The asymmetrical uplink and downlink transceiver architecture has emerged as a promising solution to reduce hardware cost and complexity in massive multiple-input multiple-output (MIMO) systems, especially under scenarios with dense antenna deployments, such as uniform planar arrays (UPAs). However, accurate full-dimensional channel state information (CSI) recovery becomes more challenging than uniform linear array (ULA) scenarios due to the significantly reduced number of radio frequency (RF) chains. Directly extending the ULA channel recovery method into UPAs will not only result in high computational complexity but also introduce angle estimation ambiguity owing to the extra vertical array dimension. To address these challenges, we propose a channel recovery framework for UPA-assisted massive MIMO systems with asymmetrical transceiver architectures. First, we introduce the concept of the mixed angle to deal with the low elevation angular resolution originating from the compact array form, and a virtual array is then constructed based on the mixed angle via the spatial correlation matrix. After that, an antenna selection algorithm is designed to maximize the virtual array aperture with a minimal number of RF chains, and a low-complexity UPA-based modified newtonized orthogonal matching pursuit (UPA-based mNOMP) channel recovery algorithm is developed to enable accurate full-dimensional CSI reconstruction. Finally, the imperfect spatial correlation matrix is considered and a orthogonal rank-one matrix pursuit-based spatial correlation matrix recovery algorithm is proposed to recover the spatial correlation matrix from its spatial sparse measurements by exploiting the low-rank property of massive MIMO channels. Simulation results validate the superiority of the proposed algorithms in achieving excellent full-dimensional channel recovery performance for asymmetrical transceiver-based massive MIMO systems with UPAs.

The mid-band frequency range, combined with extra large-scale multiple-input multiple-output (XL-MIMO), is emerging as a key enabler for future communication systems. By exploiting the advent of new spectrum resources and degrees of freedom brought by the near-field propagation, the mid-band XL-MIMO system is expected to significantly enhance throughput and inherently support advanced functionalities such as integrated sensing and communication. Although theoretical studies have highlighted the benefits of mid-band XL-MIMO systems, the promised performance gains have yet to be validated in practical systems, posing a major challenge to the standardization. In this paper, preliminaries including frame structure, channel modeling, and signal models are first discussed, followed by an analysis of key challenges in constructing a real-time prototype system. Subsequently, the design and implementation of a real-time mid-band XL-MIMO prototype system are presented. Underpinned by a novel architecture, the proposed prototype system supports specifications aligned with standardization, including a bandwidth of 200 MHz, up to 1024 antenna elements, and up to 256 transceiver chains. Operating in time-division duplexing mode, the prototype enables multiuser communication for up to 12 users, while retaining standard communication procedures. Built on hybrid software-defined radio and field programmable gate array platforms, the prototype is programmable and allows for flexible deployment of advanced algorithms. Moreover, the modular architecture ensures high scalability, making the prototype adaptable to various configurations, including distributed deployments and decentralized signal processing. Experimental results demonstrate that the prototype handles real-time digital sample processing at 1453.33 Gbps and achieves a peak data throughput of 15.81 Gbps for 12 users.

The asymmetrical transceiver architecture has shown potential in reducing hardware complexity and cost in massive multiple-input multiple-output (MIMO) systems. However, channel recovery is necessary for the asymmetrical transceiver to acquire excellent transmission performance. Although the uniform planar array (UPA) is widely deployed in practical systems, the compact form of UPAs will result in high computational complexity due to the inherent high-dimensional nature of the array. The angle ambiguity problem also arises when directly extending the uniform linear array channel recovery method into the UPAs. To address these challenges, we propose a low-complexity gridless channel recovery method in this paper. First, the concept of the mixed angle for UPAs is introduced to deal with the low elevation angular resolution originating from the compact array form. After that, an antenna selection algorithm is designed to construct a virtual array based on the mixed angle to maximize the virtual array aperture with a minimal number of antennas. Finally, a low-complexity UPAbased modified newtonized orthogonal matching pursuit channel recovery algorithm is developed to mitigate angle ambiguity, thus enabling accurate reconstruction of the full downlink channel state information. Numerical results demonstrate the superiority of the proposed method in significantly reducing the number of receive uplink radio frequency chains while ensuring satisfactory channel recovery performance.

