Millimeter Wave Massive MIMO Downlink Per-Group Communications with Hybrid Linear Precoding

We address the problem of analyzing and classifying into groups the downlink channel environment in a dense millimeter-wavelength cell, accounting for path loss, multipath fading, and User Equipment (UE) blocking, by employing a hybrid propagation and multipath fading model, thus using accurate inter-group interference modeling. The base station (BS) employs a large Uniform Planar Array (UPA) to facilitate massive Multiple-Input, Multiple-Output (MIMO) communications with high efficiency. UEs are equipped with a single antenna and are distributed uniformly within the cell. The key problem is analyzing and defining groups toward efficient precoding. Because balanced throughput is desired between groups, up to four separate frequency sub-carriers are employed in an Orthogonal Frequency Division Multiple Access (OFDMA) system. We show that by employing three or four OFDMA subcarriers, depending on the number of UEs in the cell and the cell range, the UEs can be efficiently separated into <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">high-throughput</i> groups, with each group employing Virtual Channel Model Beams (VCMB) based inner precoding, followed by efficient Multi-User Multiple-Input Multiple-Output (MU-MIMO) outer precoders. For each group, we study two different sub-grouping methods offering different advantages. We show that the improvement offered by Zero-Forcing Per-Group Precoding (ZF-PGP) over Matched Filter Precoding (MFP) and Zero-Forcing Precoding (ZFP) is very high when finite-alphabet inputs are used. We also show ZF-PGP with finite-alphabet inputs significantly outperforms MFP with Gaussian inputs in certain realistic setups. Finally, a new technique for power allocation among different UEs is proposed, called Optimized Per-Group Power Allocation (OPGPA), which achieves much higher power efficiency jointly with equal throughput among all UEs in each group.

This paper analyzes the problem of optimum user association and sum rate maximization for software defined radio access networks (SD-RANs) with access node diversity for fifth generation (5G) wireless networks. We consider four complementary types of access nodes namely, a massive multiple- input multiple-output (MIMO) base station (BS), MIMO BSs, small cells (SCs), and indoor and outdoor distributed antenna systems (DAS). The SDRAN user association problem is solved through a novel three-stage optimization scheme called the place coverage (TPC). TPC divides the user equipment (UEs) into two sets of indoor and outdoor UEs. Initially, TPC associates indoor UEs with the indoor DAS access nodes. Next, outdoor UEs are associated with the outdoor DAS access nodes, SCs, MIMO BSs, and the massive MIMO BS. Finally, the remaining resources of indoor and outdoor access nodes are used to serve the UEs that have not been served. The scope of TPC is multifold. First, TPC reflects the closest model to a real-world diverse 5G network. Secondly, servicing the indoor UEs with iDAS antennas results in lower power load per user for the access network resulting in better coverage, quality, and data speed. Thirdly, TPC provides reduced radiation levels for indoor UEs. Numerical results show that TPC provides significant sum rate and fairness gains over the received signals strength (RSS) based association schemes [1]. Moreover, TPC provides comparable performance levels as the optimum association scheme based on exhaustive search while bringing significant complexity reduction.

Time division duplexing (TDD) is adopted to exploit the uplink and downlink channel reciprocity in most of studies on massive multiple-input multiple-output (MIMO) systems. However, even in TDD, a base station (BS) still requires to transmit downlink training signals, which are named in the 3GPP standard as channel state information reference signals (CSI-RSs), to fully support spatial multiplexing in practice. This is because user equipments (UEs) may deploy less number of transmit antennas than receive antennas due to practical issues. Since uplink sounding reference signals (SRSs) are transmitted from only the transmit antennas of the UE, the BS is not able to obtain full downlink MIMO CSI by using channel reciprocity for spatial multiplexing. Hence, after reception of the downlink CSI-RSs, the UE still needs to feed back quantized CSI using a codebook to support spatial multiplexing. Taking practical antenna structures into account for reducing downlink CSI-RS overhead, this paper proposes possible approaches for downlink MIMO CSI reconstruction at the BS using the SRS with quantized downlink CSI to support spatial multiplexing. Numerical results show that the proposed techniques outperform the conventional one, i.e., solely based on the quantized CSI, in terms of the spectral efficiencies of spatial multiplexing.

