Millimeter Wave Massive MIMO Systems Research Articles

Sub-System Architecture for millimeter-wave massive MIMO systems

In this paper, we study the hybrid beamforming design for millimeter-wave (mmWave) massive multiple-input multiple-output (mMIMO) systems. The designing of hybrid beamforming for orthogonal frequency-division multiplexing (OFDM) systems is tasking since its analog beamforming is shared among all subcarriers. We adopt a two-step technique for designing the analog and digital beamforming separately in order to maximize the average achievable energy and spectral efficiency of frequency-selective mmWave mMIMO-OFDM systems. Firstly, the analog beamforming design is based on the viewpoint of sub-systems (SS) and the goal is to optimize the array gain and radio frequency chains. Secondly, the digital beamforming design is carried out by using the regularized channel diagonalization (RCD) and block diagonalization (BD) solutions. On the other hand, the BD solution is modified for single-user. Thus, we propose the use of SS-RCD for multi-user and SS-BD for single-user hybrid beamforming designs. The solutions provide interference suppression but differ in low-SNR performance when communicating to many mobile users via data streams. Simulation results present that our hybrid beamforming design outperforms several other designs.

A Survey on Beamspace Millimeter-wave Massive Mimo Systems: An Overview of Open Issues, Challenges, and Future Research Trends

Abstract: The channel sparsity is exploited by transforming the channel into an angular domain from the spatial domain at millimeter wave frequencies in Beamspace Multiple-Input Multiple-Output (B-MIMO) systems. Every beam in B-MIMO corresponds to an individual radio frequency (RF) chain, and thus, beam selection techniques can reduce the RF hardware complexity. RF chain limitation is needed for an ideal beamspace precoding scheme to attain good system performance across the entire bandwidth. New methods of beamspace channel estimation with a small amount of estimation error and low computational complexity are challenging in the beam domain due to the larger antenna arrays deployed. A novel beamspace processing technique to improve the direction of arrival estimation performance for different array configurations with improvement in system performance is desired in a 5G system. A detailed review of the B-MIMO system with its architecture and various beam selection techniques is presented in this paper. The scope of the work is elaborated by incorporating various existing beamspace precoding and channel estimation methods. The paper also presents various beamspace processing schemes for parameter estimation with a focus on the direction of arrival estimation performance.

Energy Efficiency Enhancement in User-Centric and Cell-Free Millimeter-Wave Massive MIMO Systems With Hybrid Beamforming

Energy Efficiency Enhancement in User-Centric and Cell-Free Millimeter-Wave Massive MIMO Systems With Hybrid Beamforming

Anti-Jamming Hybrid Beamforming design for Millimeter-Wave Massive MIMO systems

Anti-Jamming Hybrid Beamforming design for Millimeter-Wave Massive MIMO systems

A Two-Stage Fast Beam Search for Multi-User Millimeter-Wave Massive MIMO Systems

Millimeter-wave systems deploy spatial beam search for initial access. A narrow beam search results in significant discovery delays whereas wide beams cause high user misdetection. In this work, a novel two-stage fast beam search algorithm is proposed that utilizes a beamforming architecture with an appropriate antenna spacing for subarray design. The first stage employs analog beamforming to identify the coarse beam space. The second stage based on hybrid beamforming makes use of pattern multiplication for beam space refinement. Simulation results demonstrate that the proposed scheme achieves improved performance in comparison to existing techniques in terms of significantly reduced beam search latency with a marginal increase in user misdetection.

CNN-DPC algorithm for hybrid precoding in millimeter-wave massive MIMO systems

CNN-DPC algorithm for hybrid precoding in millimeter-wave massive MIMO systems

Energy and Spectral Efficiencies of Cell-Free Millimeter-Wave Massive MIMO Systems Under Rain Attenuation Based on Ray Tracing Simulations