In this article, the Bayesian estimator and detector are proposed in the scenario of massive communication. The ultra massive multiple-input-multiple-output (MIMO) is established at the base station (BS), which is communicated with a huge number of online devices in the near field. In order to estimate the uplink channel responses, the novel nonorthogonal pilot sequences are designed and the principle of turbo decoding is applied. Then, the sparse estimation of extra large-scale channel state information (CSI) is performed depending on the extrinsic information transferring in the spatial domain and angular domain. Besides, the mixed analog-to-digital converter (ADC) architecture is considered to accomplish the linear and nonlinear measurements. Based on this framework, the tradeoff between the system performance and hardware overhead can be achieved. Additionally, a submodule-based segmentation technique is addressed to eliminate the energy spreading phenomenon caused by the near filed effects of the ultra massive MIMO. Specifically, we also analyze the theoretical statistical result of sparse channel estimation and device activity detection using the state evolution method. Furthermore, several engineering implementation strategies are provided to enhance the efficiency improvements in the practical system of massive communication. Numerical simulation results demonstrate that the satisfactory performance of estimation/detection is beyond other methods in terms of hardware costs and computational complexity in the extra large Internet of Things (IoT) network.

In the domain of reconfigurable intelligent surface (RIS)-assisted wireless communications, efficient channel state information (CSI) feedback is crucial. This paper proposes RIS-CoCsiNet, a novel deep learning-based framework aimed at significantly enhancing feedback efficiency. The proposed method leverages the inherent correlation among neighboring user equipments (UEs) by categorizing RIS-UE CSI information into two parts: shared information among nearby UEs and unique information specific to each individual UE. By exploiting the correlation in RIS-UE CSI, redundant transmission of shared information can be substantially reduced, thereby minimizing the overhead associated with repeatedly feeding back this shared data. Unlike conventional autoencoder-based CSI feedback frameworks, our approach incorporates an additional decoder and a combination neural network (NN) at the base station. These components recover the shared information from the feedback CSI of two neighboring UEs and combine it with the individual information, respectively, without requiring any modifications at the UEs. Through end-to-end learning, the encoders at neighboring UEs are trained to collaboratively feedback shared information while independently feeding back the unique information. For UEs equipped with multiple antennas, a baseline NN architecture with long short-term memory (LSTM) modules is introduced to capture the correlation among nearby antennas. Additionally, since the RIS-UE CSI phase is not sparse, we propose magnitude-dependent phase feedback strategies that incorporate statistical or instantaneous CSI magnitude information into the phase feedback process. Extensive simulations across two diverse channel datasets validate the effectiveness of RIS-CoCsiNet.

In time division duplexing (TDD) millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, downlink channel state information (CSI) can be obtained from uplink channel estimation thanks to channel reciprocity. However, under high-mobility scenarios, frequent uplink channel estimation is needed due to channel aging. Additionally, large amounts of antennas and subcarriers result in high-dimensional CSI matrices, aggravating pilot training overhead. To address this, we propose a three-domain (3D) channel extrapolation framework across spatial, frequency, and temporal domains. First, considering the effectiveness of traditional knowledge-driven channel estimation methods and the marginal effects of pilots in the spatial and frequency domains, a knowledge-and-data driven spatial-frequency channel extrapolation network (KDD-SFCEN) is proposed for uplink channel estimation via joint spatial-frequency channel extrapolation to reduce spatial-frequency domain pilot overhead. Then, leveraging channel reciprocity and temporal dependencies, we propose a temporal uplink-downlink channel extrapolation network (TUDCEN) powered by generative artificial intelligence for slot-level channel extrapolation, aiming to reduce the tremendous temporal domain pilot overhead caused by high mobility. Numerical results demonstrate the superiority of the proposed framework in significantly reducing the pilot training overhead by 16 times and improving the system’s spectral efficiency under high-mobility scenarios compared with state-of-the-art channel estimation/extrapolation methods.

