Millimeter-Wave Massive MIMO Testbed with Hybrid Beamforming

Hybrid beamforming is a viable method for lowering the complexity and expense of massive multiple-input multiple-output systems while achieving high data rates on track with digital beamforming. To this end, the purpose of the research reported in this paper is to assess the effectiveness of the three architectural beamforming techniques (Analog, Digital, and Hybrid beamforming) in massive multiple-input multiple-output systems, especially hybrid beamforming. In hybrid beamforming, the antennas are connected to a single radio frequency chain, unlike digital beamforming, where each antenna has a separate radio frequency chain. The beam formation toward a particular angle depends on the channel state information. Further, massive multiple-input multiple-output is discussed in detail along with the performance parameters like bit error rate, signal-to-noise ratio, achievable sum rate, power consumption in massive multiple-input multiple-output, and energy efficiency. Finally, a comparison has been established between the three beamforming techniques.

SummaryThe advanced wireless communication system requires abridged energy consumption, enhanced data rate, and good signal coverage. The massive MIMO technology for 5G systems has been developed to accommodate several users simultaneously with superior throughput. The claim for high data rate wireless communication services is expanding quickly as time goes. Thus, the key difficulty is that as the number of users grows, the number of phase shifters grows as well, causing the system to consume more power; as a result, the system's energy efficiency decreases. Hybrid beamforming has recently emerged as an attractive technique for millimeter‐wave (mmWave) communication systems. The analog beamformer in the RF domain and digital beamformer in the baseband are coupled through a minimal number of RF chains in hybrid beamforming architecture. Hybrid beamforming utilizes fewer RF (radio frequency) chains than the total number of antennas to have a lower energy consumption design. The hybrid beamforming for a mmWave‐based massive MIMO system through different phase shifter selection mechanisms is proposed to achieve the highest energy efficiency for mmWave communications systems. The fully connected with phase shifter selection, sub‐connected with phase shifter selection (SPSS), and fully connected and sub‐connected with phase shifter selection with halved and doubled switches are considered for this research. The simulation results show the SPSS with halved switch outperforms on energy efficiency.

Massive multiple-input multiple-output (MIMO) theoretical performance results have attracted the attention of the community due to the possibility of increasing the spectral efficiency in wireless communications. The performance potential is mainly conditioned to the use of digital beamforming techniques which demand one radio-frequency (RF) chain per antenna element. For large arrays, this implementation may result in high complexity, power consumption, and cost. To reduce the number of RF chains, we use a hybrid beamforming (HB) architecture of an analog beamformer implemented by using phase-shifters and a low-dimensional digital beamformer. The performance of the HB depends on the resolution of the phase-shifters. However, very few works in the literature take into account finite phase-shifters. In this paper, we address the problem of designing HB in frequency selective channels using finite-resolution phase-shifters. The strategy is to exploit the second-order statistics of the channel and a least-square formulation to obtain the discrete phase of each phase-shifter. The digital part is derived based on analog solution to maximize the single-user MIMO system sum-rate. This solution requires a number of RF chains compared to the rank of the spatial covariance matrix which is far lower than ones demanded to implement the full digital beamforming. The simulation results show that the proposed technique can achieve a sum-rate performance very close to that of the digital beamforming assuming low-rank channels.

Massive multiple-input multiple-output (MIMO) will be employed in the 5th generation mobile communications (5G). To implement the massive MIMO, hybrid beamforming (HB) is one of the most promising techniques, because HB is composed of the analog beamforming (AB) and the digital beamforming (DB) and thus can decrease the number of baseband and radio frequency (RF) circuits significantly. Furthermore, HB can reduce a much larger amount of cost and power consumption than the full DB (FDB) configuration, which requires one baseband and RF circuit for each antenna element. This paper proposes a scheme to estimate both phases for phase rotators in AB and a precoding matrix for DB so that HB can approximate the block diagonalization (BD), which can nullify interference from the undesired users and form efficient transmit beams. For time division duplex (TDD) multiuser MIMO communications, the proposed scheme estimates the FDB channel matrix in the uplink (UL) and the phase for the phase rotators of AB in the downlink (DL). After obtaining an equivalent channel matrix from both the estimated FDB channel matrix and the AB precoding matrix, the scheme estimates the DB precoding matrix in DL from the equivalent channel matrix so that HB can approximate BD in DL. Computer simulations demonstrate that HB employing the proposed scheme can achieve DL average bit error rate (BER) performances that are little inferior to those of FDB.

