Multi-stream Transmission Research Articles

Machine-Learning Based Packet Switching Method for Providing Stable High-Quality Video Streaming in Multi-Stream Transmission

Machine-Learning Based Packet Switching Method for Providing Stable High-Quality Video Streaming in Multi-Stream Transmission

Multi-Stream Transmission for Directional Modulation Network via Distributed Multi-UAV-Aided Multi-Active-IRS

Multi-Stream Transmission for Directional Modulation Network via Distributed Multi-UAV-Aided Multi-Active-IRS

Channel Customization for Joint Tx-RISs-Rx Design in Hybrid mmWave Systems

In strong line-of-sight millimeter-wave (mmWave) wireless systems, the rank-deficient channel severely hampers spatial multiplexing. To address this inherent deficiency, distributed reconfigurable intelligent surfaces (RISs) are introduced in this study to customize the wireless channel. Capitalizing on the ability of the RIS to reshape electromagnetic waves, we theoretically show that a favorable channel with an arbitrary tunable rank and a minimized truncated condition number can be established by elaborately designing the placement and reflection matrix of RISs. Different from existing works on distributed RISs, the number of elements needed for each RIS to combat the path loss and the limited phase control is also considered in this research. On the basis of the proposed channel customization, a joint transmitter-RISs-receiver (Tx-RISs-Rx) design under a hybrid mmWave system is investigated to maximize the downlink spectral efficiency. Using the proposed scheme, the optimal singular value decomposition-based hybrid beamforming at the Tx and Rx can be easily obtained without matrix decomposition for the digital and analog beamforming. The bottoms of the sub-channel mode in the water-filling power allocation algorithm, which are conventionally uncontrollable when the noise power is fixed, are proven to be independently adjustable by RISs. Moreover, the transmit power required for realizing multi-stream transmission is derived. Numerical results are presented to verify our theoretical analysis and exhibit substantial gains over systems without RISs.

Multi-Stream Transmission in Massive MIMO Systems With Full-Duplex Bi-Directional Communication Links

This paper considers a full-duplex (FD) multi-user massive multiple-input multiple-output system where the base station (BS) and user equipment (UE) bi-directionally transmit and receive multiple independent data streams over the same frequency band. The BS and UEs are equipped with a large-scale antenna array. Accounting for imperfect channel state information, we derive the closed-form expressions for the achievable sum-rate and sum spectral efficiency (SE) for the proposed model using maximum-ratio combining and zero-forcing detection. The analytical solutions are verified with simulations. In addition, our analytical results highlight the benefits of multi-stream transmission and show an improved SE for fewer UEs. Moreover, the results show that increasing the antenna array size helps combat self-interference.

On the Ergodic Mutual Information of Keyhole MIMO Channels With Finite-Alphabet Inputs

This letter studies the ergodic mutual information (EMI) of keyhole multiple-input multiple-output channels having finite-alphabet input signals. The EMI is first investigated for single-stream transmission considering both cases with and without the channel state information at the transmitter. Then, the derived results are extended to the scenario of multi-stream transmission. Asymptotic analyses are performed in the regime of high signal-to-noise ratio (SNR). The high-SNR EMI is shown to converge to a constant with its rate of convergence determined by the diversity order. On this basis, the influence of the keyhole effect on the EMI is discussed. The analytical results are validated by numerical simulations.