Future wireless communication systems depend on the network’s ultra-densification, on the application of massive multiple-input multiple-output (mMIMO) techniques and on the use of higher frequency bands to satisfy the ever-increasing demands for capacity. The operation of cell-free (CF) networks in the millimeter wave (mmWave) spectrum combines those principles, because they are composed of multiple access points (APs) distributed over a geographic region which serve a small number of users. Despite the extensive available bandwidth, the mmWave spectrum imposes high path losses and significant atmospheric molecular absorption to the links. In addition, in this frequency range, rain attenuation can notably degrade communications. Therefore, this article presents a study of the impact of rain attenuation on CF networks operating in the mmWave spectrum, this is in the 26 GHz, 38 GHz and 73 GHz frequency bands, based on site-specific ray tracing simulations. The propagation simulation is characterized under the effects of reflection, diffraction, diffuse scattering, atmospheric molecular absorption, vegetation losses and rain attenuation. The channel model is characterized using a hybrid approach, with the large-scale parameters determined by ray tracing in an environment subject to random Rician small-scale fading. The system performance is measured by the sum spectral efficiency (SSE) and energy efficiency (EE). According to the results, it was observed that AP densification protects the network against the effect of rain attenuation. Furthermore, even in sparse networks, the CF system has low sensitivity with respect to the precipitation rate, resulting in relatively small reductions in the average SSE and EE.

Hybrid Precoding Based on Partial Connection for Millimeter-Wave Massive MIMO System

Aiming at poor system performance in existing partially connected hybrid precoding schemes, in this paper, we propose a partially connected hybrid precoding scheme based on alternating optimization with the auxiliary precoder. In particular, the spectral efficiency optimization problem is transformed into a sub-optimization problem under certain constraints. For each sub-optimization problem, we use the singular value decomposition method to optimize the analog precoder and digital precoder alternately, and optimal solutions are provided for both digital and analog precoders in each alternate iteration. Moreover, we design a heuristic algorithm to calculate the initial analog precoder as the starting point for alternating optimization, which speeds up the convergence speed. The simulation results show that the proposed algorithm is at least 16.2% higher than the existing two traditional partially connected algorithms at 20 dB received SNR with almost no increase in complexity.

Design of Hybrid Precoding for Millimeter-Wave Massive MIMO System

Design of Hybrid Precoding for Millimeter-Wave Massive MIMO System

Joint optimization scheme based on beam selection and interference cancellation in lens millimeter wave NOMA systems

Non-orthogonal multiple access (NOMA) has been integrated in millimeter-wave (mmWave) Massive MIMO systems to further enhance the spectrum efficiency, but NOMA has not been fully considered in lens mmWave systems. The fusion of these two technologies requires the joint design of beam selection and interference cancellation. In addition, when users follow the spatial cluster distribution, although the user clustering schemes based on K-means algorithm have been applied, the influence of the virtual and actual cluster center users on achievable sum rate has not been differentiated and analyzed in detail. To solve the above problems, a joint optimization scheme is proposed to maximize the achievable sum rate. The optimization problem is NP-hard, which is solved by using the divide-and-conquer approach. Concretely, based on the signal power loss analysis of directional deviation, a beam selection algorithm is designed for inter-cluster interference cancellation based on the K-means algorithm. Further for intra-cluster interference cancellation, a power allocation algorithm based on the bisection method is designed to guarantee the fairness of users in each cluster. The simulation results show that the proposed scheme offers a significant performance improvement in terms of both achievable sum rate and energy efficiency, and it is suitable for the large-scale user scenario due to its low complexity.

Decoupling Energy Efficient Approach for Hybrid Precoding-Based mmWave Massive MIMO-NOMA With SWIPT

In this paper, we address the problem of energy consumption associated with mixed signal components such as analog-to-digital components in millimeter-wave (mmWave) massive MIMO systems. We employ non-orthogonal multiple access (NOMA) in millimeter-wave (mmWave) massive MIMO systems to further enhance the spectrum efficiency. The simultaneous wireless information and power transmission technology (SWIPT) will be used in mmWave massive Multiple-Input multiple-Output MIMO systems. The utilization of SWIPT contributes to prolonging the battery life of mobile users (MUs) and enhances the system energy efficiency (EE), especially in the NOMA scenario where the inter-user interference can be reused for energy harvesting (EH). However, we initially designed a user grouping algorithm based on the affinity propagation clustering algorithm, which preferentially groups the user equipment (UE) based on their channel correlation and distance. Then, we design the analog RF precoder based on the selected user grouping for all beams, followed by a low-dimensional digital baseband precoder design to further mitigate inter-beam interference and maximize the achievable sum-rate for the considered system. Subsequently, we transform the original optimization problem into a joint power allocation and power-splitting maximization problem. The considered non-convex optimization problem is arduous to tackle, resulting from the presence of coupled variables and inter-user interference. To cope with this problem, a decoupled approach is adopted, in which the power allocation and power splitting are separated, and the corresponding sub-problems are solved using the Lagrangian duality method. Simulation results confirm the effectiveness of the proposed method and demonstrate that the proposed method is near-optimal and enjoys higher spectrum and energy efficiency compared with state-of-the-art designs and the conventional SWIPT-enabled mmWave MIMO-NOMA system.