In this paper, we propose a beamforming design for dual-functional radar-communication (DFRC) systems at the millimeter wave (mmWave) band, where hybrid beamforming and sub-arrayed MIMO radar techniques are jointly exploited. We assume that a base station (BS) is serving a user equipment (UE) located in a Non-Line-of-Sight (NLoS) channel, which in the meantime actively detects multiple targets located in a Line-of-Sight (LoS) channel. Given the optimal communication beamformer and the desired radar beampattern, we propose to design the analog and digital beamformers under non-convex constant-modulus (CM) and power constraints, such that the weighted summation of the communication and radar beamforming errors is minimized. The formulated optimization problem can be decomposed into three subproblems, and is solved by the alternating minimization approach. Numerical simulations verify the feasibility of the proposed beamforming design, and show that our approach offers a favorable performance tradeoff between sensing and communication.

Hybrid beamforming is a promising technology to improve the energy efficiency of massive MIMO systems. In particular, subarray hybrid beamforming can further decrease power consumption by reducing the number of phase-shifters. However, designing the hybrid beamforming vectors is a complex task due to the discrete nature of the subarray connections and the phase-shift amounts. Finding the optimal connections between RF chains and antennas requires solving a non-convex problem in a large search space. In addition, conventional solutions assume that perfect channel state information (CSI) is available, which is not the case in practical systems. Therefore, we propose a novel unsupervised learning approach to design the hybrid beamforming for any subarray structure while supporting quantized phase-shifters and noisy CSI. One major feature of the proposed architecture is that no beamforming codebook is required, and the neural network is trained to take into account the phase-shifter quantization. Simulation results show that the proposed deep learning solutions can achieve higher sum-rates than existing methods.

Millimeter wave communications (mmWave) has been considered a key enabling technique for 5G systems since it offers orders of magnitude more spectrum than current frequency bands. Unlike conventional multiple input multiple output (MIMO) networks, beamforming in millimeter-wave systems cannot be accomplished using digital techniques since only a limited number of analog-to-digital converters and mixers can be handled due to their cost and power consumption. Recently, there has been a lot of interest in a hybrid beamforming transceiver design, which includes an analog and digital beamformer, as a less expensive alternative. However, the optimal design for these hybrid beamformers has not yet been fully established. In order to approach the complex performance of the fully digital beamformer, this work will provide efficient alternative minimization techniques for a hybrid beamforming structure, namely the manifold-based geometric fully-connected antennas. According to simulation data on bandwidth efficiency, the proposed iterative algorithm performs much better than the baseline hybrid beamforming approach, namely the MMSE-MP algorithm (about 6.8% for a particular situation). Additionally, based on the recommended techniques, comparing the simulation results of the two hybrid beamforming structures will yield vital design information.

On-site study was held to investigate the influences of load factor on power consumption and energy performance of water-cooled air conditioning (WCAC) system. A comprehensive measurement has been conducted to a WCAC plant of a supermall building located in West Java, Indonesia. Hourly and daily power consumption and performance characteristics of the WCAC system at varied load factors for a period of one year were evaluated. Regression analyses were developed to evaluate the influences of load factor on the WCAC power consumption and performance. The WCAC system was found to operate with varied load factor ranging from 70 percent to 100 percent. Power consumption of the system was strongly influenced by the load factor. Power consumption boosted when the power factor increased. The increase of load factor was also found to significantly improve the cooling capacity but reduce the coefficient of performance (COP). The best potential performance of the investigated WCAC system could be obtained at load factor between 70 percent and 85 percent.

