Wave Multiple-input Multiple-output Systems Research Articles

RIS-Assisted Hybrid Beamforming and Connected User Vehicle Localization for Millimeter Wave MIMO Systems

A reconfigurable intelligent surface (RIS) is a type of metasurface that can dynamically control the reflection and transmission of electromagnetic waves, such as radio waves, by changing its physical properties. Recently, RISs have played an important role in intelligently reshaping wireless propagation environments to improve the received signal gain as well as spectral efficiency performance. In this paper, we consider a millimeter wave (mmWave) vehicle-to-vehicle (V2V) multiple-input multiple-output (MIMO) system in which, an RIS is deployed to aid downlink V2V data transmission. In particular, the line-of-sight path of the mmWave system is affected by blockages, resulting in higher signaling overhead. Thus, the system performance may suffer due to interruptions caused by static or mobile blockers, such as buildings, trees, vehicles, and pedestrians. In this paper, we propose an RIS-assisted hybrid beamforming scheme for blockage-aware mmWave V2V MIMO systems to increase communication service coverage. First, we propose a conjugate gradient and location-based hybrid beamforming (CG-LHB) algorithm to solve the user sub-rate maximization problem. We then propose a double-step iterative algorithm that utilizes an error covariance matrix splitting method to minimize the effect of location error on the passive beamforming. The proposed algorithms perform quite well when the channel uncertainty is smaller than 10%. Finally, the simulation results validate the proposed CG-LHB algorithm in terms of the RIS-assisted equivalent channel for mmWave V2V MIMO communications.

Interchannel Interference and Mitigation in Distributed MIMO RF Sensing.

In this paper, we analyze and mitigate the cross-channel interference, which is found in multiple-input multiple-output (MIMO) radio frequency (RF) sensing systems. For a millimeter wave (mm-Wave) MIMO system, we present a geometrical three-dimensional (3D) channel model to simulate the time-variant (TV) trajectories of a moving scatterer. We collected RF data using a state-of-the-art radar known as Ancortek SDR-KIT 2400T2R4, which is a frequency-modulated continuous wave (FMCW) MIMO radar system operating in the K-band. The Ancortek radar is currently the only K-band MIMO commercial radar system that offers customized antenna configurations. It is shown that this radar system encounters the problem of interference between the various subchannels. We propose an optimal approach to mitigate the problem of cross-channel interference by inducing a propagation delay in one of the channels and apply range gating. The measurement results prove the effectiveness of the proposed approach by demonstrating a complete elimination of the interference problem. The application of the proposed solution on Ancortek’s SDR-KIT 2400T2R4 allows resolving all subchannel links in a distributed MIMO configuration. This allows using MIMO RF sensing techniques to track a moving scatterer (target) regardless of its direction of motion.

Hybrid Precoding for Millimeter Wave Systems under Phase Noise problem

Millimeter wave - mm Wave - communications has been considered as a key authorised technology for the fifth generation networks. Hybrid precoding, which refer to a blend of analog and digital precoding, can achieve high spectral and energy efficiencies. However a cost-effective alternative and power consumption are needed. So hybrid precoding strategies become promising solutions. In the higher carrier frequencies, especially the millimeter-wave range, there are significant degradations in the transmitted and received signals due to radio-frequency impairments such as phase noise introduced by the local oscillators (LO), which is the irregular difference among the phase of the local oscillator and the phase of the carrier signal. It’s becoming a limiting factor in high data rate digital communication systems. In this research, we study the impact of phase noise on the performance of hybrid precoding structure in millimeter wave Multiple-Input Multiple-Output (MIMO) (mmWave MIMO) systems. Manifold optimization based alternating minimization algorithm is proposed. An optimization problem is formulated for phase noise issue in millimeter wave MIMO system. Results show that the impact of phase noise can degrade the system performance at higher RF chains.

Hierarchical Energy Efficient Hybrid Precoding for Configurable Sub-Connected MIMO Systems

This paper proposed a configurable sub-connected architecture with a framework that dynamically activates the near-optimal subset of antennas and RF chains to implement energy-efficient hybrid precoding in millimeter wave multiple-input multiple-output system. Since the exhaust search is computational intractable, we propose a two-stage hybrid precoding algorithm, where the digital precoder is designed to eliminate the inter-user interference by zero-forcing rule. Specifically, in the first stage, we introduce an extended cross-entropy algorithm that adaptively updates the probability distribution of potential states in the analog precoder matrix, which can generate a solution that is close to the optimal with a sufficiently high probability. In the second stage, a QR-based subset selection algorithm is proposed to pick the near-optimal subset of the RF chains to further cut down on the energy cost. Simulation results show that proposed extend-CE algorithm gets favorable performance in terms of energy efficiency, and QR-based RF chain selection can achieve a near-optimal performance. Other hybrid precoding algorithms can also be incorporated into the proposed RF chain selection algorithm.

Joint Bit Allocation and Hybrid Beamforming Optimization for Energy Efficient Millimeter Wave MIMO Systems

In this article, we aim to design highly energy efficient end-to-end communication for millimeter wave multiple-input multiple-output systems. This is done by jointly optimizing the digital-to-analog converter (DAC)/analog-to-digital converter (ADC) bit resolutions and hybrid beamforming matrices. The novel decomposition of the hybrid precoder and the hybrid combiner to three parts is introduced at the transmitter (TX) and the receiver (RX), respectively, representing the analog precoder/combiner matrix, the DAC/ADC bit resolution matrix and the baseband precoder/combiner matrix. The unknown matrices are computed as a solution to the matrix factorization problem where the optimal fully digital precoder or combiner is approximated by the product of these matrices. A novel and efficient solution based on the alternating direction method of multipliers is proposed to solve these problems at both the TX and the RX. The simulation results show that the proposed solution, where the DAC/ADC bit allocation is dynamic during operation, achieves higher energy efficiency when compared with existing benchmark techniques that use fixed DAC/ADC bit resolutions.