Ultra-reliable low-latency communication is the key technology for smart factories and autonomous vehicles. However, traditional beam training approaches in millimeter-wave communications generally cause significant latency and communication overhead, especially in the case of multi-user communications. To tackle this problem, we propose a novel Vision-aided Multi-user Beam Tracking (VA-MUBT) framework for mmWave massive MIMO system, which leverages deep learning based visual object detection and multiple objects tracking algorithm to enable fast beam tracking of multi-user. In addition, a prototype is constructed to evaluate the proposed VA-MUBT framework and the experimental results based on this prototype show that the accuracy of 3-time beam search can reach near 90% with only 8% overhead of the exhaustive beam search method. Hence, the proposed VA-MUBT demonstrates the superiority in achieving fast multi-user beam tracking and significantly reducing the communication overhead.

Distributed Antenna Systems (DASs) employ multiple antenna arrays in remote radio units to achieve highly directional transmission and provide great coverage performance for future-generation networks. However, the utilization of fully digital or hybrid active antenna arrays results in a significant increase in hardware costs and power consumption for DAS. To address these issues, integrating DAS with Reconfigurable Intelligent Surfaces (RIS) offers a viable approach to ensure coverage and transmission performance while maintaining low hardware costs and power consumption. To incorporate the merits of RIS into the DAS from practical consideration, a novel architecture of “Reconfigurable Distributed Antennas and Reflecting Surfaces (RDARS)” is proposed in this paper. Specifically, based on the design of the additional direct-through state together with the existing high-quality fronthaul link, any element of the RDARS can be dynamically programmed to connect with the base station (BS) via fibers and perform theconnected modeas remote distributed antennas of the BS to receive or transmit signals. Additionally, RDARS also inherits the low-cost and low-energy-consumption benefits of fully passive RISs by default configuring the elements as passive to perform thereflection mode. As a result, RDARS encompasses both DAS and RIS as special cases, offering flexible control over the trade-off betweendistribution gainandreflection gainto enhance performance. To unveil the potential of such architecture, the ergodic achievable rate under the RDARS architecture is analyzed and closed-form expression with meaningful insights is derived. The theoretical analysis proves that the RDARS can achieve a higher achievable rate than both DAS and fully passive RIS with the passive beamforming gain provided by elements actingreflection modewhile combating the “multiplicative fading” suffered by RISs through theconnected modeperformed at the RDARS. Simulation results also demonstrate the superiority of the RDARS architecture over DAS and passive RIS-aided systems and its flexible trade-off between performance and cost. To further validate the feasibility and effectiveness, an RDARS prototype with 256 elements is built for real experiments. Experimental results show that the RDARS-aided system with only one element operating inconnected modecan achieve an additional 21% and 170% throughput improvement over DAS and RIS-aided systems, respectively.

In this paper, we investigate a novel reconfigurable distributed antennas and reflecting surface (RDARS) aided multi-user massive multiple-input multiple-output (MIMO) system with imperfect channel state information (CSI) and propose a practical two-timescale (TTS) transceiver design to reduce the communication overhead and computational complexity of the system. In the RDARS-aided system, not only distribution gain but also reflection gain can be obtained by a flexible combination of the distributed antennas and reflecting surface, which differentiates the system from the others and also makes the TTS design challenging. To enable the optimal TTS transceiver design, the achievable rate of the system is first derived in closed-form. The rate expression is general and covers that of the distributed antenna systems (DAS) and reconfigurable intelligent surface (RIS) aided systems as special cases. Then the TTS design aiming at the weighted sum rate maximization is considered. To solve the challenging non-convex optimization problem with high order design variables, i.e., the transmit powers and the phase shifts at the RDARS, a block coordinate descent based method is proposed to find the optimal solutions in semi-closed forms iteratively. Specifically, two efficient algorithms are proposed with provable convergence for the optimal phase shift design, i.e., Riemannian Gradient Ascent based algorithm by exploiting the unit-modulus constraints, and Two-Tier Majorization-Minimization based algorithm with closed-form optimal solutions in each iteration. Simulation results validate the effectiveness of the proposed algorithm and demonstrate the superiority of deploying RDARS in massive MIMO systems to provide substantial rate improvement with a significantly reduced total number of active antennas/RF chains and lower transmit power when compared to the DAS and RIS-aided systems.