Massive multiple-input multiple-output (MIMO) systems, where base stations (BSs) are equipped with hundreds of antennas, are an attractive way to handle the rapid growth of data traffic. As the number of user equipments (UEs) increases, the initial access and handover in contemporary networks will be flooded by user collisions. In this paper, a random access protocol is proposed that resolves collisions and performs timing estimation by simply utilizing the large number of antennas envisioned in massive MIMO networks. UEs entering the network perform spreading in both time and frequency domains, and their timing offsets are estimated at the BS in closed form using a subspace decomposition approach. This information is used to compute channel estimates that are subsequently employed by the BS to communicate with the detected UEs. The favorable propagation conditions of massive MIMO suppress interference among UEs, whereas the inherent timing misalignments improve the detection capabilities of the protocol. Numerical results are used to validate the performance of the proposed procedure in massive MIMO networks, under uncorrelated and correlated fading channels. With $2.5 \times 10^{3}$ UEs that may simultaneously become active with probability 1%, a total of 16 frequency–time codes, and 100 antennas, a given UE is detected with probability 75%, and the accuracy of its timing estimate is on the order of few samples.

5G massive MIMO deployments up to 64T64R (64 Transmit and 64 Receive) antenna arrays are gaining traction, in part due to the smaller size of such arrays suitable for mid-band spectrum (2-5 GHz). A scheduling algorithm that can aggressively pair many UEs (User Equipment) on the same time-frequency resources is essential to fully derive the benefits of such an array. In this paper, an uplink scheduler design is proposed based on RME (Recursive Maximal Expansion) that ensures contiguous resource allocation and power constraint for each UE, while maximizing MU (Multi-User) MIMO (Multiple-Input Multiple-Output) gains by selecting and pairing UE groups based on the sum-PF (Proportional Fair) metric. We show, using realistic system level simulations, that up to 100% average cell-throughput gains can be achieved by MU-MIMO over SU (Single-User) MIMO in dense urban micro cell deployment scenarios presenting an attractive proposition for massive MIMO array deployments. It is observed that power control parameters play a critical role and should be optimized considering scheduler behavior, especially for cell-edge throughput improvement.

In the multiuser multiple-input multiple-output (MU-MIMO) system, to reduce the influence of channel correlation on system performance, the base station (BS) should select the appropriate subset of user equipments (UEs) according to their channel state information (CSI). Due to a lack of channel reciprocity, the downlink CSI needs to be fed back to the BS in frequency division duplexing (FDD) mode. Some scholars have exploited kinds of deep neural networks (DNNs) for sensing and recovering CSI. However, user selection after all the CSI is reconstructed by DNNs will bring a great time delay. In this paper, we propose a deep learning-based CSI feedback scheme called US-CsiNet. Based on adversarial autoencoder (AAE), US-CsiNet can explicitly cover user schedule information while representing CSI. At the UE side, the encoder of US-CsiNet maps the CSI into codewords of which part are feature information for user schedule. Then the BS applies these partial codewords to separate the UEs into different groups and select active UEs. Finally, the decoder of AAE reconstructs the CSI of these active UEs. US-CsiNet can not only simplify the user selection process but also guarantee the accuracy of CSI reconstruction. The simulation results show that the proposed approach outperforms maximum channel gain (MCG) user selection algorithms and achieves the nearly same performance with semiorthogonal user selection (SUS) which needs full CSI of all users at the BS.

The use of massive multiple-input multiple-output (MIMO) to serve a crowd of\nuser equipments (UEs) is challenged by the deficit of pilots. Assuming that the\nUEs are intermittently active, this problem can be addressed by a shared access\nto the pilots and a suitable random access (RA) protocol. The strongest-user\ncollision resolution (SUCRe) is a previously proposed RA protocol that often\nprivileges the UEs closer to the base station (BS). In contrast, we propose a\nnovel RA protocol using a decentralized pilot power allocation method that aims\nat a fairer performance. The proposed access class barring with power control\n(ACBPC) protocol allows each UE to estimate, without additional overhead, how\nmany UEs collided for the chosen pilot and calculate an ACB factor, which is\nthen used to determine the pilot retransmission probability in the next\nprotocol step. The results show that the proposed ACBPC protocol is superior to\nSUCRe in terms of providing a fair connectivity for very crowded networks,\nalthough still being distributed and uncoordinated as the original SUCRe\nprotocol.\n

Accurate channel state information (CSI) at base station (BS) is the key to achieve full multiplexing and diversity gain in traditional multiple-input multiple-output (MIMO) systems. In frequency-division-duplex (FDD) systems, it is obtained through downlink training and uplink feedback. However, the training and feedback overhead in massive MIMO systems is unacceptable for the large number of antennas equipped at BS. To reduce the CSI feedback overhead and improve CSI quantization accuracy, we propose a limited feedback scheme for spatio-temporal correlated massive MIMO channel. The proposed scheme jointly performs Karhunen-Loève transform (KLT) and polar-cap quantization on CSI. For the compactly deployed antennas at BS, there exists spatial correlation in massive MIMO channel. When the user equipment (UE) moves slowly, adjacent channels feature temporal correlation. The high dimensional and spatial correlated CSI is compressed through KLT. Then the compressed CSI is normalized to be isotropic to perform polar-cap quantization. Simulation results show that the proposed scheme is capable of improving the sum-rate performance with limited feedback bits for massive MIMO systems.

