Kronecker product-based codewords, constructed from 2D DFT bases, are fundamental to constructing Type I, Type II, and enhanced Type II codebooks in 5G New Radio (NR). While these codewords are conventionally interpreted as directed orthogonal beams, this paper reveals that a significant portion of these codebooks is associated with evanescent waves, rendering them redundant for practical array beamforming and channel representation. This redundancy is rigorously proven using mathematical and electromagnetic models and validated by full-waveform and system-level simulations. Leveraging this redundancy, we propose a method to compress these codebooks, typically reducing their size by 21.5%. This compression significantly decreases signaling and pilot overhead, enhancing the efficiency of channel state information feedback and beam training without adding algorithmic complexity. We also propose codebooks for irregular arrays that are compatible with existing NR feedback frameworks, potentially accelerating the standardization of irregular array deployment. Inspired by the revelation of codebook redundancy, we extend the discussions to the properties of near-field channel and classical Rayleigh channels, and offer practical suggestions for future standard designs.

Reconfigurable intelligent surface (RIS)-assisted cell-free (CF) multiple-input multiple-output (MIMO) networks can significantly enhance system performance. However, the extensive deployment of RIS elements imposes considerable channel acquisition overhead, with the high density of nodes and antennas in RIS-assisted CF networks amplifying this challenge. To tackle this issue, in this paper, we explore integrating RIS-user equipment (UE) association into downlink RIS-assisted CF transmitter design, which greatly reduces the channel acquisition costs. The key point is that once UEs are associated with specific RISs, there is no need to frequently acquire channels from non-associated RISs. Then, we formulate the problem of joint RIS-UE association and beamforming at APs and RISs to maximize the weighted sum rate (WSR). In particular, we propose a two-stage framework to solve it. In the first stage, we apply a many-to-many matching algorithm to establish the RIS-UE association. In the second stage, we introduce a sequential optimization-based method that decomposes the joint optimization of RIS phase shifts and AP beamforming into two distinct subproblems. To optimize the RIS phase shifts, we employ the majorization-minimization (MM) algorithm to obtain a semi-closed-form solution. For AP beamforming, we develop a joint block diagonalization algorithm, which yields a closed-form solution. Simulation results demonstrate the effectiveness of the proposed algorithm and show that, while RIS-UE association significantly reduces overhead, it incurs a minor performance loss that remains within an acceptable range. Additionally, we investigate the impact of RIS deployment and conclude that RISs exhibit enhanced performance when positioned between APs and UEs.

Channel state information (CSI) compression and feedback is a common way of acquiring the CSI at the transmitter in frequency division duplex (FDD) massive multiple-input multiple-output (mMIMO) systems due to the lack of channel reciprocity. However, implicit reciprocity potentially exists in the bi-directional channels of an FDD system because they in fact share physically the same propagation paths. We propose to leverage this implicit reciprocity in FDD mMIMO systems to minimize the feedback overhead and enhance the CSI recovery with uplink channel information at the transmitter. To achieve this, we develop a disentangled representation (DR) learning enabled neural network (NN), named DrCsiNet, to realize the selective CSI compression feedback with the assistance of uplink CSI. The proposed DrCsiNet successfully extracts the information of reciprocity implicitly shared between the downlink and uplink channels in FDD mMIMO, while it simultaneously extracts selective information from the downlink CSI excluding the implicit reciprocity component for compression feedback. We conduct extensive simulations to evaluate the performance of the proposed DrCsiNet against existing methods under various setups. Results demonstrate remarkable performance gains of DrCsiNet for CSI recovery and evidence a strong generalization ability across various network structures. These findings validate the efficacy of disentangling implicit CSI reciprocity embedded in uplink CSI for enhancing the downlink CSI recovery in FDD mMIMO.

In imperfect multiple-input multiple-output (MIMO) systems, model-based methods for performance prediction and optimization generally experience degradation in the dynamically changing environment with unknown interference and uncertain channel state information (CSI). To adapt to such challenging settings and better accomplish the network auto-tuning tasks, we propose a generic learnable model-driven framework. We further consider transmit regularized zero-forcing (RZF) precoding as a usage instance to illustrate the proposed framework. The overall process can be divided into three cascaded stages. First, we design a light neural network for refined prediction of sum rate based on coarse model-driven approximations. Then, the CSI uncertainty is estimated on the learned predictor in an iterative manner. In the last step the regularization term in the transmit RZF precoding is optimized. The effectiveness of the generic framework and the derivative method thereof is showcased via simulation results.

In New Radio (NR) massive multiple input multiple output (MIMO) system, accurate acquisition of downlink channel state information (CSI) at the base station (BS) is of great importance. However, the overhead of CSI feedback becomes larger with the increased number of BS antennas. In order to further reduce feedback overhead, deep learning (DL)-based approaches are recently proposed to solve this problem. In this paper, we propose a DL model using a neural network for implicit CSI feedback mechanism to compare the existing feedback method based on the eType II codebook introduced by the 3rd Generation Partnership Project (3GPP) Rel-16. Simulation results show that DL-based approach has higher performance gains in comparison with the standard method and shows great potential in future MIMO systems.