Reconfigurable intelligent surface (RIS) has emerged as a cost-effective solution for green communications in 6G. However, its further extensive use has been greatly limited due to its fully passive characteristics. Considering the appealing distribution gains of distributed antenna systems (DAS), a flexible reconfigurable architecture called reconfigurable distributed antenna and reflecting surface (RDARS) is proposed. RDARS encompasses DAS and RIS as two special cases and maintains the advantages of distributed antennas while reducing the hardware cost by replacing some active antennas with low-cost passive reflecting surfaces. In this paper, we present a RDARS-aided uplink multi-user communication system and investigate the system transmission reliability with the newly proposed architecture. Specifically, in addition to the distribution gain and the reflection gain provided by the connection and reflection modes, respectively, we also consider the dynamic mode switching of each element which introduces an additional degree of freedom (DoF) and thus results in a selection gain. As such, we aim to minimize the total sum mean-square-error (MSE) of all data streams by jointly optimizing the receive beamforming matrix, the reflection phase shifts and the channel-aware placement of elements in the connection mode. To tackle this nonconvex problem with intractable binary and cardinality constraints, we propose an inexact block coordinate descent (BCD) based penalty dual decomposition (PDD) algorithm with the guaranteed convergence. Since the PDD algorithm usually suffers from high computational complexity, a low-complexity greedy-search-based alternating optimization (AO) algorithm is developed to yield a semi-closed-form solution with acceptable performance. Numerical results demonstrate the superiority of the proposed architecture compared to the conventional fully passive RIS or DAS. Furthermore, some insights about the practical implementation of RDARS are provided.

To reap the promising benefits of massive multiple-input multiple-output (MIMO) systems, accurate channel state information (CSI) is required through channel estimation. However, due to the complicated wireless propagation environment and large-scale antenna arrays, precise channel estimation for massive MIMO systems is significantly challenging and costs an enormous training overhead. Considerable time-frequency resources are consumed to acquire sufficient accuracy of CSI, which thus severely degrades systems’ spectral and energy efficiencies. In this paper, we propose a dual-attention-based channel estimation network (DACEN) to realize accurate channel estimation via low-density pilots, by jointly learning the spatial-temporal domain features of massive MIMO channels with the temporal attention module and the spatial attention module. To further improve the estimation accuracy, we propose a parameter-instance transfer learning approach to transfer the channel knowledge learned from the high-density pilots pre-acquired during the training dataset collection period. Experimental results reveal that the proposed DACEN-based method achieves better channel estimation performance than the existing methods under various pilot-density settings and signal-to-noise ratios. Additionally, with the proposed parameter-instance transfer learning approach, the DACEN-based method achieves additional performance gain, thereby further demonstrating the effectiveness and superiority of the proposed method.

Accurate channel state information (CSI) is essential for downlink precoding in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems with orthogonal frequency-division multiplexing (OFDM). However, obtaining CSI through feedback from the user equipment (UE) becomes challenging with the increasing scale of antennas and subcarriers and leads to extremely high CSI feedback overhead. Deep learning-based methods have emerged for compressing CSI but these methods generally require substantial collected samples and thus pose practical challenges. Moreover, existing deep learning methods also suffer from dramatically growing feedback overhead owing to their focus on full-dimensional CSI feedback. To address these issues, we propose a low-overhead Incorporation-Extrapolation based Few-Shot CSI feedback Framework (IEFSF) for massive MIMO systems. An incorporation-extrapolation scheme for eigenvector-based CSI feedback is proposed to reduce the feedback overhead. Then, to alleviate the necessity of extensive collected samples and enable few-shot CSI feedback, we further propose a knowledge-driven data augmentation (KDDA) method and an artificial intelligence-generated content (AIGC) -based data augmentation method by exploiting the domain knowledge of wireless channels and by exploiting a novel generative model, respectively. Experimental results based on the DeepMIMO dataset demonstrate that the proposed IEFSF significantly reduces CSI feedback overhead by 64 times compared with existing methods while maintaining higher feedback accuracy using only several hundred collected samples.