The forthcoming fifth generation (5G) systems with high beamforming gain antenna units, millimeter-wave (mmWave) frequency bands together with massive Multiple Input Multiple Output (MIMO) techniques are key components for accurate positioning methods. In this paper, we propose the positioning technique that is relying on the sparsity in the MIMO-OFDM channel in time and spatial domains, together with effective beamforming methods. We will study the proposed solution in a multi-connectivity context, which has been considered so far for the purpose of improving the user equipment (UE) communication data rate. We utilize the multi-connectivity for positioning, in order to improve robustness to measurement errors and increase positioning service continuity. In particular, we show that when a UE that has connectivity to more base stations, the total power and delay needed for positioning can be reduced.

Massive multiple-input multiple-output (MIMO) systems hold the potential to be an enabling technology for 5G cellular. Uniform planar array (UPA) antenna structures are a focus of much commercial discussion because of their ability to enable a large number of antennas in a relatively small area. With UPA antenna structures, the base station can control the beam direction in both the horizontal and vertical domains simultaneously. However, channel conditions may dictate that one dimension requires higher channel state information (CSI) accuracy than the other. We propose the use of an additional one bit of feedback information sent from the user to the base station to indicate the preferred domain on top of the feedback overhead of CSI quantization in frequency division duplexing (FDD) massive MIMO systems. Combined with variable-rate CSI quantization schemes, the numerical studies show that the additional one bit of feedback can increase the quality of CSI significantly for UPA antenna structures.

There has been a growing interest in analog network coding, which can improve system throughput and spectrum efficiency significantly in wireless systems. Two-way relay scenario is considered in this paper, where the base station (BS) and the relay are both equipped with multiple antennas, and each of K user equipments (UEs) in the cell has a single antenna. An advanced multi-user scheduling scheme is proposed for the analog network coded two-way relay channel, where UEs and BS exchange information via the relay node during two time slots. Because each UE with single antenna has poor transmission and reception capability, the scheduling scheme is carefully designed to decrease multi-user interference and optimize beamforming gain from BS to relay. Analytical results of sum rate and outage probability have been developed to demonstrate that the multi-user diversity gain can be achieved by the proposed scheme. Meanwhile, the user fairness of the proposed scheme is on the same order as that of round robin (RR), and the computation complexity only increases linearly with the number of users in the serving cell. Monte-Carlo simulation is conducted to exhibit the performance gains from the proposed scheduling scheme. Results show that the proposed channel correlation based user selection scheme outperforms existing traditional scheduling schemes significantly, even when the cellular user payload is low.