In order to enhance the coverage and capacity of fifth generation cellular networks (5G) which is expected to be employed soon, massive multiple-input multiple-output (m-MIMO) and millimeter-wave (mm-Wave) technologies are being developed as solutions for these challenges. However, for large-scale antenna arrays that are anticipated in MIMO technology, the utilization of traditional fully-digital MIMO beam-forming methods is not practical, requiring one radio frequency (RF) chain per antenna element, leading to high cost and high power consumption. In order to reduce the RF chains number, hybrid analog and digital beam-forming has been studied and proposed. In this paper, a hybrid beam-forming structure is proposed in a massive MIMO communication channel with multiple users, each user having different number of data streams, with additive white Gaussian noise (AWGN) interfering with the signal throughout the path. The bit error rate (BER) of each transmitter for each user will be studied and compared under different circumstances for the proposed system VS different fully digital beam-forming methods, along with the spectral efficiency of the proposed hybrid beam-forming structure for each channel compared to fully digital, or in other words optimal beam-forming. Results of these comparisons will be shown and discussed to conclude the advantages and disadvantages of the proposed scheme.

Millimeter wave(mmWave) supported Massive MIMO systems with directional beamforming enables gigabit-per-second data rates with large bandwidth and achieves sufficient link margin but algorithms, system operations still rely on microwave-based modifications. The mmWave channel has a very poor scattering nature, so sensing channel parameters need very large training pilot signals. The Presence of large antenna arrays with analog beamforming and giga-sample mixed-signal devices cause computational complexities and high power consumption. Realizing these issues, we propose a mmWave specific deep-learning enabled adaptive-compressive based channel sensing and hybrid beamforming solution that jointly optimizes the channel vectors in an unsupervised manner and generate RF beamforming/combining vectors that achieve precoding gains comparable to near-optimal achievable rates. Simulation results show that for a 64 by 64 transmit-receive antenna system, existing exhaustive search algorithm needs 4096 channel pilot signals, compressive algorithms need minimum 300 pilots. In contrast, our proposed solution needs only 4 pilots to sense the channel parameters and directly predicts RF beamforming/combining vectors for the hybrid architectures. Lesser channel training pilots cause low power consumption and proposed hybrid RF precoding solution overcomes hardware limitations faced by analog-only beamforming that shows up a promising development for the solution of mmwave channel estimation and hybrid precoding problem.

Hybrid analog and digital beamforming is a promising technique for large-scale MIMO systems since it can achieve a performance close to the performance of the conventional fully-digital beamforming schemes, but with much lower hardware implementation complexity and power consumption. One of the major challenges in hybrid beamforming is to design the hybrid beamformers for broadband systems with frequency-selective channels. This is because in broadband systems, it is desirable to design the same analog beamformers for the entire band while adapting digital beamformers in each frequency tone. In this paper, we consider the hybrid beamforming design for large-scale MIMO systems with orthogonal frequency division multiplexing (OFDM) modulation. Specifically, we propose a unified heuristic design for two different hybrid beamforming structures, the fully-connected and partially-connected structures, to maximize the overall spectral efficiency of a broadband system. Numerical results show that the proposed algorithm outperforms the existing hybrid beamforming designs and further the proposed algorithm for the fully-connected structure can achieve spectral efficiency close to that of the optimal fully-digital solution with much less number of RF chains.