Optimal Hybrid Precoding Based QoE for Partially Structured Massive MIMO System

Precoding is a beamforming technique that supports multi-stream transmission in which the RF chain plays a significant role as a digital precoding at the receiver for wireless communication. The traditional precoding contains only digital signal processing and each antenna connects to each RF chain, which provides high transmission efficiency but high cost and hardware complexity. Hybrid precoding is one of the most popular massive multiple input multiple output (MIMO) techniques that can save costs and avoid using complex hardware. At present, network services are currently in focus with a wide range of traffic volumes. In terms of the Quality of Service (QoS), it is critical that service providers pay a lot of attention to this parameter and its relationship to Quality of Experience (QoE) which is the measurement of the overall level of user satisfaction. Therefore, this paper proposes hybrid precoding of a partially structured system to improve transmission efficiency and allocate resources to provide network services to users for increasing the user satisfaction under power constraints that optimize the quality of baseband precoding and radio frequency (RF) precoding by minimizing alternating algorithms. We focus on the web browsing, video, and Voice over IP (VOIP) services. Also, a Mean Opinion Score (MOS) is employed to measure the level of user satisfaction. The results show that the partially structured system provides a good user satisfaction with the network's services. The partially structured system provides high energy efficiency up to 85%. Considering web service, the partially structured system for 10 users provides MOS at 3.21 which is higher than 1.75 of fully structured system.

Accommodating LAA Within IEEE 802.11ax WiFi Networks for Enhanced Coexistence

Given the abundance of unlicensed spectrum in 5 GHz band, licensed-assisted-access (LAA) technology presents an efficient and simple approach to alleviate the spectrum crunch in wireless networks. The recent proposal of IEEE 802.11ax as an advanced WiFi standard to accommodate more high rate connections motivates the investigation with respect to the feasibility and benefits of LAA in coexistence with unlicensed user access under this new WiFi protocol. In this article, we first introduce an enhanced LAA and WiFi coexistence mechanism based on spatial multi-stream transmission within IEEE 802.11ax access. We then derive a stream selection and user replacement strategy based on the proposed synchronous coexistence scheme to improve LAA performance without sacrificing WiFi throughput. We further present a Lyapunov algorithm to optimize the LAA access intensity level with rapid convergence and low-complexity. Numerical results demonstrate the effectiveness of the proposed algorithms, and the mutual benefits of the proposed coexistence framework to both LAA and WiFi users.

빔포밍 알고리즘

Beamforming techniques have become an integral part of the fifth-generation(5G) of mobile communication systems and continue to evolve for providing high spectral efficiency, spatial diversity, and beamforming gain in millimeter-wave and terahertz(THz) communication for future 6G networks. This paper presents a comprehensive overview of beamforming algorithms from statistical signal processing and optimization perspectives. The fundamentals of beamforming for wireless communication and several representative beamforming algorithms for single-stream and multi-stream transmission are discussed.

Channel Capacity of Massive MIMO With Selected Multi-Stream Transmission in Spatially Correlated Fading Environments

This study analyzed the channel capacity of multiple-input multiple-output (MIMO) reception by large-scale arrays at both ends in spatially correlated fading environments. The environments considered were Rayleigh fading for representing the non-line-of-sight condition and Nakagami-Rice fading for the line-of-sight condition. The transmission characteristics of massive MIMO were evaluated using the asymptotic eigenvalue distribution of a Wishart matrix composed of the channel matrix. The transmission scheme adopted here was eigen-mode multi-stream transmission that selected L from N streams of N-by-M (M ≥ N) massive MIMO. From the analysis, we determined that the effect of spatial correlation for the case of L/N being less than approximately 20% enhances the channel capacity, unlike spatial diversity operation. In this way, we clarified the effect of spatial correlation on channel capacity of massive MIMO quantitatively through asymptotic eigenvalue analysis.

MmWave Amplify-and-Forward MIMO Relay Networks With Hybrid Precoding/Combining Design

In this paper, we consider the amplify-and-forward relay networks in mmWave systems and propose a hybrid precoder/combiner design approach. The phase-only RF precoding/combining matrices are first designed to support multi-stream transmission, where we compensate the phase for the eigenmodes of the channel. Then, the baseband precoders/combiners are performed to achieve the maximum mutual information. Based on the data processing inequality for the mutual information, we first jointly design the baseband source and relay nodes to maximize the mutual information before the destination baseband receiver. The proposed low-complexity iterative algorithm for the source and relay nodes is based on the equivalence between mutual information maximization and the weighted MMSE. After we obtain the optimal precoder and combiner for the source and relay nodes, we implement the MMSE-SIC filter at the baseband receiver to keep the mutual information unchanged, thus obtaining the optimal mutual information for the whole relay system. Simulation results show that our algorithm achieves better performance with lower complexity compared with other algorithms in the literature. In addition, we also propose a robust joint transceiver design for imperfect channel state information.

