carriers adds another layer of design complexity. Two hybrid beamforming architectures, i.e., mixed and fully connected ones, are taken into account to tackle this problem, considering the decode-and-forward (DF) relay node. To reduce the complexity of the original optimization problem, an attempt is made to decompose it into sub-problems. Leveraging this, each sub-problem is addressed by following a decoupled design methodology. The phase-only beamforming solution is derived to maximize the sum of spectral efficiency, while digital baseband processing components are designed to keep interference within a predefined limit. Computer simulations are conducted by changing system parameters under different accuracy levels of channel-state information (CSI), and the obtained results demonstrate the effectiveness of the proposed technique. Additionally, the mixed structure shows better energy efficiency performance compared to its counterparts and outperforms benchmarks. Full article

The practical implementation of massive multi-user multi-input–multi-output (MU-MIMO) downlink communication systems power amplifiers that are energy efficient; otherwise, the power consumption of the base station (BS) will be prohibitive. Constant envelope (CE) precoding is gaining increasing interest for its capability to utilize low-cost, high-efficiency nonlinear radio frequency amplifiers. Our work focuses on the topic of CE precoding in massive MU-MIMO systems and presents an efficient CE precoding algorithm. This algorithm uses an alternating minimization (AltMin) framework to optimize the CE precoded signal and precoding factor, aiming to minimize the difference between the received signal and the transmit symbol. For the optimization of the CE precoded signal, we provide a powerful approach that integrates the majorization-minimization (MM) method and the fast iterative shrinkage-thresholding (FISTA) method. This algorithm combines the characteristics of the massive MU-MIMO channel with the second-order Taylor expansion to construct the surrogate function in the MM method, in which minimizing this surrogate function is the worst-case of the system. Specifically, we expand the suggested CE precoding algorithm to involve the discrete constant envelope (DCE) precoding case. In addition, we thoroughly examine the exact property, convergence, and computational complexity of the proposed algorithm. Simulation results demonstrate that the proposed CE precoding algorithm can achievean uncoded biterror rate (BER) performance gain of roughly $1\mathrm{dB}$ compared to the existing CE precoding algorithm and has an acceptable computational complexity. This performance advantage also exists when it comes to DCE precoding. Full article

Photon counting has been proven to possess excellent signal detection capabilities at low power levels and has extensive potential applications in sixth-generation (6G) communications. However, the inherent dependency between the signal and noise complicates system analysis, and optimizing achievable rates in photon-counting visible light communication (VLC) systems remains unresolved. This paper introduces a new method aimed at minimizing multi-user interference (MUI) through a zero-forcing (ZF) scheme and maximizing the weighted sum rate of the proposed downlink multi-user photon-counting multiple-input multiple-output (MU-PhC-MIMO) VLC system by solving an optimization problem. The key point lies in our utilization of the ZF approach to derive a reasonable asymptotic approximation expression for the weighted sum rate. Subsequently, we use variable substitution and methods like successive convex approximation (SCA) to iteratively convexify the non-convex optimization problem and maximize the weighted sum rate under the ZF form. Compared to other algorithms, this approach can save 2.5 dB of transmission power to achieve the same system-weighted sum rate and significantly outperforms the repetition coding scheme at sufficient transmission power. Full article

This paper presents two precoding techniques for a reconfigurable intelligent surface (RIS)-assisted multi-user (MU) multiple-input multiple-output (MIMO) double quadrature spatial modulation (DQSM) downlink transmission system. Instead of being applied at the remote RIS, the phase shift vector is applied at the base station (BS) by using a double precoding stage. Results show that the proposed RIS-MU-MIMO-DQSM system has gains of up to 17 dB in terms of bit error rate (BER) and a reduction in detection complexity of 51% when compared with the conventional MU-MIMO system based on quadrature amplitude modulation (QAM). Compared with a similar system based on amplify and forward (AF) relay-assisted technique, the proposed system has a gain of up to 18 dB in terms of BER under the same conditions and parameters. Full article

