Massive MIMO Systems

In this paper, we consider the problem of Angle of Departure (AoD) based channel feedback in Frequency Division Duplex (FDD) massive Multiple-Input Multiple-Output (MIMO) systems with multiple antennas at the users. We consider the use of Zero-Forcing Block Diagonalization (BD) as the downlink precoding scheme. We consider two different cases; one in which the number of streams intended for a user equals the number of antennas at that user and the other case in which the number of streams is less than the number of user antennas. BD requires the feedback of the subspace spanned by the channel matrix at the user or a subspace of it in the case of having less number of streams than the number of antennas at a specific user. Based on our channel model, we propose a channel feedback scheme that requires less feedback overhead compared to feeding back the whole channel matrix. Then, we quantify the rate gap between the rate of the system with perfect Channel State Information (CSI) at the massive MIMO Basestation (BS) and our proposed channel feedback scheme for a given number of feedback bits. Finally, we design feedback codebooks based on optimal subspace packing in the Grassmannian manifold. We show that our proposed codes achieve performance that is very close to the performance of the system with perfect CSI at the BS.

The antenna selection technology has become essential for massive multiple-input multiple-output (MIMO) systems since it allows us to benefit from the massive increase in the number of antennas without a necessary use of additional RF components. However, reducing the number of active antennas may exhibit a performance loss regardless of the used antenna selection technique. In this paper, we propose a low-complexity and near maximum-likelihood (ML) performance achieving receiver for large MIMO systems with antenna selection. The proposed approach is based on the likelihood ascent search (LAS) algorithm and allows to increase the system performance in terms of bit error rate (BER). The performance of the proposed detector is analyzed by Monte-Carlo simulations. Numerical results show that our technique provides a gain of about 1 dB.

In this paper, we investigate the suitability of single carrier frequency domain processing (SC-FDP) as the downlink transmission scheme in a massive multiple-input multiple-output (MIMO) system. By deriving the sum-rate of the SC-FDP massive MIMO system theoretically, we show that this method obtains a sum-rate similar to that of orthogonal frequency division multiplexing (OFDM) massive MIMO. We also derive the theoretical sum-rate of both SC-FDP and OFDM in a non-synchronized massive MIMO scenario and show that the rate of the former is significantly larger than that of the latter. Moreover, we theoretically analyze the sum-rate of both systems in the presence of power amplifier non-linearity. All the sum-rates are derived for both zero forcing and matched filter precoding schemes. The results show that the effect of power amplifier non-linearity on the sum-rate of both systems is similar when the number of users is large. We also compare SC-FDP with OFDM from the peak to average power ratio (PAPR) and complexity viewpoints. Although the PAPR of SC-FDP signals is lower than that of OFDM signals, for MIMO systems, the difference between their PAPR decreases as we increase the number of users. Thus, both techniques can have similar PAPR in massive MIMO systems. The overall complexity of SC-FDP and OFDM is similar. Due to the mentioned facts, SC-FDP can be considered as a promising transmission scheme for the downlink of the massive MIMO systems in the presence of carrier frequency offset and power amplifier non-linearities.

Massive multiple-input multiple-output (MIMO) systems have attracted much attention lately due to the many advantages they provide over single- antenna systems. Owing to the many antennas, low- cost implementation and low power consumption per antenna are desired. To that end, massive MIMO structures with one-bit analog-to-digital converters (ADC) have been investigated in many studies. However, the effect of a strong interferer in the adjacent band on one-bit quantized massive MIMO systems have not been examined yet. In this study, we analyze the performance of uplink massive MIMO with one-bit ADCs under frequency selective fading with orthogonal frequency division multiplexing (OFDM) in the perfect and imperfect receiver channel state information cases. We derive analytical expressions for the bit error rate and ergodic rate. We show that the interfering band can be suppressed by increasing the number of antennas or the oversampling rate when a zero-forcing receiver is employed.

Massive multiple-input multiple-output (MIMO) systems are a main enabler of the excessive throughput requirements in 5G and future generation wireless networks as they can serve many users simultaneously with high spectral and energy efficiency. To achieve this massive MIMO systems require accurate and timely channel state information (CSI), which is acquired by a training process that involves pilot transmission, CSI estimation, and feedback. This training process incurs a training overhead, which scales with the number of antennas, users, and subcarriers. Reducing the training overhead in massive MIMO systems has been a major topic of research since the emergence of the concept. Recently, deep learning (DL)-based approaches have been proposed and shown to provide significant reduction in the CSI acquisition and feedback overhead in massive MIMO systems compared to traditional techniques. In this paper, we present an overview of the state-of-the-art DL architectures and algorithms used for CSI acquisition and feedback, and provide further research directions.