Multi-Stream Opportunistic Network Decoupling: Relay Selection and Interference Management

We study multi-stream transmission in the $K \times N \times K$ channel with interfering relay nodes, consisting of $K$ multi-antenna source--destination (S--D) pairs and $N$ single-antenna half-duplex relay nodes between the S--D pairs. We propose a new achievable scheme operating with partial effective channel gain, termed multi-stream opportunistic network decoupling (MS-OND), which achieves the optimal degrees of freedom (DoF) under a certain relay scaling law. Our protocol is built upon the conventional OND that leads to virtual full-duplex mode with one data stream transmission per S--D pair, generalizing the idea of OND to multi-stream scenarios by leveraging relay selection and interference management. Specifically, two subsets of relay nodes are opportunistically selected using alternate relaying in terms of producing or receiving the minimum total interference level. For interference management, each source node sends $S \,(1 \le S \le M)$ data streams to selected relay nodes with random beamforming for the first hop, while each destination node receives its desired $S$ streams from the selected relay nodes via opportunistic interference alignment for the second hop, where $M$ is the number of antennas at each source or destination node. Our analytical results are validated by numerical evaluation.

Distributed Multi-Stream Beamforming in MIMO Multi-Relay Interference Networks

In this paper, multi-stream transmission in interference networks aided by multiple amplify-and-forward (AF) relays in the presence of direct links is considered. The objective is to minimize the sum power of transmitters and relays by beamforming optimization under the stream signal-to-interference-plus-noise-ratio (SINR) constraints. For transmit beamforming optimization, the problem is a well-known non-convex quadratically constrained quadratic program (QCQP) that is NP-hard to solve. After semi-definite relaxation (SDR), the problem can be optimally solved via alternating direction method of multipliers (ADMM) algorithm for distributed implementation. Analytical and extensive numerical analyses demonstrate that the proposed ADMM solution converges to the optimal centralized solution. The convergence rate, computational complexity, and message exchange load of the proposed algorithm outperforms the existing solutions. Furthermore, by SINR approximation at the relay side, distributed joint transmit and relay beamforming optimization is also proposed that further improves the total power saving at the cost of increased complexity.

The Benefits of Hybrid Caching in Gauss–Poisson D2D Networks

Device caching has recently been proposed as an efficient way to offload traffic from congested cellular networks. However, previous works usually ignore the fact that, in practice, the user device may not be willing to help others due to the limited battery capacity. In this paper, we introduce cooperation among the device-to-device (D2D) transmitters and propose two novel hybrid caching strategies—single-point caching combined with two-point cooperative caching with joint transmission (SPC-CCJT) or multi-stream transmission (SPC-CCMT)—aiming at saving the energy cost of content deliverers. Using tools from stochastic geometry, we propose an analytical framework of the hybrid caching strategies by modeling the locations of the D2D transmitters as a Gauss–Poisson process (GPP) to accurately capture the clustering and cooperative behaviors. First, we consider a probabilistic caching placement and optimize the caching distribution to maximize the cache hit probability. Second, to compare the performance between different content delivery strategies, we derive the success probability and per-user capacity for SPC, CCJT, and CCMT, respectively. These results are then applied to evaluate the offloading gain and the distribution of the content retrieval delay for SPC-CCJT and SPC-CCMT in the GPP-based D2D networks. It turns out that significant offloading gain and delay improvement can be achieved by hybrid caching with cooperation while the energy cost of each cooperator is kept low.