This paper studies the reconfigurable intelligent surface (RIS)-enhanced channel estimation and device activity detection technique for the next generation massive internet of things (IoT) networks. Thanks to its low cost, RIS can be introduced into massive IoT networks to extend the area coverage and support more online devices. However, introducing RIS also brings new challenges in channel estimation and device detection for massive connectivity systems owning to the resulting cascaded channel and its inherent passive characteristics. To address this issue, we first formulate the RIS-aided channel estimation and device activity detection as a joint sparse signal recovery problem by simultaneously exploring the sparsity of sporadic transmission and RIS-aided channel links. After that, an RIS-aided generalized Turbo multiple measurement vector algorithm to estimate the channels between the devices and the base station, and detect the active devices jointly under different channel distributions, i.e., the Bernoulli Gaussian scale mixture distribution and the Bernoulli Gaussian approximation distribution. Furthermore, we analyze the state evolution equations of the proposed channel estimation technique and the theoretical detection results from the perspective of missing detection and false alarm probabilities are also provided. Numerical results confirm the correctness of the theoretical analysis, and show that RIS is beneficial for improving the mean square error performance of the channel estimators, as well as the active device detection performance of detectors in massive connectivity systems.

Massive multiple-input multiple-output (MIMO) systems have suffered from extremely high hardware complexity and cost because of the introduction of a tremendous number of antennas. Recently, one way to alleviate this is by considering the unequal uplink and downlink data transmission requirements and employing an asymmetrical transceiver. Such asymmetrical transceiver architecture, however, also brings out channel dimension inconsistency between the uplink and downlink. Thus, to well achieve the large array gain and fully exploit the potentials of asymmetrical transceiver-based massive MIMO systems, accurately recovering the full-dimensional downlink channel state information (CSI) based on the obtained small-dimensional uplink CSI is necessary. Nevertheless, the CSI at different antennas plays a different role in the CSI recovery due to the spatial correlation. Therefore, investigating appropriate antenna selection for asymmetrical transceiver-based massive MIMO systems is valuable and essential. To address this, we first formulate the antenna selection problem to minimize the mean-square recovery error of the full-dimensional downlink CSI in this paper. Then, two receive antenna selection algorithms are proposed by exploiting the low-rank property of the spatial correlation matrices under single-user scenarios. We also extend these algorithms to multi-user scenarios, and semi-closed-form optimal selection coefficients are derived. Numerical results demonstrate that, with the aid of the proposed antenna selection algorithms, the full-dimensional downlink CSI can be well recovered, which thus paves the way for asymmetrical transceiver-based massive MIMO systems to achieve their excellent downlink transmission performance with a much lower overall system hardware complexity and cost.

In this article, we present a hybrid channel estimation algorithm for uniform planar array (UPA)-assisted millimeter-wave (mmWave) massive multiple-input–multiple-output (MIMO) Internet of Things (IoT) systems by exploiting the benefits from both the compressed sensing (CS) and the sparse Bayesian learning (SBL). Compared with existing studies, the distribution characteristics and correlations between propagation paths in the elevation (e)- and azimuth (a)-angle domains are considered to enhance the estimation performance. Specifically, we first redefine the e-angles and the a-angles to simplify the system model. Then, a novel autoregressive (AR)-Gaussian channel prior is proposed to capture both the sparsity and the clustering properties of mmWave massive MIMO IoT channels. After that, we provide a channel approximation method to overcome the channel uncertainty by exploiting the structure of the AR-Gaussian channel prior. The hybrid beamforming (HBF) architecture with limited radio-frequency (RF) chains in mmWave IoT systems is also considered. Finally, we propose a hybrid channel estimation algorithm, which consists of two stages. Based on the different distribution characteristics in different angle domains, the CS-based channel estimation is performed for e-angles on stage one, while the SBL-based channel estimation is applied for a-angles on stage two. Numerical results reveal that compared with the existing CS- and SBL-only methods, the proposed hybrid channel estimation algorithm exhibits better performance in terms of computational complexity, sparsity robustness, and estimation accuracy.