The problem of beam alignment and tracking in high mobility scenarios such as high-speed railway(HSR) becomes extremely challenging, since large overhead cost and significant time delay are introduced for fast time-varying channel estimation. To tackle this challenge, we propose a learning-aided beam prediction scheme for HSR networks, which predicts the beam directions and the channel amplitudes within a period of future time with fine time granularity, using a group of observations. Concretely, we transform the problem of high-dimensional beam prediction into a two-stage task, i.e., a low-dimensional parameter estimation and a cascaded hybrid beamforming operation. In the first stage, the location and speed of a certain terminal are estimated by maximum likelihood criterion, and a data-driven data fusion module is designed to improve the final estimation accuracy and robustness. Then, the probable future beam directions and channel amplitudes are predicted, based on the HSR scenario priors including deterministic trajectory, motion model, and channel model. Furthermore, we incorporate a learnable non-linear mapping module into the overall beam prediction to allow non-linear tracks. Both of the proposed learnable modules are model-based and have a good interpretability. Compared to the existing beam management scheme, the proposed beam prediction has (near) zero overhead cost and time delay. Simulation results verify the effectiveness of the proposed scheme.

Beam alignment and tracking for millimeter-wave communication networks in highly mobile scenarios, such as high-speed railway, suffer from large overhead cost and time delay loss. To solve this problem, we propose a learning-aided beam management scheme, which divides the high-dimensional beam prediction procedure into two stages, i.e., parameter estimation and hybrid beamforming. The locations and velocities of the mobile terminals are estimated using the maximum likelihood criterion, and a data fusion module is employed to further improve the estimation accuracy and robustness. Then, the next probable beam directions and the corresponding hybrid precoders are derived based on the estimated parameter set. Numerical simulations show that, the proposed method yields significantly lower overhead cost and time delay compared to the existing beam management scheme.

We study the precoding for the high-rank MIMO in the LTE-A Pro systems. Unlike the low-rank precoding, layer mapping has a relatively large impact on the performance for high-rank precoding. First, we construct a model on the relationship between layer mapping and precoding. Then we derive the system throughput with the resulting model, so that the performance of the layer mapping and precoding can be evaluated. Further, we operate a sub-space optimization for the codebook-based precoding to maximize the system throughput. Simulation results show that after optimizing layer mapping, high-rank precoding achieves much better performance.

To fully exploit the spatial multiplexing gains and array gains of massive multiple-input-multiple-output (MIMO), the channel state information must be obtained accurately at the transmitter side (CSIT). However, conventional channel estimation solutions are not suitable for Frequency-Division Duplexing (FDD) multi-user massive MIMO systems, due to overwhelming pilot and feedback overhead. In this paper, We find that part of the user channels tend to exhibit an approximate beam-blocked sparsity. To exploit this property, we propose a novel blocked compressive channel estimation scheme based on user grouping to reduce the pilot and feedback overhead. More specifically, we adopt user grouping by making the users in one group have similar channel covariance, which makes the channels in one group exhibit beam block sparsity. Then users feed the compressed measurements back to BS and the BS performs the CSIT recovery. Using the beam block sparsity, an optimal block orthogonal matching pursuit algorithm (OBOMP) is developed which effectively recovers the channel parameters. Numerous simulation results demonstrate our proposed scheme outperforms conventional solutions.

Massive MIMO is a promising technique to provide high data rate with good energy efficiency for the future wireless cellular communication. However, its performance benefit often can be realized only when accurate channel state information (CSI) is available at the transmitter to perform accurate beamforming. With large number of antennas, full CSI consumes too much overhead to feed back without compression. To reduce CSI feedback overhead, CSI feedback scheme with dual stage precoding structure is designed to quantize the long term spatial channel correlation information and short term linear precoder information. In this paper, we discuss how to optimize this dual stage precoding scheme in the typical dual-polarized massive MIMO system. The eigenvalues of spatial correlation matrix are used to improve feedback efficiency. By relaxing the constant modulus constraint in codebook design, more flexible long term precoding can be used and adapt to the channel. A specific structure of long term precoding matrix for dual-polarized MIMO system is proposed to ensure the orthogonality of the final precoder for multi-layer transmission.

Massive MIMO is one of the promising techniques to improve the spectral efficiency and network performance in future 5G networks. Compared to FDD, it is relatively easier to realize downlink massive MIMO for TDD as downlink channel information can be obtained via uplink-downlink channel reciprocity. This paper provides our field test results of massive MIMO system with a base station prototype equipped with 64 transmit antennas. Significant throughput gain is observed by performing 3D-beamforming to current LTE-Advanced handsets by using standard-transparent Multiuser(MU) MIMO techniques. With the massive MIMO base station prototype, MU-MIMO is realized by multiplexing maximum of eight handsets in spatial domain considering both azimuth and elevation directions. In addition to the field trial test results, future potential enhancements for TDD massive MIMO system are discussed. Evaluation results of evaluating some enhancements on uplink reference signal are provided.

