Hybrid Beamforming via the Kronecker Decomposition for the Millimeter-Wave Massive MIMO Systems

Despite its promising performance gain, the realization of mmWave massive MIMO still faces several practical challenges. In particular, implementing massive MIMO in the digital domain requires hundreds of RF chains matching the number of antennas. Furthermore, designing these components to operate at the mmWave frequencies is challenging and costly. These motivated the recent development of hybrid-beamforming where MIMO processing is divided for separate implementation in the analog and digital domains, called the analog and digital beamforming, respectively. Analog beamforming using a phase array introduces uni-modulus constraints on the beamforming coefficients, rendering the conventional MIMO techniques unsuitable and call for new designs. In this paper, we present a systematic design framework for hybrid beamforming for multi-cell multiuser massive MIMO systems over mmWave channels characterized by sparse propagation paths. The framework relies on the decomposition of analog beamforming vectors and path observation vectors into Kronecker products of factors being uni-modulus vectors. Exploiting properties of Kronecker mixed products, different factors of the analog beamformer are designed for either nulling interference paths or coherently combining data paths. Furthermore, a channel estimation scheme is designed for enabling the proposed hybrid beamforming. The scheme estimates the AoA of data and interference paths by analog beam scanning and data-path gains by analog beam steering. The performance of the channel estimation scheme is analyzed. In particular, the AoA spectrum resulting from beam scanning, which displays the magnitude distribution of paths over the AoA range, is derived in closed-form. It is shown that the inter-cell interference level diminishes inversely with the array size, the square root of pilot sequence length and the spatial separation between paths.Comment: Submitted to IEEE JSAC Special Issue on Millimeter Wave Communications for Future Mobile Networks, minor revisio

A wireless precoding technique for millimetre-wave MIMO system based on SIC-MMSE

A communication method is proposed using Minimum Mean Square Error (MMSE) precoding and Successive Interference Cancellation (SIC) technique for millimetre-wave multiple-input multiple-output (mm-Wave MIMO) based wireless communication system. The mm-Wave MIMO technology for wireless communication system is the base potential technology for its high data transfer rate followed by data instruction and low power consumption compared to Long-Term Evolution (LTE). The mm-Wave system is already available in indoor hotspot and Wi-Fi backhaul for its high bandwidth availability and potential lead to rate of numerous Gbps/user. But, in mobile wireless communication system this technique is lagging because the channel faces relative orthogonal coordination and multiple node detection problems while rapid movement of nodes (transmitter and receiver) occur. To improve the conventional mm-wave MIMO nodal detection and coordination performance, the system processes data using symbolized error vector technique for linearization. Then the MMSE precoding detection technique improves the link strength by constantly fitting the channel coefficients based on number of independent service antennas (M), Signal to Noise Ratio (SNR), Channel Matrix (CM) and mean square errors (MSE). To maintain sequentially encoded user data connectivity and to overcome data loss, SIC method is used in combination with MMSE. MATLAB was used to validate the proposed system performance

Efficient beamforming techniques for millimeter wave MIMO systems

Due to the continued evolution of 5G standards, the need for higher rates of data, lower latency network access, and implementations that are more energy efficient have become clear. To enable wireless communications at rates over tens of Gbps, the wide bandwidth of mmWave spectrum can be exploited. Beamforming (or precoding) is used to compensate for the high path loss in the mmWave frequencies. Although the small wavelength of mmWave signals tolerates a large number of antennas being crowded into a small area, the high-power consumption and cost of mixed signal components make it difficult to earmark a separate radio frequency (RF) chain for each antenna. The addition of analog processing to the digital precoding, known as hybrid beamforming (HB), is an efficient solution for massive MIMO systems, which results in a number of active RF chains lower than antennas. Extensive work has determined that HB can approach the performance of the optimal precoder, assuming optimal antennas implementation, ideal environment, and full rank effective channel. However, depending on the implementation techniques adopted for the RF precoder and other system parameters, such as the number of active RF chains, the limited distance between antennas, and the geometric placement of both ends of wireless systems, the effective channel matrix could be rank deficient, which degrades the performance of the system. The first part of this dissertation provides a new set of solutions for HB that approaches the performance of a fully digital beamformer in terms of MIMO multiplexing gains, by taking careful consideration of the matrix of the effective channel, appropriately selecting independent columns of the analog precoder, calculating the least squares solution for the digital precoder, and selecting its digital gain based on the selected columns of the RF precoder and the effective channel. Multiple hybrid precoding schemes are developed and proposed, taking careful consideration of the complexity and power consumption of the system, appropriately reducing them while keeping the same level of quality of service. The second part considers a hybrid precoding scheme with low-resolution phase shifters (PSs) in a mmWave MIMO system. Finite resolution PSs are a good alternative because they need simpler hardware implementation than those with infinite resolution. The proposed system considers separating the antennas from each other by sufficient distance to ensure a less correlated channel, and thus, a minimal loss in the capacity, which is our objective. The capacity gap between the optimal precoder and our proposed hybrid precoding with low-resolution PSs can be reduced without increasing the number of RF chains. In particular, we focus on properly selecting the weights of the RF beamformer, which create independent beams that send data streams to the receiver. As a result, the structure of the multipath propagation channel will be exploited by the transmitted beams, which maximizes the system capacity. Finally, this dissertation investigates technologies and methods that can be adopted to lower the cost and power of HB and are able to maintain more users while keeping higher data rates. The idea of connecting the RF chains to a subset of the antennas and spacing those sub-arrays to provide additional diversity gains is a promising approach. However, this spacing technique cannot be implemented at the receiver side due to its limited size. To reduce power at the receiver, low-resolution analog-to-digital converters (ADCs) can be implemented. This idea is powerful because with a simple circuit and digital combining based on coarse quantization, it allows a gain of performance, maintaining a low hardware complexity, and saving energy at the same time. It will be of great interest to implement and develop techniques that can merge the idea of distanced sub-arrays for the uplink between base stations with the low-resolution ADCs on the downlink

