Optimized hybrid beamforming method with limited feedback in Massive MIMO systems

Frequency division duplex (FDD) downlink massive multiple-input and multiple-output (MIMO) systems encounter difficulty in acquiring the sparsity and threshold parameter. As a remedy, a time-frequency block sparse channel estimation method based on compressed sensing (CS) is proposed that consists of the generalized block adaptive matching pursuit (gBAMP) algorithm. By using the time-frequency joint block sparsity of massive MIMO systems, the index sets selected in the iterative process of the algorithm are further optimized to improve the stability of the algorithm. Then, in the absence of the threshold parameter, the adaptive iterative stop condition is determined using the residual based on the Frobenius norm (F-norm), and the effectiveness of the method is proved. Simulations show that the algorithm can quickly and accurately estimate the massive MIMO channel information and outperforms its counterparts.

The development of massive multiple input and multiple output (MIMO) brings various advantages in the fifth generation (5G) wireless communication systems, which can achieve high spectral efficiency and energy efficiency. In this paper, considering the security and energy efficiency in massive MIMO, a physical layer security approach named chaotic antenna-index three-dimensional modulation (CATM) encryption scheme is proposed, which adds an additional dimension to the conventional two-dimensional modulation techniques. For security, the keys generated by chaotic system are used to protect information bits, antenna combination and mapping mode (ACMM). Both analytical and simulation results show that the proposed CATM scheme offers not only considerable security but also higher reliability and energy efficiency than conventional massive MIMO.

The authors consider downlink transmission in a multi-cell massive multiple-input and multiple-output (MIMO) system and focus on decreasing the feedback overhead caused by cell cooperation in constant envelope precoding (CEP). In the literature, the single-cell case for CEP with perfect channel state information (CSI) has been studied. Here, considering full cooperation among the cells, they develop CEP for the multi-cell case. They devise a low overhead centralised construction for CEP which employs limited cooperation among the cells, providing higher system throughput. To achieve the minimum feedback overhead, a distributed realisation of CEP is proposed where each cell locally performs CEP. Furthermore, a new optimisation problem is solved to compensate for the effects of pilot contamination. In addition, to reduce the computational complexity, a relaxed iterative form is presented for the limited cooperation and the distributed scenarios. Numerical results show that in the proposed structures, despite a tremendous decrease in the system overhead, the performance confronts merely an insignificant degradation, <10%, compared to the full-cooperation case. Moreover, in the presence of imperfect CSI, the performance of the distributed structure whose imperfect CSI is compensated for, approaches the performance of this structure in the perfect CSI scenario.

In this paper, a novel iterative detection technique that combines deep learning (DL) and the approximated algorithm of successive over relaxation (SOR) is proposed to achieve high reliability and reduce the computational complexity. Recently, as the demanded data rates increase, the massive multiple-input and multiple-output (MIMO) system has drawn attention in wireless communication. In massive MIMO, the implementation of traditional detectors for high reliability has become impractical, and the reduction for the complexity of detectors has emerged as a practical implementation challenge. The existing DL-based detection technique of orthogonal approximate message passing network (OAMPNet) can provide high detection performance. However, the computational complexity is too high for the implementation in massive MIMO systems. The proposed detection technique uses SOR algorithm to reduce the computational complexity, and the relaxation parameter of SOR is adaptively determined by a learning algorithm. A non-linear estimator using the DL algorithm is combined with the SOR algorithm to achieve high reliability, and regardless of the size of the MIMO system, only the size of the DL architecture determines the complexity of the non-linear estimator. Simulation results show that the proposed detector outperforms the conventional linear detector based on minimum mean square error (MMSE) and achieves high reliability with lower complexity than OAMPNet in various channel environments with spatial correlation.

Channel state information (CSI) acquisition is one of the major challenges for massive MIMO systems to enable high efficiency MIMO transmissions. Limited feedback schemes, which quantize CSI into limited number of bits and feed these bits back to the base station (BS), are widely used in real-life mobile networks for BSs to obtain downlink CSI. Practical limited feedback schemes usually have large feedback granularity and introduce severe nonlinear quantization error due to the constraint on feedback overhead. It is hard for legacy signal processing technologies to reconstruct CSI accurately for high efficiency downlink transmissions. Different from the legacy signal processing technologies, deep neural networks trained with big data have strong abilities to fit any nonlinear functions and learn the complex signal model behind the big data, which is a useful tool to solve problems that are nonlinear or hard to be described mathematically. Motivated by these observations, we consider a deep learning based approach for BS receivers to reconstruct CSI based on the limited feedback bits from user equipment (UE). The CSI reconstruction is similar with the image super-resolution which reconstructs a high resolution image from a low resolution one based on the structure information learned by the neural network itself. Performance evaluations demonstrate that the proposed method outperforms the legacy algorithms. The improvement on spectrum efficiency is about 25%. It can also reconstruct the CSI with lower density reference signal (RS) than that in current specification, e.g., the RS density can be 1/32 of current one. Therefore, the reference signal and feedback overhead can be reduced with our method. These results show the potential gain of applying deep learning to physical layer signal processing at the receiver side only, which has little air-interface impact and does not require much effort on enhancing network specifications. Therefore, it is feasible to be deployed in current 5th generation mobile networks with the pace of network evolutions, and opens the gate of intelligent physical layer design for future generations of mobile networks.