Hybrid precoding for millimetre wave MIMO systems based on particle swarm optimisation

Millimetre wave (mmWave) multiple-input multiple-output (MIMO) systems have been proposed to enable Gbps communication for next-generation cellular systems and local area networks. To compensate for the severe propagation loss of the mmWave channel, a cost-effective hybrid precoding architecture, combining a digital precoder and an analogue precoder, is widely used in mmWave MIMO systems. In this study, two hybrid precoding algorithms based on particle swarm optimisation (PSO) algorithm will be proposed for two different hybrid precoding structures, i.e. the fully connected and partially connected structures. First, the authors use the phase of the analogue precoding variable to replace the corresponding variable with the constraints of the unit norm to solve the non-convex constraints skillfully. Then they design analogue precoding by using PSO algorithm and compute the digital precoding based on the least squares solution. Finally, the proposed algorithms are compared with the existing advanced algorithms. Simulation results demonstrate that the proposed algorithms can approach the optimal performance under the corresponding structure.

Exploring AoA/AoD Dynamics in Beam Alignment of Mobile Millimeter Wave MIMO Systems

In this paper, we present a priori-aided beam alignment scheme for time-varying millimeter wave multiple-input multiple-output (MIMO) systems, where the temporal correlations of the angle of departure (AoD) and angle of arrival (AoA) of the propagation path are exploited. Specifically, the variations of AoD and AoA between two consecutive channel realizations are described by a set of transition probabilities, respectively, which characterize the temporal correlations. We aim to maximize the average successful estimation probability (ASEP) by jointly optimizing the selected transmitting–receiving training beam pairs and their associated transmit power. When the transition probabilities are known, this problem is formulated as a mixed integer nonlinear programming (MINLP). Due to the non-convex nature of the formulated MINLP, we propose a semi-exhaustive search algorithm (SESA) to obtain a suboptimal solution. Moreover, in order to achieve maximum ASEP, we prove that the training beam pairs corresponding to the directions which have higher transition probabilities should be used more times than those with lower transition probabilities. Numerical results show that the proposed joint optimization with the SESA can achieve a higher ASEP than the existing works.

Givens rotation based column-wise hybrid precoding for millimeter wave MIMO systems

Hybrid precoding is a promising technique for millimeter wave (mmWave) large-scale multiple-input multiple-output (MIMO) systems. This paper considers a hybrid precoding architecture which is composed of a reductive digital precoder and a phase-only control radio frequency (RF) precoder, and proposes a Givens rotation based column-wise hybrid precoding algorithm. In the proposed algorithm, the digital precoding matrix and the RF precoding matrix are alternatively optimized column by column. For the optimization of the constant modulus RF precoding vector, it is interesting that the optimal solution can be expressed as a product of an arbitrary constant modulus vector and a series of Givens rotation matrices. Accordingly, the original problem is transformed into an unconstrained problem of the Givens rotations, which is easy to be solved. After obtaining the optimal RF precoding vector, the optimal digital precoding vector can be given through the least squares method. Simulation results show that the proposed algorithm can approach the performance of the fully digital precoder effectively and enjoy a higher spectral efficiency than previous works under various channel environments and different system settings.

Distance Hardening in Large MIMO Systems

Asymptotic analysis plays an important role in the performance evaluation and technology design of large multiple-input multiple-output (MIMO) systems. This paper investigates an asymptotic property of large MIMO systems, called distance hardening . It involves distance metrics defined in terms of many factors, such as the channel matrix and the difference matrix of two arbitrary transmitted codewords. The asymptotic property is revealed by the use of the Chernoff bounding method and expressed in an analogous way to the well-known channel hardening. More specifically, we first derive Chernoff bounds on the tail probabilities of distance metrics, in which we are interested. Then, we formulate the asymptotic property based on a generalized definition of the hardening phenomena by referring to a formal definition of the channel hardening. The distance hardening is shown to have implications for space-time code design, MIMO detection, and millimeter wave MIMO systems. Numerical simulations are conducted to verify our theoretical development.

Spatially Sparse Beamforming Training for Millimeter Wave MIMO Systems

To realize high beamforming gain and thus sufficient link budget, millimeter wave (MMW) multiple-input multiple-output (MIMO) systems employ large antenna arrays at both the transmitter and receiver. However, due to the power and cost limitation, only a limited number of radio frequency (RF) chains are available for MMW antenna arrays. In other words, the number of RF chains is far smaller than that of antenna elements. This MMW MIMO setup poses a formidable challenge for channel estimation, which is conventionally required to realize the optimal singular value decomposition (SVD) beamforming. To circumvent the formidable channel estimation, this paper proposes two iterative antenna training schemes for the SVD beamforming in MMW MIMO systems. Relying on the channel reciprocity in time division duplex, the proposed schemes employ power iteration and Lanczos iteration, respectively, to gradually approach the SVD beamforming. During iterations, we only need to estimate several channel-related vectors instead of the MIMO channel matrix. Thanks to the spatial sparsity in MMW channels, the training overheads required by the proposed schemes are moderate. Simulations demonstrate that the proposed schemes outperform the counterpart in terms of performance and training overhead, and can achieve the performance very close to that of the perfect SVD beamforming.