SIC-MMSE method based wireless precoding technique for millimetre-wave MIMO system

A communication method is proposed using Minimum Mean Square Error (MMSE) precoding and Successive Interference Cancellation (SIC) technique for millimetre-wave multiple-input multiple-output (mm-Wave MIMO) based wireless communication system. Background: The mm-Wave MIMO technology for wireless communication system is the base potential technology for its high data transfer rate followed by data instruction and low power consumption compared to Long-Term Evolution (LTE). The mm-Wave system is already available in indoor hotspot and Wi-Fi backhaul for its high bandwidth availability and potential lead to rate of numerous Gbps/user. But, in mobile wireless communication system this technique is lagging because the channel faces relative orthogonal coordination and multiple node detection problem while rapid movement of nodes (transmitter and receiver) occur. Methods/Improvement: To improve the conventional mm-wave MIMO nodal detection and coordination performance, the system processes data using symbolized error vector technique for linearization. Then the MMSE precoding detection technique improves the link strength by constantly fitting the channel coefficients based on number of independent service antennas (M), Signal to Noise Ratio (SNR), Channel Matrix (CM) and mean square errors (MSE). To maintain sequentially encoded user data connectivity and to overcome data loss, SIC method is used in combination with MMSE. Improvements: MATLAB was used to validate proposed system performance. Simulation analysis shown that, with the increase number of antennas use, the spectral efficiency also increased and higher then millimetre-wave MIMO or Single MMSE system. This research observed that, hybrid controller or combined control method have the better efficiency then single method, where SIC-MMSE based hybrid controller is a good example

Model-Driven Beamforming Neural Networks

Beamforming is evidently a core technology in recent generations of mobile communication networks. Nevertheless, an iterative process is typically required to optimize the parameters, making it ill-placed for real-time implementation due to high complexity and computational delay. Heuristic solutions such as zero-forcing (ZF) are simpler but at the expense of performance loss. Alternatively, deep learning (DL) is well understood to be a generalizing technique that can deliver promising results for a wide range of applications at much lower complexity if it is sufficiently trained. As a consequence, DL may present itself as an attractive solution to beamforming. To exploit DL, this article introduces general data- and model-driven beamforming neural networks (BNNs), presents various possible learning strategies, and also discusses complexity reduction for the DL-based BNNs. We also offer enhancement methods such as training-set augmentation and transfer learning in order to improve the generality of BNNs, accompanied by computer simulation results and testbed results showing the performance of such BNN solutions

Physical Layer Service Integration in 5G: Potentials and Challenges

High transmission rate and secure communication have been identified as the key targets that need to be effectively addressed by fifth generation (5G) wireless systems. In this context, the concept of physical-layer security becomes attractive, as it can establish perfect security using only the characteristics of wireless medium. Nonetheless, to further increase the spectral efficiency, an emerging concept, termed physical-layer service integration (PHY-SI), has been recognized as an effective means. Its basic idea is to combine multiple coexisting services, i.e., multicast/broadcast service and confidential service, into one integral service for one-time transmission at the transmitter side. This article first provides a tutorial on typical PHY-SI models. Furthermore, we propose some state-of-the-art solutions to improve the overall performance of PHY-SI in certain important communication scenarios. In particular, we highlight the extension of several concepts borrowed from conventional single-service communications, such as artificial noise (AN), eigenmode transmission etc., to the scenario of PHY-SI. These techniques are shown to be effective in the design of reliable and robust PHY-SI schemes. Finally, several potential research directions are identified for future work.Comment: 12 pages, 7 figure