Spectral Efficiency Optimization For Millimeter Wave Multiuser MIMO Systems

As a key enabling technology for 5G wireless, millimeter wave (mmWave) communication motivates the utilization of large-scale antenna arrays for achieving highly directional beamforming. However, the high cost and power consumption of RF chains stands in the way of adoption of the optimal fullydigital precoding in large-array systems. To reduce the number of RF chains while still maintaining the spatial multiplexing gain of large-array, hybrid precoding architecture has been proposed for mmWave systems and received considerable interest in both industry and academia. However, the optimal hybrid precoding design has not been fully understood, especially for the multi-user MIMO case. This paper is the first work that directly addresses the nonconvex hybrid precoding problem of mmWave multi-user MIMO systems (without any approximation) by using penalty dual decomposition (PDD) method. The proposed PDD method have a guaranteed convergence to KKT solutions of the hybrid precoding problem under a mild assumption. Simulation results show that, even when both the transmitter and the receivers are equipped with the fewest RF chains that are required to support multi-stream transmission, hybrid precoding can still approach the performance of fully-digital precoding in both the infinite resolution phase shifter case and the finite resolution phase shifter case with several bits quantization.

Constant Envelope Precoding for MIMO Systems

Constant envelope (CE) precoding is an appealing transmission technique, which enables highly efficient power amplification, and is realizable with a single radio frequency (RF) chain at the multi-antenna transmitter. In this paper, we study the transceiver design for a point-to-point multiple-input multiple-output (MIMO) system with CE precoding. Both single-stream transmission (i.e., beamforming) and multi-stream transmission (i.e., spatial multiplexing) are considered. For single-stream transmission, we optimize the receive beamforming vector to minimize the symbol error rate (SER) for any given channel realization and desired constellation at the combiner output. By reformulating the problem as an equivalent quadratically constrained quadratic program (QCQP), we propose an efficient semi-definite relaxation (SDR) based algorithm to find an approximate solution. Next, for multi-stream transmission, we propose a new scheme based on antenna grouping at the transmitter and minimum mean squared error (MMSE) or zero-forcing (ZF) based beamforming at the receiver. The transmit antenna grouping and receive beamforming vectors are then jointly designed to minimize the maximum SER over all data streams. Finally, the error-rate performance of single- versus multi-stream transmission is compared via simulations under different setups.

Low Complexity Wireless Powered Information Transfer Strategy for Multiuser MIMO Systems

In this paper, the minimum achievable rate maximization problem for wireless-powered multiple users is investigated. Zero-forcing (ZF)-based precoding is considered for information transmission (IT) in the uplink from multi-antenna users to a multi-antenna access point that charges the users through wireless power transmission (PT) in the downlink. A max–min user rate optimization problem is formulated when a ZF-based transmission is used for spatial division multiplexing in the IT phase. We propose a low-complexity method to design the energy beamformer for the PT phase, the transmit power allocation for multi-stream transmission for the IT phase, and the time ratio between PT and IT phases in terms of maximizing the minimum achievable rate. The validity of the proposed scheme is shown through complexity analysis and numerical simulations.

Joint hybrid precoder and combiner design for multi‐stream transmission in mmWave MIMO systems

Millimeter wave (mmWave) communications have been considered as a key technology for future 5G wireless networks since it can provide orders-of-magnitude wider bandwidth than current cellular bands. To overcome the severe propagation loss of the mmWave channel, an economic and energy-efficient analogue/digital hybrid precoding and combining transceiver architecture is widely used in mmWave massive multiple-input multiple-output (MIMO) systems. The digital precoding/combining layer offers more freedom than pure analogue one and enables multi-stream transmission. In this study, the authors consider the problem of codebook-based joint hybrid precoder and combiner design for multi-stream transmission in mmWave MIMO systems. The authors propose to jointly select an analogue precoder and combiner pair for each data stream successively, which can maximise the channel gain as well as suppress the interference between different data streams. Then, the digital precoder and combiner are computed based on the obtained effective baseband channel to further mitigate the interference and maximise the sum-rate. Both fully-connected and partially-connected hybrid beamforming structures are investigated. Simulation results demonstrate that the proposed algorithms exhibit prominent advantages in combating interference between different data streams and offer satisfactory performance improvements compared with the existing codebook-based hybrid beamforming schemes.

