Utilizing Partial Reciprocity to Efficient Codebook Design for FDD Massive MIMO Systems

MIMO (Multiple Output Multiple Input) systems employ both transmitting and receiving arrays and space-time coding methods in order to achieve significantly greater channel capacities. Since the data rate possible for digital HF systems is generally comparatively low, it would be very useful if the capacity improvements inherent in MIMO techniques could be exploited at HF. In order to assess this possibility and in order to gain some insight into the optimum design of HF MIMO systems, the characteristics of the digital HF channel are explored in relation to this utilization. To accomplish this a wideband HF simulator, based on a physical model and including time-varying electron density irregularities is employed and used to assess correlation of the received multi-component signal at spaced antennas for different multipath scenarios and ionosphere parameters. The optimum parameters and scenarios for employment of HF MIMO systems are then discussed.

Massive MIMO systems achieve high sum spectral efficiency by simultaneously serving large numbers of users. In time division duplexing systems, however, the reuse of uplink training pilots among cells results in channel estimation errors, which causes downlink inter-cell interference. Handling this interference is challenging due to the large channel dimensionality and the high complexity associated with implementing large precoding/combining matrices. In this paper, we propose multi-layer precoding to enable efficient and low-complexity operation in full-dimensional massive MIMO, where a large number of antennas are used in two dimensions. In multi-layer precoding, the precoding matrix of each base station is written as a product of a number of precoding matrices. Multi-layer precoding: 1) leverages the directional characteristics of large-scale MIMO channels to manage inter-cell interference with low channel knowledge requirements and 2) allows for an efficient implementation using hybrid analog/digital architectures. We present and analyze a specific multi-layer precoding design for full-dimensional massive MIMO systems. The asymptotic optimality of the proposed design is then proved for some special yet important channels. Numerical simulations verify the analytical results and illustrate the potential gains of multi-layer precoding compared with other multi-cell precoding solutions.

Multi-user multiple input multiple output (MU-MIMO) system is an excellent choice for the next generation broadband communications because of its great potential in enhancing MIMO system capacity. One major challenge of such system is the jointly optimal transceiver beamforming design that maximizes the sum capacity under a total power constraint. Existing approaches have provided convex optimization solution by exploiting the duality between transmitter and receiver. However, with high computation complexity, these approaches are ineligible of practical implementation. In this paper, we aim to provide complexity efficient transceiver beamformer and power allocation design in MU-MIMO downlink (broadcast) channels. We first develop an iterative sub-optimal algorithm based on cyclic self-SINR-maximization (CSSM) beamforming design and water-filling power allocation. Comparing to convex-optimization-based (COB) approaches, the proposed solution (i.e. CSSM-WF algorithm) have insignificant sum-rate degradation but very low computational complexity and extremely fast converging speed. Trying to improve the performance of CSSM-WF algorithm, we then introduce another algorithm denoted as efficient COB (ECOB) algorithm in which CSSM-WF algorithm is used to generate a good start point for optimal COB approaches. Simulation results prove the efficiency of both proposed algorithms.

Large number of antennas and radio frequency (RF) chains at the base stations (BSs) lead to high energy consumption in massive MIMO systems. Thus, how to improve the energy efficiency (EE) with a computationally efficient approach is a significant challenge in the design of massive MIMO systems. With this motivation, a learning-based stochastic gradient descent algorithm is proposed in this article to obtain the optimal joint uplink and downlink EE with joint antenna selection and user scheduling in single-cell massive MIMO systems. Using Jensen's inequality and the characteristics of wireless channels, a lower bound on the system throughput is obtained. Subsequently, incorporating the power consumption model, the corresponding lower bound on the EE of the system is identified. Finally, learning-based stochastic gradient descent method is used to solve the joint antenna selection and user scheduling problem, which is a combinatorial optimization problem. Rare event simulation is embedded in the learning-based stochastic gradient descent method to generate samples with very small probabilities. In the analysis, both perfect and imperfect channel side information (CSI) at the BS are considered. Minimum mean-square error (MMSE) channel estimation is employed in the study of the imperfect CSI case. In addition, the effect of a constraint on the number of available RF chains in massive MIMO system is investigated considering both perfect and imperfect CSI at the BS.

