Experimental evaluations on carrier aggregation and multi-user MIMO associated with EVD-based CSI feedback for LTE-Advanced downlink

Multiple-input multiple-output (MIMO) systems can achieve high data-rate and high capacity transmission. In MIMO systems, the scheme that weight substreams based on minimum mean square error (MMSE) criterion at the transmitter using feedback channel state information (CSI) achieves good performance (denoted hereafter as MMSE precoder). However, if the optimum number of substreams is not selected based on CSI and methods for reducing the performance degradation due to feedback delay is not used, MMSE precoder cannot achieve the full performance possible. In this paper, we propose an MMSE precoder with mode selection for MIMO systems, which selects both the number of substreams and the modulation scheme for each substream to minimize the average BER at a fixed rate. We evaluate the BER of the proposed MMSE precoder with two methods for reducing performance degradation due to feedback delay, channel prediction and receive weight robust to feedback delay. We also evaluate the BER of the proposed MMSE precoder with the method that combines channel prediction with receive weight robust to feedback delay. Simulation results show that the BER of the proposed MMSE precoder is improved compared to that of the conventional MMSE precoder using the fixed number of substreams. We also show that the method that combines channel prediction with receive weight robust to feedback delay can achieve good BER even when the large feedback delay exists.

This paper presents laboratory experimental results on 4-by-2 multi-user (MU)-MIMO that achieves up to 1 Gbps throughput using carrier aggregation with a 100-MHz bandwidth in the LTE-Advanced downlink. The achievable throughput performance employing adaptive modulation and coding (AMC) with outer-loop threshold control based on acknowledgement (ACK) / negative ACK (NACK) feedback signaling in order to compensate for the fluctuation in the measured signal-to-interference plus noise ratio (SINR) value due to MU-MIMO multiplexing is evaluated using the implemented LTE-Advanced transceivers. Additionally, we evaluate the influence of the angle between spatially multiplexed users and the user combination in MU-MIMO operation with different received signal-to-noise ratio (SNR) conditions. Laboratory experimental results show that by performing AMC with outer-loop threshold control, the throughput of approximately 1 Gbps is achieved at the average received SNR of 30 dB. Furthermore, based on the experimental results, we confirm that angles narrower or wider than approximately 60 degrees between spatially multiplexed mobile stations (MSs) yield a lower ability to decrease the mutual interference using minimum mean square error (MMSE)-based precoding in 4-by-2 MU-MIMO. We also show that especially when the angle between spatially multiplexed MSs is narrower or wider than approximately 60 degrees, selecting the user pairs with a large difference in the average received SNR is effective in improving the total throughput.

Interference alignment (IA) is a multi-user wireless communication strategy that enables several transmitters simultaneous communicate with their paired receivers without interfering each other. IA is a multiplexing gain optimal strategy at the expense of requiring the global channel state information (CSI) at the transmitters. Most previous works assume the availability of perfect CSI which is not possible in practice. The performance of IA algorithms based on perfect CSI drop dramatically in circumstance where CSI errors exist. In this paper we propose a robust minimum mean square error (MMSE) IA algorithm with imperfect CSI. Based on a Gaussian statistic channel error model, we give the mean square error objective function with per transmitter power constraint. The objective function is not joint convex in precoders and decoders, but convex in precoders with fixed decoders and vice versa. We convert the problem to two convex sub problems and iteratively find the solutions. Numerical results are presented to validate the effectiveness of proposed algorithm when CSI errors exist.

In this paper, we design a zero-forcing (ZF) Tomlinson-Harashima precoding (THP) and a minimum mean square error (MMSE) THP for multiuser MIMO relay system with direct link. Based on these precoding structures, we propose the optimal combining weights of the ZF THP and the MMSE THP in half duplex relay system, and the sub-optimal weights of MMSE THP to reduce feedback channel state information (CSI). Through the Monte Carlo simulation, we verify that the optimal weights combining method outperforms the constant weights combining method. In addition, we confirm that the performance of the sub-optimal weights combining method is close to the optimal method, while reducing the feedback CSI simultaneously.

Carrier aggregation (CA) is considered as a key enabling technology to provide higher rates for users in LTE/5G networks. However, the increased transmission rate is accompanied by higher energy consumption. The existing CA energy efficiency resource optimization allocation scheme in 5G does not fully consider the following two issues, namely, the impact of delayed channel state information feedback on the rationality of resource allocation and the increasing in energy consumption caused by frequent switching of component carriers (CCs) by narrowband users; this paper proposed a CA energy-efficient joint resource optimization allocation (PEJA) scheme based on channel information prediction. The proposed scheme (PEJA) fully considers the above two issues. Firstly, the algorithm of random forest predicting channel state information is designed. On the basis, the CA energy-efficient joint resource optimization allocation (PEJA) scheme based on channel information prediction is proposed. The simulation results show that the proposed algorithm PEJA has a higher energy efficiency and throughput than the comparison algorithm under different numbers of users and different transmission powers. The PEJA algorithm is more energy efficient than the PEJA-NC algorithm, which does not consider the CC handover of narrowband users. To sum up, the proposed PEJA energy-efficient resource allocation scheme maximizes system energy efficiency and achieves a higher throughput.