Beam and Channel Tracking for 5G Communication Systems Using Adaptive Filtering Techniques: A Comparison Study

In this paper, we study the problem of beam tracking of a multipath channel in millimeter-wave massive MIMO communication system using adaptive filters. We focus on the performance of least-mean-square filter (LMS) and recursive least-squares filter (RLS) algorithms, compared to a reference extended Kalman filter (EKF), in scenarios where the wireless channel is dominated by a single line of sight (LOS) path or a small number of strong paths. The signal direction and channel coefficients are tracked and updated using these filters. Our results recommend that beamforming systems at millimeter-wave bands should consider variable number of paths rather than a single dominant LOS path. Furthermore, we show that the mean squared-error (MSE) of the innovation process gives a better overall view of the tracking performance than the MSE of the state parameters.

Evaluation of Cell-Free Millimeter-Wave Massive MIMO Systems Based on Site-Specific Ray Tracing Simulations

Ultra densification of the number of antennas combined with the use of large bandwidths in the millimeter wave (mmWave) spectrum is considered one of the main methodologies to achieve the quality of service requirements for future generations of wireless communications. Massive multiple-input multiple-output (mMIMO) cell-free (CF) systems have a large number of access points (APs) distributed in the coverage area, serving simultaneously a smaller number of users consuming the same time-frequency resources. In order to support realistic CF networks designs, this work proposes a performance analysis based on ray tracing simulations. The propagation modeling considers reflection, diffraction, diffuse scattering, atmospheric molecular absorption and foliage and rainfall losses. CF networks with APs equipped with multiple antennas operating in the 26 GHz, 38 GHz and 73 GHz bands are evaluated. From the simulation results, the communication channel is characterized and parameterized. Different performance analysis of CF networks are performed, based on downlink spectral efficiency. In addition, the performance of CF networks in rainy environments is evaluated, and it has been observed that this architecture promotes resistance to the effect of rain attenuation.

A Very-Low Pilot Scheme for mmWave Hybrid Massive MIMO-OFDM Systems

To reduce the large training overhead required by a millimeter-wave (mmWave) massive MIMO system, this letter proposes a pilot placement scheme and the corresponding receiver technique for multi-user uplink channel estimation and data detection over frequency-selective fading channels when OFDM is employed. Specifically, a small group of subcarriers transmit a small segment of pilots followed by data payloads; and all other subcarriers transmit data payloads only. To recover all channels and payload data, the basic idea is that for pilot subcarriers, a semi-blind method is employed for channel estimation and data detection based on low-rank matrix completion; while for the non-pilot subcarriers, the sparsity of mmWave channels in the delay-domain is exploited, based on which the channel estimation can be solved using the compressed-sensing (CS) technique. Simulation results show that significant performance gains can be achieved by our proposed scheme with very low pilot overhead.

Optical True Time Delay-Based Hybrid Beamforming for Limited-Feedback Millimeter-Wave Massive MIMO Systems

Optical true time delay-based (OTTD-based) beamforming can widen the instant bandwidth of phased antenna array systems dramatically. Herein, the low-cost implementation of OTTD-based, sub-connected hybrid beamforming (SC-HBF) is proposed for limited-feedback millimeter-wave massive MIMO systems. In the proposed OTTD-based SC-HBF, weight control modules (WCMs) of analog beamforming are decoupled from all subarrays and centrally deployed. The number of WCMs is less than that of subarrays, and each WCM can be reused. Meanwhile, we develop an optical codebook-based (OC-based) hybrid precoding algorithm, where the use of sub-optimal beam modes is considered. Based on the proposed OC-based hybrid precoding, we analyze the performance of the OTTD-based SC-HBF. The obtained spectral efficiency shows that the OTTD-based SC-HBF achieves near-optimal performance, although the number of WCMs is less than that of subarrays.