In massive multiple-input multiple-output (MIMO) system with time-shifted pilots, the interference from other cell groups, where the pilots are staggered with the ones in the target cell group, can be eliminated when the number of antennas at base station (BS) is infinite. However, with only hundreds of antennas at the BS in practice, both intra-group interference and inter-group interference can not be ignored. Aiming at this problem, a Signal to Leakage and Noise Ratio (SLNR) based downlink precoding and power control scheme is proposed in this paper. In the design of precoder, not only the interference leaked to the users within the same cell is considered, but also the interference leaked to the BSs which are receiving the uplink pilots is included by introducing a conversion factor. Furthermore, the channel estimation error (CEE) is modelled and considered to enhance the robustness of the precoder. To obtain the tradeoff between uplink channel estimation and downlink data transmission, the preferable transmission power and conversion factor are found by maximizing the asymptotic sum-rate, which is dependent on the large-scale fading coefficients and some constants. In the calculation of asymptotic sum-rate, the truncated polynomial expansion (TPE) method is used to simplify the precoder and further to decouple the sum-rate computation from the small-scale fading coefficients. Simulation results show that the proposed downlink transmission scheme outperforms the maximum ratio transmission (MRT) based downlink scheme remarkably. Moreover, the performance obtained by asymptotic sum-rate optimization is very close to the complicated instantaneous sum-rate optimization.

With a large number of antennas at the transmitter and receiver, massive multiple-input multiple-output (MIMO) systems can achieve substantially high spectral efficiency as well as high stability and reliability. Nevertheless, one of the major challenges for its practical use is its computational complexity for symbol detection that grows exponentially with the number of antennas. In this work, we focus on a low complexity implementation of massive MIMO detector based on matched filter (MF) detection with parallel interference cancellation (PIC). In general, MF-based detector performs poorly in the high SNR region. Nevertheless, with the help of powerful channel coding such as low-density parity-check (LDPC) codes and the well-designed PIC, the resulting error performance of the proposed MF-PIC can approach or even outperform the conventional minimum mean square error (MMSE)-based detector with much less complexity. Furthermore, due to its parallel structure, low latency implementation based on parallel architecture is possible and thus is suitable for practical massive MIMO systems.

Filter bank multicarrier with pulse amplitude modulation (FBMC-PAM) is a candidate waveform for the fifth generation of wireless networks (5G). It can transmit at the same rate as orthogonal frequency division multiplexing (OFDM) opposed to the conventional FBMC-based systems. It also has a minimum overlapping factor leading to a transmit signal with a shorter ramp-up and ramp-down than the existing FBMC- based systems. However, this brings some shortcomings to the FBMCPAM system in terms of equalization. In this paper, performance of FBMC-PAM is studied in massive multiple-input multipleoutput (MIMO) channels. It is discussed that although in massive MIMO channels, multiuser interference as well as channel noise average out, there remains some residual interference due to the channel distortions even for an infinite number of base station (BS) antennas. Consequently, the signal to interference ratio (SIR) saturates after the number of BS antennas reaches a certain value. To quantify this saturation level, we find an upper bound for the SIR performance of FBMC-PAM in such channels. Then, our theoretical analysis is confirmed through simulations. Moreover, it is shown that by choosing an appropriate number of subcarriers, although the SIR saturates, FBMC-PAM can equalize the channel efficiently and the error floor in the bit error rate (BER) can be completely removed.We have chosen OFDM as a benchmark, because of its infinite SIR in multipath channels. We also show that FBMC-PAM provides even a better BER performance than OFDM due to its higher bandwidth efficiency.

Massive multiple-input multiple-output (MIMO) technology for wireless communication is considered a key technology for the future fifth-generation (5G) communication networks. To reduce the propagation loss of channel, MIMO systems with analog or digital hybrid precoders and combiners (HPC) architecture have been used. This review paper presents the various types of analog and digital HPC designs for the massive MIMO communication systems that were investigated within the last decade. The reviewed systems are useful for both single-user (SU) and multi-user MIMO (MU-MIMO) systems. The key parameter of the study was spectral efficiency (SE), signal-to-noise ratio (SNR), signal-to-interference pulse noise ratio (SINR), system sum-rate (SR) performance, computational complexity (CC), co-channel interference (CCI), inter-primary base station interference (IPBSI), multiuser interference (MUI), effective desired channel gain (EDCG), and minimum mean square error (MMSE).

Due to the market demands, current communication systems evolve rapidly from existing 4G standard to a future 5G, that implies higher capacity and flexibility. In 4G systems the orthogonal frequency-division multiplexing (OFDM) modulation and multiple-input multiple-output (MIMO) technique are used to enable higher rates for mobile users, while in 5G it is expected to further increase the performances by using a significantly higher number of antennas (massive MIMO) and cooperative relays for solving the problem of coverage and capacity at the cell border. In this work, massive multiuser MIMO OFDM (MV-MIMO) systems, with 10 to 100 antennas are used, without relay nodes, to evaluate the bit error rate (BER) as a function of the Signal to Noise Ratio (SNR). Then, the system is further developed, by introducing amplify-and-forward (AF) relays as intermediaries between the transmitter and the receiver to see if the performances are improved.