Reconfigurable intelligent surface (RIS) stands out as a promising technology by enhancing the electromagnetic wave propagation environment with its passive reflecting elements. In this paper, we focus on the ergodic sum-rate analysis and maximization of the RIS-assisted uplink multiuser multiple-input multiple-output (MIMO) multiple-access channel (MAC) under Rician fading by exploiting full statistical channel state information (CSI). The spatial correlations at the base station, the users, and the RIS are also considered. By using the replica method originated in statistical physics, the closed-form asymptotic ergodic sum-rate of the system is first derived in the large-system regime on the account of the unique channel structure of the RIS-assisted MIMO-MAC system. Then, based on the derived asymptotic ergodic sum-rate, we propose an alternating optimization (AO) algorithm to jointly design the transmit covariance matrix of users and the phase-shifting matrix of RIS with full statistical CSI. Simulation results are also demonstrated to verify the accuracy of the derived asymptotic ergodic sum-rate and the superiority of the proposed AO algorithm. The results reveal that the derived closed-form asymptotic ergodic sum-rate matches very well with the Monte Carlo results even for a small number of antennas, and the proposed AO algorithm can achieve up to 10 bps/Hz gain at high signal-to-noise (SNR) regime, which is therefore valuable for future RIS-assisted MIMO-MAC system designs.

Recently, reconfigurable intelligent surfaces (RISs) have drawn intensive attention to enhance the coverage of millimeter wave (mmWave) communication systems. However, existing works mainly consider the RIS as a whole uniform plane, which may be unrealistic to be installed on the facade of buildings when the RIS is extreme large. To address this problem, in this paper, we propose a sparse array of sub-surface (SAoS) architecture for RIS, which contains several rectangle shaped sub-surfaces termed as RIS tiles that can be sparsely deployed. An approximated ergodic spectral efficiency of the SAoS aided system is derived and the performance impact of the SAoS design is evaluated. Based on the approximated ergodic spectral efficiency, we obtain an optimal reflection coefficient design for each RIS tile. Analytical results show that the received signal-to-noise ratios can grow quadratically and linearly to the number of RIS elements under strong and weak LoS scenarios, respectively. Furthermore, we consider the visible region (VR) phenomenon in the SAoS aided mmWave system and find that the optimal distance between RIS tiles is supposed to yield a total SAoS VR nearly covering the whole blind coverage area. The numerical results verify the tightness of the approximated ergodic spectral efficiency and demonstrate the great system performance.

Millimeter wave (mmWave) band, or high frequencies such as THz, has large undeveloped band of spectrum. However, wireless channels over the mmWave band usually have one or two paths only due to the severe attenuation. The channel property restricts its development in the multiple-input multiple-output (MIMO) system, which can improve throughput by increasing the spectral efficiency. Recent development in reconfigurable intelligent surface (RIS) provides new opportunities to mmWave communications. In this study, we propose a mmWave system, which used low-precision analog-to-digital converters (ADCs), with the aid of several RIS arrays. Moreover, each RIS array has many reflectors with discrete phase shift. By employing the linear spatial processing, these arrays form a synthetic channel with increased spatial diversity and power gain, which can support MIMO transmission. We develop a MIMO detector according to the characteristics of the synthetic channel. RIS arrays can provide spatial diversity to support MIMO transmission, however, different number, antenna configuration, and deployment of RIS arrays affect the bit error rate (BER) performance. We present state evolution (SE) equations to evaluate the BER of the proposed MIMO detector in the different cases. The BER performance of indoor system is studied extensively through leveraging by the SE equations. We reveal numerous insights about the RIS effects and discuss the appropriate system settings. In addition, our results demonstrate that the low-cost hardware, such as the 3-bit ADCs of the receiver side and the 2-bit uniform discrete phase shift of the RIS arrays, only moderately degenerate the system performance.