Digital precoding techniques have been widely applied in multiple-input multiple-output (MIMO) systems to enhance spectral efficiency (SE) which is crucial in 5G New Radio (NR). Therefore, the 3rd Generation Partnership Project (3GPP) has developed codebook-based MIMO precoding strategies to achieve a good trade-off between performance, complexity, and signal overhead. This paper aims to evaluate the performance bounds in SE achieved by the 5G-NR precoding matrices in single-user (SU) and multi-user (MU) MIMO systems, namely Type I and Type II, respectively. The implementation of these codebooks is covered providing a comprehensive guide with a detailed analysis. The performance of the 5G-NR precoder is compared with theoretical precoding techniques such as singular value decomposition (SVD) and block-diagonalization to quantify the margin of improvement of the standardized methods. Several configurations of antenna arrays, number of antenna ports, and parallel data streams are considered for simulations. Moreover, the effect of channel estimation errors on the system performance is analyzed in both SU and MU-MIMO cases. For a realistic framework, the SE values are obtained for a practical deployment based on a clustered delay line (CDL) channel model. These results provide valuable insights for system designers about the implementation and performance of the 5G-NR precoding matrices. Full article

Advances in higher-end spectrum utilization has enabled user equipment to dock multiple antenna elements, and hence make use of selectivity via equalization in new generation of mobile networks. The equalization can exploit channel statistics to shape covariance matrices, and hence improve network performance at the physical layer of these networks by projecting segregated signals to non-overlapping subspaces. We propose to establish the promise of covariance shaping method by incorporating the equalizers in the modelling of a downlink multi-user multiple-input multiple-output (MU-MIMO) systems and thereby characterizing a key performance indicator, namely, the sum ergodic capacity. This is achieved by utilizing a residue theory approach which can account for indefinite eigenvalues. The system modelling is generic in a sense that it requires the base station (BS) to only have second order statistics of the channel rather than instantaneous knowledge. Furthermore, the BS incorporates a transmit beamformer design to enhance the ergodic capacity and feedforward the information of covariance shaping equalizers. Search method for transmit beamforming is also proposed which shows a promising three fold increase in sum ergodic capacity at signal-to-noise ratio of 10 dB for the considered MU-MIMO system. Proposed characterization of the system is authenticated using simulation means, and a comparative analysis of transmit beamformer designs on the sum ergodic rate is showcased. Full article

Multiple Input Multiple Output (MIMO) systems have been gaining significant attention from the research community due to their potential to improve data rates. However, a suitable scheduling mechanism is required to efficiently distribute available spectrum resources and enhance system capacity. This paper investigates the user selection problem in Multi-User MIMO (MU-MIMO) environment using the multi-agent Reinforcement learning (RL) methodology. Adopting multiple antennas’ spatial degrees of freedom, devices can serve to transmit simultaneously in every time slot. We aim to develop an optimal scheduling policy by optimally selecting a group of users to be scheduled for transmission, given the channel condition and resource blocks at the beginning of each time slot. We first formulate the MU-MIMO scheduling problem as a single-state Markov Decision Process (MDP). We achieve the optimal policy by solving the formulated MDP problem using RL. We use aggregated sum-rate of the group of users selected for transmission, and a $20\%$ higher sum-rate performance over the conventional methods is reported. Full article

Wi-Fi is a popular wireless technology and is continuously extended to keep pace with requirements such as high throughput, real-time communication, dense networks, or resource and energy efficiency. The IEEE 802.11ax standard, also known as Wi-Fi 6, promises to provide data rates of up to almost 10 Gb/s, lower energy consumption, and higher reliability. Its capabilities go far beyond Wi-Fi 5 (802.11ac) and novel technical concepts have been introduced for this purpose. As such, the Wi-Fi 6 standard includes Multi-User Orthogonal Frequency Division Multiple Access (MU OFDMA), Multi-User Multiple-Input Multiple-Output (MU MIMO), new mechanisms for Spatial Reuse (SR), new mechanisms for power saving, higher-order modulation, and additional minor improvements. In this paper, we provide a survey of Wi-Fi 6. Initially, we provide a compact technological summary of Wi-Fi 5 and its predecessors. Then, we discuss the potential application domains of Wi-Fi 6, which are enabled through its novel features. Subsequently, we explain these features and review the related works in these areas. Finally, performance evaluation tools for Wi-Fi 6 and future roadmaps are discussed. Full article