With the increasing rate of wireless devices and high bandwidth operations, wireless networking and communications are becoming over crowded. To cope with such crowded and messy situation, massive MIMO is designed to work with hundreds of low-cost serving antennas at a time as well as improve the spectral efficiency at the same time. Time Division Duplexing (TDD) has been used for gaining beamforming which is a major part of massive MIMO, to gain its best improvement to transmit and receive pilot sequences. All the benefits are only possible if the channel state information or channel estimation is gained properly. Least Square (LS), Minimum Mean Square Error (MMSE) and Linear Minimum Mean Square Error (LMMSE) are the estimators commonly used for estimating channel matrices. We have optimized these methods using genetic algorithm to minimize the mean squared error and finding the best channel matrix from existing algorithms with less computational complexity. Our simulation result has shown that the use of Genetic Algorithm (GA) worked well on existing algorithms in a Rayleigh slow fading channel and existence of Additive White Gaussian Noise. We found that the GA optimized LS is better than existing algorithms. GA provides optimal result in some few iterations in terms of Mean Square Error (MSE) with respect to SNRand computational complexity.

Considering virtual cell clusters formed by several overlapping virtual cells in ultra dense network (UDN), virtual cells which are in the same virtual cell cluster coordinate transmissions to reduce inter-virtual cell interference. However, general beamforming scheme design for channel state information (CSI) demand still remains large, and system overhead for high-dimensional channel training and channel state information feedback is explosively-increased. To solve this problem, this paper focuses on hierarchical beamforming scheme based on virtual cell cluster. Specifically, based on perfect intra-cluster CSI, beamforming direction vector is designed by optimizing the achievable rate of each cluster with Zero-Forming block diagonalization method. Based on the large-scale inter-cluster CSI, the strength of beamforming can be devised by solving the sum rate maximization problem and mitigating the residual co-channel interference among different clusters with an iterative power allocation algorithm via a generalized weighted minimum mean square error (WMMSE) approach. Simulation results demonstrate that the proposed scheme achieves significant performance gain and requires less CSI than traditional schemes.

Free space optical communication (FSO) is widely deployed to transmit high data rates for rapid communication traffic increase. Asymmetrically clipped optical orthogonal frequency division multiplexing (ACO-OFDM) modulation is a very efficient FSO communication technique in terms of transmitted optical power. However, its performance is limited by atmospheric turbulence. When the channel includes strong turbulence or is non-deterministic, the bit error rate (BER) increases. To reach optimal performance, the ACO-OFDM decoder needs to know accurate channel state information (CSI). We propose novel detection using different deep learning (DL) algorithms. Our DL models are compared with minimum mean square error (MMSE) detection methods in different turbulent channels and improve performance especially for non-stationary and non-deterministic channels. Our models yield performance very close to that of the MMSE estimator when the channel is characterized by weak or strong turbulence and is stationary. However, when the channel is non-stationary and variable, our DL model succeeds in improving the performance of the system and decreasing the signal to noise ratio (SNR) by more than 8 dB compared to that of the MMSE estimator, and it succeeds in recovering the received data without needing to know accurate CSI. Our DL decoders also show notable speed and energy efficiency improvement.

The time reversal (TR) technique combined with the ultra-wideband (UWB) system offers a new potential for decreasing the cost and complexity of the UWB receivers. In spite of TR–UWB's good performance in perfect channel state information (CSI), it is very sensitive to the channel estimation error. The effect of channel imperfection on the TR–UWB system is considered in this paper. At first, based on a minimum mean square error (MMSE) equalizer receiver, a prefilter is calculated in closed form to improve the performance of the TR–UWB system in an imperfect CSI scenario. Furthermore, for comparison purposes, a similar calculation for prefilter is carried out based on a simple matched filter (MF) receiver. Then, in order to improve the MF receiver performance, a two-stage iteration-based algorithm is developed. The initial value for this iteration-based improved algorithm is considered to be a prefilter which is calculated in the TR–UWB system with MMSE equalizer. This optimized algorithm causes the channel estimation error in the TR–UWB system to become zero in some steps. Finally, exhaustive simulations are done to demonstrate the performance advantage attained by the improved algorithm.