In this paper, we investigate link adaption in massive Multiple-Input and Multiple-Output(MIMO) system, where the base station is equipped with a large number of antennas. Specifically for the linear minimum mean square error (MMSE) receiver at the base station, the achievable ergodic rate is used to calculate channel quality indicator (CQI) for each user equipment (UE). We propose a rate calculation method based on the free deterministic equivalent, which relies on the statistical channel state information (CSI). The proposed method has much lower computational complexity than the conventional link adaption method such as exponential effective signal to interference plus noise ratio (SINR) mapping (EESM). Simulation results show that the the spectral efficiency obtained by the proposed method can approach almost the same level as EESM, while much lower computational complexity is needed.

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.

In fifth-generation (5G) technology, massive multiple input and multiple output (MIMO) plays a very significant role in spectral efficiency enhancement and interference cancellation. It also does beam forming using maximal ratio combining (MRC) to obtain high throughput and good quality of service (QoS). All these are achievable by using higher-order modulation technique, transmission power control, and proper usage of radio resources in RF channel with minimizing interference. As MIMO technology uses multiple transmitting and receiving antennas, the spectral resources can be shared between multiple UEs simultaneously. Further, the 5G network is multi-tier and ultra-dense heterogeneous cellular network (HetNet) that accomplishes efficient spectrum usage through carrier aggregation. For improving spectrum resource efficiency (SRE) and RF channel utilization, source bits must be accurately transmitted to the destination. This can be quantified with high symbol efficiency and minimum Bit Error Rate (BER). In this paper, BER for various RF channels was investigated with AWGN noise, Rayleigh fading, and MIMO capacity for multiple UEs. The experimentation is done using MATLAB14b, and the iteration is carried out till convergence is obtained.KeywordsMIMOBER5GSmall cellAWGNRayleigh fadingHeterogeneous cellular network

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.

Localization beyond 5G wireless communication systems is a key technology enabler. It has attracted increasing research as the number of antennas in the wireless transmitters. Localization of user terminals is important communications technology, particularly for a better quality of service, improved security, and location awareness applications. Massive multiple-input and multiple-output (MIMO) is considered one of the key technologies in wireless communications. Therefore, it attracts a significant amount of research. In this paper, an estimate of the location of the user terminal with high precision is proposed by using only a single massive MIMO cell. The massive MIMO system has a high degree of freedom and a high number of antennas. which is utilized to estimate the angle of arrival (AoA) with high accuracy even with highly spatially correlated users then the location is estimated. The simulation finding results show high accuracy as compared to the previously published work. The estimation error in AoA is 1 % and 5 % in distance at 16dB SNR.

With the deployment of small cell networks, low-cost backhaul schemes for small cell base stations (SBSs) have attracted great attentions. Self-backhaul using cellular communication technology is considered as a promising solution. Although some excellent works have been done on self-backhaul in small cell networks, most existing works do not consider the recent advances of full-duplex (FD) and massive multiple-input and multiple-output (MIMO) technologies. By jointly considering them, we propose a novel self-backhaul scheme for small cell networks in this paper, where the macro base station is equipped with massive MIMO antennas, and the SBSs have the FD communication capability. Then, we can achieve the simultaneous transmissions of the access link of users and the backhaul link of SBSs in the same frequency and then spectrum efficiency is improved. Furthermore, we formulate the power allocation problem of the MBS and SBSs as an optimization problem. Because the formulated power allocation problem is a non-convex problem, we transfer the original problem into a difference of convex program by using successive convex approximation method and variable substitution, and then solve it using a constrained concave convex procedure based iterative algorithm. Finally, extensive simulations are conducted to verify the effectiveness of the proposed scheme.