With the growing need for Internet of things (IoT) applications, wireless communications are facing the challenge of providing massive connectivity. However, as the number of users increases, the capacity of massive MIMO systems will be severely affected due to the huge channel estimation overhead. Therefore it is necessary to introduce non-orthogonal pilots to massive MIMO systems in order to ease the burden on the resources for channel estimation. In this paper, we propose a KKT-basediterativenon-orthogonalpilotdesignalgorithmwhich maximizes the mutual information between the received signal and the channel for MMSE channel estimation. For the typical antenna deployment of uniform linear array (ULA), we also provide scalable estimation schemes with low complexity based onchannelangularrepresentationasalternatives.Thesimulation results show that our schemes significantly save the estimation overhead at the cost of slight decrease in the MSE performance of the channel estimation, and the saving is even more prominent when the system accommodates more users.

Through utilizing the technology of compressive sensing (CS), the channel estimation methods can achieve the purpose of reducing pilots and improving spectrum efficiency. The channel estimation and pilot design scheme are explored during the correspondence under the help of block-structured CS in massive MIMO systems. The block coherence property of the aggregate system matrix can be minimized so that the pilot design scheme based on stochastic search is proposed. Moreover, the block sparsity adaptive matching pursuit (BSAMP) algorithm under the common sparsity model is proposed so that the channel estimation can be caught precisely. Simulation results are to be proved the proposed design algorithm with superimposed pilots design and the BSAMP algorithm can provide better channel estimation than existing methods.

We consider a hybrid combiner design for downlink massive multiple-input multiple-output systems when there is residual inter-user interference and each user is equipped with a limited number of radio frequency (RF) chains (less than the number of receive antennas). We propose a hybrid combiner that minimizes the mean-squared error (MSE) between the information symbols and the ones estimated with a constant amplitude constraint on the RF combiner. In the proposed scheme, an iterative alternating optimization method is utilized. At each iteration, one of the analog RF and digital baseband combining matrices is updated to minimize the MSE by fixing the other matrix without considering the constant amplitude constraint. Then, the other matrix is updated by changing the roles of the two matrices. Each element in the RF combining matrix is obtained from the phase component of the solution matrix of the optimization problem for the RF combining matrix. Simulation results show that the proposed scheme performs better than conventional matrix-decomposition schemes.

Adaptive control design using stability analysis and tracking errors dynamics for nonlinear square MIMO systems

In the intelligent reflecting surface (IRS)-assisted MIMO systems, optimizing the passive beamforming of the IRS to maximize spectral efficiency is crucial. However, due to the unit-modulus constraint of the IRS, the design of an optimal passive beamforming solution becomes a challenging task. The feature input of existing schemes often neglects to exploit channel state information (CSI), and all input data are treated equally in the network, which cannot effectively pay attention to the key information and features in the input. Also, these schemes usually have high complexity and computational cost. To address these issues, an effective three-channel data input structure is utilized, and an attention mechanism-assisted unsupervised learning scheme is proposed on this basis, which can better exploit CSI. It can also better exploit CSI by increasing the weight of key information in the input data to enhance the expression and generalization ability of the network. The simulation results show that compared with the existing schemes, the proposed scheme can effectively improve the spectrum efficiency, reduce the computational complexity, and converge quickly.

This paper has aimed to design fractional-order sliding mode controller (FO-SM) for time delayed MIMO system. The proposed controller has many uncertain parameters so we used adaptive particle swarm optimization method to determine the parameters. The controller provides faster tracking and more stable structure than the integer order. The selection of the controller parameters is important for control performance so intelligent optimization can provide optimal control parameters. Simulation results show that FO-SM controller has robust control performance through change of time delay.