Multiple transmit antennas can be employed in a downlink channel to serve multiple users under the assumption of perfect channel state information (CSI) of all users at the base station. In practice, however, the channel knowledge available at the base station is not perfect. For instance, in case of a time division duplex (TDD) system, the transmit CSI is the estimated uplink channel. On the other hand, in a frequency division duplex (FDD) system, feedback from the users is required to obtain transmit CSI. In this case, we assume the transmit CSI consists of a quantized version of the normalized estimated channel vector and the statistics of the channel norm of each user. In this work we consider the transceiver design of the downlink of a multiuser MISO system based on the minimum mean square error (MMSE) criterion with imperfect CSI as discussed above.

Deep learning (DL)-based channel state information (CSI) feedback improves the capacity and energy efficiency of massive multiple-input multiple-output (MIMO) systems in frequency division duplexing mode. However, multiple neural networks with different lengths of feedback overhead are required by time-varying bandwidth resources. The storage space required at the user equipment (UE) and the base station (BS) for these models increases linearly with the number of models. In this paper, we propose a DL-based changeable-rate framework with novel quantization scheme to improve the efficiency and feasibility of CSI feedback systems. This framework can reutilize all the network layers to achieve overhead-changeable CSI feedback to optimize the storage efficiency at the UE and the BS sides. Designed quantizer in this framework can avoid the normalization and gradient problems faced by traditional quantization schemes. Specifically, we propose two DL-based changeable-rate CSI feedback networks CH- CsiNetPro and CH- DualNetSph by introducing a feedback overhead control unit. Then, a pluggable quantization block (PQB) is developed to further improve the encoding efficiency of CSI feedback in an end-to-end way. Compared with existing CSI feedback methods, the proposed framework saves the storage space by about 50% with changeable-rate scheme and improves the encoding efficiency with the quantization module.

In this letter, we study and present the performance of multi-user transmitter preprocessing (MUTP) assisted downlink (DL) communications, where the channel state information (CSI) required at the transmitter to carry out the MUTP is estimated at the receiver, and conveyed to the base station (BS) with the aid of a noisy feedback channel that undergoes fading as well. In particular, the CSI is estimated with a known channel estimation technique, and the phases and magnitudes of the CSI are vector quantized and fed back to the transmitter via a fading feedback channel. This vector quantized CSI which is also corrupted by noise is recovered with the aid of a minimum mean square error (MMSE) based hard detector. The recovered CSI is then exploited to formulate the preprocessing matrix which is based on creating an orthogonal subspace to the interfering subspace. Our simulations show that the impact of codebook size and quantization errors play a dominant role in determining the performance of the considered DL system in terms of the achievable symbol error rate (SER).

Two numerical approaches, the Robust-Generalized Iterative Approach (R-GIA) and the Robust-Transmit Covariance Optimization Approach (R-TCOA), are proposed for jointly designing the minimum mean square error (MMSE) precoders and decoders of uplink multiuser multiple-input-multiple-output (MIMO) systems with arbitrary linear equality power constraints and possibly imperfect channel state information (CSI). The R-TCOA always gives optimum solutions but is only applicable when the rank constraints on the precoders are relaxed, the spatial correlation matrix for the transmit antennas of each user is an identity matrix, and there exists a scalar such that squaring the source covariance matrices is the same as multiplying them by it. The statistics of the CSI error also need to be the same for all users if the power constraints of the users are interdependent. The R-GIA, on the other hand, has no such restrictions. But whenever the R-TCOA is applicable, both approaches converge, and all the transmit covariance matrices are full rank, the two solutions are actually equivalent (i.e. the R-GIA is also optimum)! Numerical results show that these two robust approaches, for the most part, outperform their non-robust counterparts in various different channel correlation scenarios.

Capacity maximizing in massive MIMO with linear precoding for SSF and LSF channel with perfect CSI

In this paper, we propose a joint channel estimation and symbol detection (JCESD) algorithm relying on message-passing algorithms (MPA) for orthogonal frequency division multiple access (OFDMA) systems. The channel estimation and symbol detection leverage the framework of expectation propagation (EP) and belief propagation (BP) with the aid of Gaussian approximation, respectively. Furthermore, to reduce the computation complexity involved in channel estimation, the matrix inversion is transformed into a series of diagonal matrix inversions through the Sherman-Morrison formula. Simulation experiments show that the proposed algorithm can reduce the pilot overhead by about 50%, compared with the traditional linear minimum mean square error (LMMSE) algorithm, and can approach to the bit error rate (BER) performance bound of perfectly known channel state information within 0.1 dB.