Massive multiple-input multiple-output (MIMO) systems can achieve high data rates due to the large number of antennas at the base station when it serves a small number of users. However, deploying massive MIMO in current mobile networks faces a lot of practical issues such as limited physical space and power consumption. Packing large number of antennas in a limited physical space makes spatial correlation between the base station antennas inevitable. In this paper, we study the effect of correlation and we investigate the effectiveness of increasing the number of antennas given limited physical space. Hybrid analog and digital MIMO decoding is adopted to exploit the large array gain at a lower cost and power consumption. Also, different antenna-array structures are investigated and compared in terms of power consumption versus performance.

We study the possibility of removing the cyclic prefix (CP) overhead from orthogonal frequency division multiplexing (OFDM) in massive multiple-input multiple-output (MIMO) systems. We consider the uplink transmission, while our results are applicable to the downlink as well. The absence of CP increases the spectral efficiency in expense of intersymbol interference and intercarrier interference. It is known that in massive MIMO, the effects of uncorrelated noise and multiuser interference vanish as the number of base station antennas tends to infinity. To investigate if the channel distortions in the absence of CP fade away, we study the performance of the standard maximum ratio combining receiver. Our analysis reveals that in this receiver, there always remains some residual interference leading to saturation of signal-to-interference-plus-noise ratio. To resolve this problem, we propose using the time reversal (TR) technique. Moreover, in order to further reduce the multiuser interference, we propose a zero-forcing equalization to be deployed after the TR combining. We compare the achievable rate of the proposed system with that of the conventional CP-OFDM. We show that in realistic channels, a higher spectral efficiency is achieved by removing the CP from OFDM, while reducing the computational complexity.

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) system is a key technology of 5G communications and a research focus of 6G communications in the future. It can be widely used in communication, radar, power and transmission systems. In the mmWave MIMO system, hybrid beamforming (HBF) is regarded as a potentially promising technology, because it keeps a good balance between hardware complexity and system performance. In this paper, we propose a new hybrid beamforming scheme for the multi-user MIMO (MU-MIMO) scenario. We first design the analog beamforming by trying to avoid the loss of information to maximize the sum-rate of the communication system. Then based on the idea of successive interference cancellation (SIC), we develop the successive block diagonalization (SBD) method to eliminate interference between two adjacent users, which can avoid the computation caused by the influence between users that are far apart. Unlike the baseband BD technology, the proposed scheme has low complexity. The simulation results are shown to validate the proposed scheme.

With the large number of antennas and subcarriers the overhead due to pilot transmission for channel estimation can be prohibitive in wideband massive multiple-input multiple-output (MIMO) systems. This can degrade the overall spectral efficiency significantly, and as a result, curtail the potential benefits of massive MIMO. In this paper, we propose a neural network (NN)-based joint pilot design and downlink channel estimation scheme for frequency division duplex (FDD) MIMO orthogonal frequency division multiplex (OFDM) systems. The proposed NN architecture uses fully connected layers for frequency-aware pilot design, and outperforms linear minimum mean square error (LMMSE) estimation by exploiting inherent correlations in MIMO channel matrices utilizing convolutional NN layers. Our proposed NN architecture uses a non-local attention module to learn longer range correlations in the channel matrix to further improve the channel estimation performance.We also propose an effective pilot reduction technique by gradually pruning less significant neurons from the dense NN layers during training. This constitutes a novel application of NN pruning to reduce the pilot transmission overhead. Our pruning-based pilot reduction technique reduces the overhead by allocating pilots across subcarriers non-uniformly and exploiting the inter-frequency and inter-antenna correlations in the channel matrix efficiently through convolutional layers and attention module.

In this paper, we develop the channel estimation algorithm for a massive multiple input multiple output (MIMO) system using one-bit analog-to-digital converters (ADCs). Although, one-bit quantization significantly reduces the deployment cost and power consumption of the massive MIMO, the great distortion induced by one-bit quantization makes channel estimation more difficult. Therefore, a channel estimation method by executing a deep neural network (DNN) over multiple signal segments is proposed for the uplink of one-bit massive MIMO. The average of the DNN’s outputs throughout all the segments is the final channel estimate for a transmission block. In order to improve the estimation accuracy without increasing the length of pilot, the data symbols got based on the initial channel estimates are used as the other part of pilots to refine the estimation result. Moreover, a sliding window based pilot segment method is adopted to increase the number of signal segments with a same pilot length. The simulation results show that the proposed scheme outperforms least squares (LS) and Bussgang linear minimum mean squared error (BLMMSE) channel estimators in the whole signal-to-noise-ratio (SNR) region.