Near-Optimal Hybrid Processing for Massive MIMO Systems via Matrix Decomposition

For the practical implementation of massive multiple-input multiple-output (MIMO) systems, the hybrid processing (precoding/combining) structure is promising to reduce the high cost rendered by large number of RF chains of the traditional processing structure. The hybrid processing is performed through low-dimensional digital baseband processing combined with analog RF processing enabled by phase shifters. We propose to design hybrid RF and baseband precoders/combiners for multi-stream transmission in point-to-point massive MIMO systems, by directly decomposing the pre-designed unconstrained digital precoder/combiner of a large dimension. The constant amplitude constraint of analog RF processing results in the matrix decomposition problem non-convex. Based on an alternate optimization technique, the non-convex matrix decomposition problem can be decoupled into a series of convex sub-problems and effectively solved by restricting the phase increment of each entry in the RF precoder/combiner within a small vicinity of its preceding iterate. A singular value decomposition based technique is proposed to secure an initial point sufficiently close to the global solution of the original non-convex problem. Through simulation, the convergence of the alternate optimization for such a matrix decomposition based hybrid processing (MD-HP) scheme is examined, and the performance of the MD-HP scheme is demonstrated to be near-optimal.

Channel Estimation and Hybrid Precoding for Millimeter-Wave MIMO Systems: A Low-Complexity Overall Solution

To enable multi-stream transmission and increase the achievable rate, a hybrid digital/analog precoding structure is usually adopted in millimeter-wave (mmWave) MIMO systems. However, it may require matrix operations with a scale of antenna size, which is generally large in mmWave communications. Moreover, the channel estimation is also rather time-consuming due to the large number of antennas at both Tx/Rx sides. In this paper, a low-complexity overall channel estimation and hybrid precoding approach is proposed. In the channel estimation phase, a hierarchical multi-beam search scheme is proposed to fast acquire $N_{\mathrm {S}}$ (the number of streams) multipath components (MPCs)/clusters with the highest powers. In the hybrid precoding phase, the analog and digital precodings are decoupled. The analog precoding is designed to steer along the $N_{\mathrm {S}}$ acquired MPCs/clusters at both Tx/Rx sides, shaping an $N_{\mathrm {S}}\times N_{\mathrm {S}}$ baseband effective channel, while the digital precoding is performed in the baseband with the reduced-scale effective channel. Performance evaluations show that, compared with the state-of-the-art scheme, while achieving a close or even better performance when the number of radio frequency chains or streams is small, both the time complexity of the channel estimation and the computational complexity of the hybrid precoding are reduced.

Weighted Subspace Fitting for Two-Dimension DOA Estimation in Massive MIMO Systems

Massive multiple-input multiple-output system can enable multi-stream transmission for high spectrum efficiency, which is a key technology in future 5G cellular networks. In this paper, we degenerate a direction of arrival (DOA) related 2-D weighted subspace fitting function into two independent parameterized 1-D versions. Based on this, we develop a novel 2-D DOA estimation algorithm, which can be utilized to assist in performing downlink precoding. Furthermore, we also make an analysis on the computational complexity and the theoretical Cramer–Rao lower bound. The direct merits are as follows: the proposed algorithm includes only once polynomial rooting and also does not require angle paring, hence it is of computational efficiency; in addition, compared with some existing algorithms, it can achieve higher 2-D angle estimating accuracy. A series of Monte Carlo simulations are subsequently carried out, which demonstrate the effectiveness of the proposed algorithm.