Massive Multiple-Input and Multiple-Output (M-MIMO) is considered a key technique in fifth-generation (5G) and beyond 5G (B5G) standards. Among the various benefits that M-MIMO provides for wireless systems, the fact that it allows reducing interference levels stands out, which can be translated into an spectral efficiency (SE), energy efficiency (EE) and hardware efficiency (HE) improvement. In this sense, this work analyzes the interference reduction factor (IRF) in the uplink of M-MIMO systems over uncorrelated Rician fading channels. Exact closed-form and asymptotic expressions are derived to evaluate the IRF, which are validated via Monte-Carlo simulations in some representative scenarios. The results show that the IRF behavior is different in Rayleigh and Rician fading channels. In particular, increasing the specular component power, or equivalently, the Rician K factor, affects drastically the IRF, i.e., the capability of the M-MIMO system to reduce interference is affected.

Orthogonal frequency division multiplexing (OFDM) is a practical technique for communicating over broadband channels in multi-path fading environments. It is well known that varying the power allocation from frequency tone-to-frequency tone, commonly called power loading, can improve capacity or error rate performance. Unfortunately, the implementation of power loading is complicated by the fact that the transmitter must have knowledge of the forwardlink channel. This assumption is often unrealistic, particularly in systems using frequency division duplexing (FDD). In this paper we present a limited feedback approach for power loading OFDM symbols. In this approach, the receiver, which is assumed to have full forwardlink channel knowledge, designs a power loading vector and conveys it to the transmitter over a limited rate feedback channel. The technique uses a codebook of power loading vectors known to both the transmitter and receiver. We design limited feedback schemes for error rate optimized and capacity optimized power loading. We show how to design quantizers using iterative optimization techniques from the theory of vector quantization for the case of error rate selection. In the capacity case, we characterize optimal feedback schemes for asymptotic signal-to-noise ratio (SNR) scenarios and present a simple method called multi-mode power loading that uses a codebook that switches subcarriers off and on. Simulation results show that the limited feedback techniques provide performance close to perfect channel knowledge power loading.

In a massive multiple-input multiple-output (MIMO) system, channel state information (CSI) is essential for the base station (BS) to achieve high performance gain. The user equipment (UE) needs to estimate CSI and then feeds it back to the BS in the frequency division duplexing (FDD) mode. Effective compression of CSI will significantly reduce the cost of channel feedback, and many deep learning (DL) based channel compression schemes have been proposed to achieve this goal. In this paper, rather than viewing CSI as images as in most existing works, we propose a new perspective of viewing CSI as an information sequence and analogize CSI feedback to a machine translation task. Further, we propose a novel sequence to sequence (Seq2Seq) model for CSI feedback composed of only recurrent neural networks and a small-scale fully connected layer, avoiding the convolution and pooling structure commonly used in current DL-based works. The advantage of this scheme is that it fully integrates the physical characteristics of the MIMO channel in the spatial domain into the structure of the neural network and avoids the loss of some prominent unsmooth or discontinuous features caused by the inappropriate convolution and pooling operation. Simulation results show that the proposed Seq2Seq model outperforms other DL-based CSI compression techniques under various communication scenarios.

The work of cooperative perception realized by mmWave Vehicle to everything (V2X) [3] has pointed out that vehicles require much network bandwidth to exchange sensor information for safe automated driving; e.g., they require higher than 1Gbps V2V bandwidth to ensure safe velocity of 70 km/h. On the other hand, some research works have shown that channel state information (CSI) feedback is able to mitigate interference for 5G frequency division duplexing (FDD) mmWave massive MIMO systems. Existing approaches either do not consider the characteristics of FDD mmWave V2X systems or assume all channels are ideal, i.e., uncorrelated and identical. To achieve higher throughput and low overhead for FDD mmWave V2X systems, there are two major issues: (1) how to acquire an accurate CSI under the environment of non-identical channels, and (2) how to effectively use such information to improve overall system throughput. Our idea is to jointly consider the characteristics of the angle of arrival (AoA) method and Khler manifolds to design of codebook feedback approach for mmWave V2X massive systems. That is, to get an accurate CSI, we leverage the measurement method with the idea of the AoA approach. With this AoA measurement, we propose a novel Khler manifolds codebook feedback approach and determine an optimal bit allotment over each mmWave V2X link for achieving better interference reduction as well as lower overhead for V2X CSI exchange. In addition, our analytic results validate that proposed AoA Khler manifolds codebook feedback scheme is more adequate than the traditional CSI feedback with regard to interference reduction. Simulations show that the proposed AoA Khler manifolds codebook feedback scheme can significantly improve the vehicle rate and reduce the overhead for V2V CSI exchange.