In this paper, we investigate an anti-jamming hybrid beamforming (HBF) design in millimeter-wave (mmWave) massive MIMO systems for reliable wireless communications. Different from the conventional schemes designed by assuming perfect channel state information (CSI) of both communication channels and jamming channels. We aim to design the HBF scheme by minimizing the dispersion of the output signals in statistics without the knowledge of the jamming channel information. Two partially-connected HBF architectures are considered. Specifically, we propose a two-stage robust HBF design by solving the formulated non-convex problem. First, we introduce an auxiliary vector that represents the product of an analog beamformer (ABF) and a digital beamformer (DBF), and propose a pseudo-Newton method assisted beamforming algorithm by relaxing the non-convex constraint on the ABF. In the second stage, we separately optimize the ABF and DBF for two different partially-connected HBF architectures by proposing a matrix decomposition-based alternating minimization method. Finally, simulation results are provided for demonstrating the superiority of our proposed robust HBF schemes over other benchmark schemes by considering the security threat of potential jamming attacks, especially when the number of snapshots is small.

In this paper, a 2-layer rate-splitting multiple access (RSMA) framework with a balanced clustering design is proposed for cell-free massive multiple-input multiple-output (CF-mMIMO) systems. Aiming at enhancing spectral efficiency (SE) and meanwhile ensuring low-complexity and scalability in large-scale systems, this work addresses different types of multi-user interferences by utilizing a two-stage optimization scheme with a designed spatial reduction matrix that encompasses the clustering design and joint RSMA. First, a balanced clustering design is developed for 2-layer RSMA to manage intra-and inter-cluster interference more efficiently than conventional user-centric clustering, simultaneously considering AP-user connectivity in CF-mMIMO systems and the impact of cluster similarity on RSMA. By employing the spectral clustering method to solve the bipartite graph partitioning problem with the min-max cut objective, the number of clusters is determined adaptively. Based on the above clustering design, a joint optimization of inner and outer RSMA is proposed to mitigate intra-and inter-cluster interferences simultaneously. Simulation results verify the SE enhancement, low-complexity, and scalability of the proposed 2-layer RSMA framework, compared with benchmark frameworks.

Spectral efficiency of hybrid precoding and combining design for mm-Wave multi-user massive MIMO systems

This paper proposes a novel resource allocation scheme for optimizing the downlink of a user-centric cellfree massive multiple-input multiple-output (MIMO) system. The proposed approach aims to optimize the number of users served by each access point based on channel conditions while adapting to variable packet error rate and modulation and coding schemes. To enhance the received signal-to-noise plus interference ratio, the authors use a precoding design approach called the local protective partial zero-forcing that categorizes users based on their channel gain. The problem is formulated as a joint optimization of user assignment, resource allocation, and the precoding design. Closed-form expressions for the data rate are derived, and a new algorithm for resource allocation is introduced that outperforms several different scenarios while keeping the computational complexity reasonable. Compared to fixed parameter schemes, the proposed approach provides an optimal selection of the number of users for each access point and has the potential to significantly improve the system throughput, making it a novel and impactful solution for the practical implementation of user-centric cell-free massive MIMO systems.

This paper develops a framework to analyze the outage capacity of multiuser diversity in a multiuser MIMO transmit antenna selection with maximal-ratio combining (TAS/MRC) system. A closed-form expression of the system outage capacity is derived, through which we show how and with what characteristics the MIMO configurations, the number of users and the multiuser diversity affect the outage capacity performance. Both the analytical and simulation results show that (1) the outage capacity increases with the increase of mean of effective average SNR and decreases with the increase of variance of the effective average SNR; (2) to obtain a higher outage capacity, the number of receive antennas should be no more than that of transmit antennas. This paper has presented some conclusions which can be used for the efficient design of multiuser MIMO systems.