Compressive scanning (CS) has exhibited its potential in improving the path tracking efficiency of millimeter wave (mmWave) systems. However, its practical performance is significantly degenerated by hardware imperfections, such as carrier frequency offset (CFO). Conventional CFO estimation methods that compare the phases of two measurements cannot be applied in CS straightforwardly because the two successive beacons are different. To overcome these problems, we propose a novel CFO-robust compressive path-tracking algorithm by introducing a two-stage CFO estimation procedure before performing coherent CS detection. Unlike conventional CFO estimates, the CFO estimate in the proposed algorithm can be obtained from the signal strength value of the received signal at the cost of a small amount of additional computation complexity. Numerical results demonstrate the superiority of the proposed algorithm in both single-path and multipath scenarios.

In this letter, we investigate the symbol detection of an uplink massive multiple-input multiple-output system impaired by phase noise at the transmitter and receiver sides. We propose a low-complexity iterative algorithm using approximate Bayesian inference based on the framework of generalized expectation consistent signal recovery to recover the symbol vector from nonlinear noisy measurements. Numerical results show that the proposed algorithm outperforms the existing algorithm and approaches the symbol error rate limit of a genie detector in high signal-to-noise ratio (SNR) regime, while the performance loss is very small in medium SNR. In particular, the complexity of proposed algorithm is quadratic, which makes it particularly suitable for large systems.

Due to the underlying sparse structure of the mmWave channels, which indeed makes the exact closed-form capacity expressions inherently hard to derive, there has been less research on the ergodic capacity of mmWave systems. To overcome this problem, by means of the majorization theory, this paper analyzes the ergodic capacity of point-to-point mmWave communication systems under finite-dimensional channel model. In particular, we derive several closed-form ergodic capacity approximations, which exhibit excellent tightness in spite of whether the steering matrices are singular or not. Then, several Jensen's approximations and bounds of the ergodic capacity are also derived. The results indicate that the ergodic capacity seems to increase logarithmically with the number of antennas, the transmit SNR per antenna, and the eigenvalues of the steering matrix products. Besides, the DFT matrices can effectively characterize the spatial directions of mmWave channels when the number of antennas grows large. After that, high-SNR ergodic capacity, high-SNR slope, and power offset are also analyzed. It indicates that for a finite-dimensional channel, the maximum multiplexing gain increases with the number of paths instead of the number of antennas in Rayleigh channels. Numerical simulations are performed to validate the results.

In this paper, we present the link-level simulation of a time division duplex (TDD)-based massive multiple-input multiple-output (MIMO) system with multi-antenna user equipments (UEs). The system model of the proposed massive MIMO system is first given, based on which the general settings of our link-level simulation are presented, including the essential parameters, the LTE-like frame structure, and the discontinuous pilot allocation for all UEs. Block diagrams are then illustrated, along with the link-level data transmission procedures of both the uplink and the downlink. Thereinto, the low-complexity but well-performance channel estimation algorithm and the MIMO detector for the uplink are introduced, and two block diagonalization (BD) precoding schemes for the downlink are also illustrated. Finally, numerical results in terms of the bit error rate (BER) and the throughput are investigated and analyzed. Running time for these two proposed precoding schemes is also given for comparison of computational complexity.

Under the assumption of single bounce channel model, the position and velocity of a mobile station (MS) can be determined by time of arrival (TOA), angle of arrival (AOA) and doppler-shifted frequency (DSF) measurements at three base stations (BSs) when line of sight (LOS) paths between the three BSs and the MS are all blocked. The equations relating the measured TOAs, AOAs and DSFs to the location parameters are nonlinear and under-determined which are hard to solve. A novel grid search method which requires only two-dimensional search in x-y coordinates and achieves good location accuracy is presented. Simulation results verify the effectiveness of the proposed method.