Multi-user multiple-input multiple-output (MU-MIMO) technology can significantly improve the spectral and energy efficiencies of wireless networks. In the uplink MU-MIMO systems, the optimal precoder design at the base station utilizes the Lagrange multipliers method and the centralized iterative algorithm to minimize the mean squared error (MSE) of all users under the power constraint. The precoding matrices need to be fed back to the user equipment to explore the potential benefits of the joint transceiver design. We propose a CNN-based compression network named PCQNet to minimize the feedback overhead. We first illustrate the effect of the trainable compression ratios and feedback bits on the MSE between the original precoding matrices and the recovered ones. We then evaluate the block error rates as the performance measure of the centralized implementation with an optimal minimum mean-squared error (MMSE) transceiver. Numerical results show that the proposed PCQNet achieves near-optimal performance compared with other quantized feedback schemes and significantly reduces the feedback overhead with negligible performance degradation. Full article

Multi-user multiple-input and multiple-output (MU-MIMO) systems are the mainstream of current antenna design. This paper proposes a dual-band 6 × 6 MIMO glasses antenna for Wi-Fi 6E and Wi-Fi 7 indoor wireless communication. The six antennas have the same structure, all of which are F-shaped monopole antennas. They are on the left and right temples, at the upper and lower ends of the left and right frames, which effectively uses the space of the glasses. The substrate uses FR4 $\left(\mathsf{\epsilon }\mathrm{r}=4.4,\tan \mathsf{\delta }=0.02\right)$. The antenna design is compact (9 mm × 50 mm × 0.8 mm) and the glasses model is made of FR4. The overall model is similar to virtual reality (VR) glasses, which are convenient for a user to wear. The proposed antenna has three working frequency bands, at 2.4 GHz, 5 GHz, and 6 GHz. Through matching and optimization, the reflection coefficient can be lower than −10 dB. In addition, this paper evaluates two usage environments for simulation and measurement on the head and free space. The measurement results show that when the operating frequency band is at 2.45 GHz, the antenna efficiency is 86.1%, and the antenna gain is 1.9 dB. At 5.5 GHz, the antenna efficiency is 86.5%, and the antenna gain is 4.4 dB. At 6.7 GHz, the antenna efficiency is 85.4%, and the antenna gain is 3.7 dB. When the isolation of the MIMO antenna system is optimized, the low-frequency band is better than −10 dB, and the high-frequency band is better than −20 dB. The measured envelope correlation coefficient (ECC) values are all lower than 0.1. Full article

In this paper, an unmanned aerial vehicle (UAV)-aided multi-antenna configured downlink mmWave cooperative generalized frequency division multiplexing (GFDM) system is proposed. To provide physical layer security (PLS), a 3D controlled Lorenz mapping system is introduced. Furthermore, the combination of T-transformation spreading codes, walsh Hadamard transform, and discrete Fourier transform (DFT) techniques are integrated with a novel linear multi-user multiple-input multiple-output (MU-MIMO) gyre precoding (GP) for multi-user interference reduction. Furthermore, concatenated channel-coding with multi-user beamforming weighting-aided maximum-likelihood and zero forcing (ZF) signal detection schemes for an improved bit error rate (BER) are also used. The system is then simulated with a single base station (BS), eight massive machine-type communications (mMTC) users, and two UAV relay stations (RSs). Numerical results reveal the robustness of the proposed system in terms of PLS and an achievable ergodic rate with signal-to-interference-plus-noise ratio (SINR) under the implementation of T-transformation scheme. By incorporating the 3D mobility model, brownian perturbations of the UAVs are also analyzed. An out-of-band (OOB) reduction of 320 dB with an improved BER of $1\times {10}^{-4}$ in 16-QAM for a signal-to-noise ratio, $E_b/N_0$, of 20 dB is achieved. Full article