For massive MIMO system with hundreds of antennas at the base station (BS), zero forcing (ZF) precoding can achieve the near-optimal capacity due to the asymptotically orthogonal channel, but it involves complicated matrix inversion of large size. In this paper, we propose a Richardson Method (RM) based precoding to avoid the complicated matrix inversion in an iterative way, which can reduce the complexity by one order of magnitude. We also prove that the optimal relaxation parameter to RM can be approached by a simple and quantified value to maximize the convergence rate of RM-based precoding, which only depends on the number of BS antennas and the number of users. Simulation results show that RM-based precoding can achieve the near-optimal performance of ZF precoding with only a small number of iterations.

Massive multiple-input multiple-output (MIMO) using a large number of antennas at the base station (BS) is a promising technique for the next-generation 5G wireless communications. It has been shown that linear precoding schemes can achieve near-optimal performance in massive MIMO systems. However, classical linear precoding schemes such as zero- forcing (ZF) precoding suffer from high complexity due to the fact they require the matrix inversion of a large size. In this paper, we propose a low-complexity precoding scheme based on the least square QR (LSQR) method to realize the near-optimal performance of ZF precoding without matrix inversion. We show that the proposed LSQR-based precoding can reduce the complexity of ZF precoding by about one order of magnitude. Simulation results verify that the proposed LSQR-based precoding can provide a better tradeoff between complexity and performance than the recently proposed Neumann-based precoding.

In this paper, we study the achievable uplink rates of massive multiple-input multiple-output (MIMO) systems using maximal-ratio combining (MRC) and zero-forcing (ZF) receivers, assuming imperfect channel state information (CSI). Unlike all previous studies, the fast fading MIMO channel matrix here is modeled to have an arbitrary-rank deterministic component as well as a Rayleigh-distributed random component. In particular, it is found that with a non-zero Ricean K-factor, the approximations and the exact uplink rates converge to the same constant value if the number of base station antennas, M, grows large, while the transmit power of each user is scaled down proportionally to 1/M. However, if the channel is Rayleigh fading, we can only cut the transmit power of each user proportionally to 1/√M. In addition, we show that with increasing Ricean K-factor, the uplink rates will converge to fixed values for both MRC and ZF receivers.

In this paper, we address the popular content distribution (PCD) problem in a highway scenario, in which popular files are distributed to a group of on-board units (OBUs) driving through a single roadside unit (RSU). Due to the high speeds, the OBUs may not finish downloading a large file within the limited time for vehicle-to-roadside (V2R) communication and a peer-to-peer (P2P) network consisting of OBUs out of the RSU coverage can be constructed for completing the file delivery process. However, due to fast and unpredictable topological changes of the vehicular ad hoc network (VANET), the static methods in traditional P2P networks can be inefficient. We model this problem as a coalition formation game with transferable utilities, and propose a coalition formation algorithm that converges into a Nash-stable partition adapting to environmental changes. Based on this algorithm, we further propose a distributed scheme for the overall PCD problem. Simulation results show that our scheme presents a considerable performance improvement relative to the non-cooperative case using the carrier sense multiple access with collision avoidance (CSMA/CA).

In this paper we analyze the performance of a small cell network where the locations of the base stations are generated according to Poisson distribution and linear multiuser precoding is employed. The performance metrics of both the overall outage probability (OOP) and the symbol error probability (SEP) are investigated for this small cell network. Tight closed-form expressions for the OOP and the average SEP are derived, and an asymptotic approximation to the OOP and the average SEP are also obtained, respectively. Their accuracy are validated via our numerical results. In addition, we propose a new access mechanism to maximize the energy efficiency and further evaluate its performance analytically. Both theoretical and numerical results show that the proposed scheme could effectively improve the efficiency of the small cell heterogeneous network.

In this paper, we study the problem of cooperative interference management in an OFDMA two-tier small cell network. In particular, we propose a new approach for allowing the small cells to cooperate, so as to optimize their sum-rate, while cooperatively satisfying their maximum transmit power constraints. Unlike existing works which assume that only disjoint groups of cooperative small cells can emerge, we formulate the small cells' cooperation problem as an overlapping coalition formation game. In this game, each small cell base station can choose to participate in one or more cooperative groups (or coalitions) simultaneously, so as to optimize the tradeoff between the benefits and costs associated with cooperation. We study the properties of the proposed game and we show that it exhibits negative externalities due to interference. Then, we propose a novel decentralized algorithm that allows the small cell base stations to interact and self-organize into a stable overlapping coalitional structure. Simulation results show that the proposed algorithm results in a notable performance advantage in terms of the total system sum-rate, relative to the noncooperative case and the classical algorithms for coalitional games with non-overlapping coalitions.

Existing minimum-symbol-error-rate equalizers were derived based on the symbol-error-rate objective function. Due to the complexity of the objective function the derivation is not straightforward. In this paper we present a new approach to derive the minimum-symbol-error-rate adaptive equalizers. The problem is formulated as minimizing the norm between two subsequent parameter vectors under the constraint of symbol-error-rate minimization. The constrained optimization problem then is solved with the Lagrange multiplier method, which results in an adaptive algorithm with normalization. Simulation results show that the proposed algorithm outperforms the existing adaptive minimum-symbol-error-rate equalizer in convergence speed and steady-state performance.

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