In the search for enhanced wireless communication systems, multibeam phased array receivers have gained prominence. They promise to significantly boost data rates, reduce latency, and improve reliability. However, implementing multibeam receivers with traditional beamforming techniques can be challenging due to the high computational demands and the need for multiple radio frequency (RF) chains. Hybrid beamforming combines the advantages of digital and analog beamforming to strike a balance between performance and complexity. Our research delves into the following aspects of Hybrid beamforming for multibeam phased array receivers: Reduced Hardware Complexity: By blending digital and analog beamforming, we reduce the number of required RF chains, making multibeam receivers more cost-effective. Enhanced Beam Steering: Hybrid beamforming maintains precise control over multiple beams, ensuring efficient signal reception and transmission. Real-World Applications: We discuss practical applications in 5G and beyond, satellite communications, and radar systems. The paper delves into the fundamental concepts of analog and digital beamforming, illustrating their respective strengths and limitations. It then presents the architecture of hybrid beamforming systems, emphasizing the synergistic integration of analog and digital components. Special attention is given to the design considerations of beamforming networks, antenna arrays, and the beam steering mechanism. KEYWORDS Hybrid Beamforming , Phased Array , Multi-Beam , Analog Beamforming , Digital Beamforming , Beamforming Networks , Antenna Array , Beam Steering , Spatial Multiplexing , Precoding , Channel Estimation , Interference Mitigation , Power Consumption , Millimeter Wave (mmWave) , 5G and Beyond .

Economical and energy-efficient analog/digital hybrid beamforming has been widely considered as a promising approach for millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. While most hybrid beamforming techniques consider a fully-connected structure with a large number of phase shifters (PSs), the partially-connected structure has drawn more attention recently since it requires much less PSs and can further improve energy-efficiency. However, the impractical assumption of infinite or high resolution of PSs in existing solutions frustrates the real-world deployment of hybrid beamforming designs, and low- resolution PSs are typically adopted to reduce the hardware complexity and power consumption. In an effort to achieve maximum hardware efficiency, this paper focuses on the partially-connected architecture with one-bit (binary) PSs and considers the problem of joint hybrid precoder and combiner design for such mmWave MIMO systems. We propose to successively design the analog beamformers associated with each pair of sub- array, aiming at conditionally maximizing the spectral efficiency. A novel binary analog precoder and combiner optimization algorithm is proposed under a rank-1 approximation of the interference-included equivalent channel with polynomial complexity in the number of antennas. Then, the digital precoder and combiner are computed based on the obtained effective baseband channel to further enhance the spectral efficiency. Simulation results demonstrate the advantages of proposed hardware-efficiency hybrid precoder and combiner design.

Wireless communication at higher frequency bands has attracted research interest for fifth generation and beyond (5GB) wireless networks due to the large amount of unused bandwidth at these frequencies. However, there are substantial challenges associated with higher frequency bands due to the high path loss of the propagation environment and the high power consumption of the transceivers. Hybrid beamforming with massive multiple-input multiple-output (MIMO) has emerged as a solution to these problems by combining the performance and flexibility of digital beamforming with the energy efficiency of analog beamforming. Optical beamforming has recently been considered as an alternative to implement the analog component of a hybrid beamformer, which may offer improvements in size, weight and power consumption in comparison to conventional electronics. This paper proposes a new approach to implement an optical beamforming system based on photonic vector modulators using tunable photonic filters. Our experimental demonstration of the proposed optical beamformer shows that microring resonator (MRR)-based photonic vector modulators can be calibrated to achieve a root-mean-square (RMS) phase error of better than 2° and an amplitude error of 0.3 dB. Our findings identify a pathway to realize large-scale, fully-connected hybrid beamformers by leveraging compact and low loss photonic resonators.

Although massive multiple-input multiple-output (MIMO) can enhance the overall system performance significantly, it could suffer from high cost and power consumption issues due to using a large number of radio frequency (RF) chains. Two different approaches are commonly exploited to overcome these issues. The first approach is using hybrid beamforming, which consists of analog and digital beamforming, to reduce the total number of RF chains. The second approach is adopting low-resolution analog-todigital converters (ADCs) for each RF chain. For both approaches, channel estimation becomes a difficult task. This paper addresses the problem of channel angle of arrival (AoA) estimation in massive MIMO using both hybrid beamforming and one-bit magnitudeaided (OMA) ADCs. An iterative algorithm is developed to estimate the channel AoA, and the appropriate threshold per iteration is analyzed. Numerical results show that the proposed technique can achieve sufficient AoA estimation performance with practical values of the signal-to-noise ratio (SNR).