&lt;p&gt;This thesis considers the analysis of current and future cellular communication systems. The main focus is on multiuser multiple-input multiple-output (MU-MIMO) antenna systems. The goal of this work is to characterize the achievable spectral efficiency of MU-MIMO systems, as well as to analyze the performance of practical, linear MU-MIMO transceiver structures in heterogeneous propagation environments. The analytical derivations in this thesis are based on the mathematical theory of finite and large dimensional random matrices. A collection of new general random matrix theory results, which permit efficient numerical evaluation are derived. With downlink regularized zero-forcing (RZF) processing at a cellular base station (BS), a general framework for the analysis of the expected (average) signal-to-interference-plus-noise-ratio (SINR) and ergodic sum spectral efficiency is developed for uncorrelated and semi-correlated Rayleigh fading, as well as uncorrelated Ricean fading propagation channels. In contrast to existing results, the presented analyses are extremely general, applicable to single-cellular, multi-cellular, as well as distributed antenna systems. These systems could consist of arbitrary numbers of transmit and receive antennas, link signal-to-noise-ratios (SNRs), equal and unequal transmit correlation structures, and line-of-sight (LoS) levels, respectively. Numerical results are presented for single-cellular, as well as for two-tier multi-cellular systems demonstrating the impact of novel BS coordination strategies to suppress dominant inter-cellular interference. With dominant LoS directions in the propagation channel, the instantaneous downlink zero-forcing (ZF) SNR of a given terminal is analyzed. The ZF SNR is shown to be approximated by a gamma distribution for any number of transmit and receive antennas, link SNRs, and LoS levels. Furthermore, for moderately sized MU-MIMO systems, simplified instantaneous and ergodic sum spectral efficiency analyses are presented with RZF, ZF and matched-filter (MF) transmission on the downlink, and minimum-mean-squared-error, ZF and maximum-ratio combining (MRC) on the uplink, respectively. The simple nature of the derived expressions lead to the discovery of several valuable system level insights as a function of the contributing network parameters. Numerical results are presented for conventional and moderate MU-MIMO systems. Considering downlink semi-correlated Rayleigh fading channels with spatial correlation at the BS, it is mathematically proven that common correlation patterns for each terminal predicts lower ergodic sum spectral efficiencies in comparison to terminal specific correlation patterns. Closed-form approximations for the expected SINR and ergodic sum spectral efficiency are derived for both MF and ZF precoding, demonstrating the sensitivity of unequal correlation structures on the expected signal, interference and noise powers, respectively. The presented numerical results provide a cautionary tale of the impact of unequal correlation patterns on MU-MIMO performance and the importance of modeling this phenomenon. Finally, an approximate uplink performance analysis of large MU-MIMO systems with MRC and space-constrained uniform linear antenna arrays (ULA) is presented for semi-correlated Ricean fading channels. A space-constrained channel model is proposed, encapsulating the effects of unequal receive spatial correlation, unequal LoS levels, and unequal link gains for each terminal. The per-terminal and cell-wide ergodic sum spectral efficiencies are characterized and numerous practical special cases are presented. A limiting analysis of the ergodic per-terminal and cell-wide spectral efficiencies is also carried out, as the number of BS antennas grow without bound with a finite number of terminals and fixed physical dimensions of the ULA. Numerical results demonstrate the impact of space-constrained ULAs on the MU-MIMO system performance with variation in the LoS levels, correlation structures, physical array dimensions, and system size, respectively.&lt;/p&gt;

This paper focuses on the uplink (UL) of a single-cell massive multiple-input multiple-output (MIMO) systems in which the base station (BS) equipped with M antennas communicates with K single-antenna user equipments (UEs). Due to the finer spatial resolution achieved by a huge number of antennas at the base stations, these systems can substantially reduce intra-cell interference with simple signal processing. However, most precoding schemes exhibit prohibitively high computation complexity as the system dimensions increase. For example, the near-optimal minimum mean squared error (MMSE) precoding requires the inversion of a large matrix. To reduce further the complexity, the truncated polynomial expansion (TPE) is applied to approximate the inverse of large matrices that appear on the expressions of the optimal linear transceivers. A key requirement to achieve good detection performance is to find good polynomial coefficients (J), where the polynomial coefficients (J) are optimized to maximize the system performance. The simulation results show that the proposed scheme provides very close to optimal performance while reducing significantly the computational complexity.

This paper proposes a novel heuristic algorithm that mitigates pilot contamination in Massive MIMO (multiple-input, multiple-output) systems by optimizing the allocation of pilots, which are a shared resource among base stations (BSs). The goal is to minimize the effect of pilot contamination caused by interference from reusing the same pilot sequence from multiple user equipment (UE) in different cells. Specifically, we aim to maximize the minimum uplink signal to interference ratio (SIR) through the network by assigning the same pilot to the UEs in different cells that cause the least amount of interference. For this purpose, the algorithm starts with two cells that have the worst arithmetic mean of the potential SIR between their users. Then the algorithm continues the assignment procedure for the rest of the cells depending on the arithmetic mean of the worst potential SIR of the users of a cell and the users of the already assigned cells. The simulation results showed that our results are very close to the optimal solution where the exhaustive search is feasible, and outperforms the results obtained from recently published related work which served as a benchmark when the exhaustive search could not be applied to the search space size.

This paper studies the downlink sum-rate and energy efficiency (EE) of massive multiple-input multiple-output (MIMO) systems under the effect of channel aging caused by the movements of users. First, a closed-form expression of the downlink sum-rate where zero-forcing (ZF) precoder is adopted at the base station (BS) is derived. Based on the expression, the power-scaling law that when the number of BS antennas M approaches to infinity, the transmit power of BS can be reduced proportionally to 1/√M is still satisfied in the massive MIMO downlink system. Second, we utilize a general power consumption model to analyze the EE of the system. Both theoretical analysis and Monte-Carlo simulations show that the emergence of channel aging could degrade the sum-rate and the EE of the system.