Sparse Bayes Tensor and DOA Tracking Inspired Channel Estimation for V2X Millimeter Wave Massive MIMO System

Efficient vehicle-to-everything (V2X) communications improve traffic safety, enable autonomous driving, and help to reduce environmental impacts. To achieve these objectives, accurate channel estimation in highly mobile scenarios becomes necessary. However, in the V2X millimeter-wave massive MIMO system, the high mobility of vehicles leads to the rapid time-varying of the wireless channel and results in the existing static channel estimation algorithms no longer applicable. In this paper, we propose a sparse Bayes tensor and DOA tracking inspired channel estimation for V2X millimeter wave massive MIMO system. Specifically, by exploiting the sparse scattering characteristics of the channel, we transform the channel estimation into a sparse recovery problem. In order to reduce the influence of quantization errors, both the receiving and transmitting angle grids should have super-resolution. We obtain the measurement matrix to increase the resolution of the redundant dictionary. Furthermore, we take the low-rank characteristics of the received signals into consideration rather than singly using the traditional sparse prior. Motivated by the sparse Bayes tensor, a direction of arrival (DOA) tracking method is developed to acquire the DOA at the next moment, which equals the sum of the DOA at the previous moment and the offset. The obtained DOA is expected to provide a significant angle information update for tracking fast time-varying vehicular channels. The proposed approach is evaluated over the different speeds of the vehicle scenarios and compared to the other methods. Simulation results validated the theoretical analysis and demonstrate that the proposed solution outperforms a number of state-of-the-art researches.

Millimeter-wave massive MIMO channel estimation based on majorization–minimization approach

Millimeter-wave massive MIMO systems acquire a high dimensional distorted channel matrix to perform channel estimation. The captured channel matrix is sparse and rank deficient due to low scattering at millimeter-wave bands. The reduced number of RF chains enable the indirect access of signal at each antenna. This makes it difficult to estimate the channel accurately. To address this issue, researchers have focussed on compressive sensing and convex regularization methods. The first one performs well in noiseless scenario and while the latter suffers from over shrinking of all eigenvalues equally and is not suitable for large dimensional matrices. In this paper, a majorization minimization based fast non-convex (MM-FNC) algorithm is proposed to solve non-convex regularization based channel estimation. The proposed algorithm estimates the low-rank channel with fast convergence properties for large dimensional arrays. Simulation results show that the proposed approach exhibits superior performance over the CS-based orthogonal matching pursuit (CS-OMP) method, nuclear norm minimization methods (NNM) and matrix factorization (MF) methods in terms of normalized mean square error and achievable spectral efficiency with respect to different signal-to-noise ratio level.

Performance analysis and power allocation of mixed-ADC multi-cell millimeter-wave massive MIMO systems with antenna selection

Performance analysis and power allocation of mixed-ADC multi-cell millimeter-wave massive MIMO systems with antenna selection

AI based Computation for Hybrid Precoding/Combining in Millimeter-Wave Massive MIMO Systems

Millimeter-wave (mm-Wave) have emerged as a potential leading technology for the 5G cellular systems due to the enormous availability of high radio-frequency spectrum, which can deliver extreme data speed and enhance spectral efficiency (SE). An economical architecture of hybrid precoder (HP) is widely used in mm-Wave massive MIMO systems (mm-WmM) to recompense for the severe propagation loss of the mm-Waves. This paper examines the design of the hybrid precoder and combiner (HPC) in mm-WmM by integrating Artificial Intelligence (AI) based optimization algorithm. AI is going to be a key component to enhance the performance of 5G wireless communications and beyond. The emerging AI based computation using Hierarchical Particle Swarm Optimization technique (HPSO) is proposed to design a HPC to maximize the SE in mm-Wave massive MIMO systems.Results obtained from simulations demonstrate the improved performance of the HPSO algorithm in contrast to the existing algorithms and can accomplish close to the optimal performance.

Hybrid Beamforming With Reduced RF Chain Based on PZF and PD-NOMA in mmWave Massive MIMO Systems

Hybrid beamforming (HB) is a solution for reducing radio frequency (RF) chains in millimeter-wave (mmWave) massive MIMO systems. By reducing RF chains, HB can achieve energy efficient transmission compared to fully digital beamforming of massive MIMO system. However, if the resolution of RF precoder is not quantized, the energy consumption of HB may become similar to fully digital beamforming. And, if the resolution of RF precoder is heavily quantized, the spectral efficiency of HB is decreased significantly. Non-orthogonal multiple access (NOMA) is one of the emerging technologies for the fifth generation (5G) or beyond fifth-generation (B5G) wireless communication to support high spectral efficiency. Especially, power domain NOMA (PD-NOMA) can share same resources to transmit signals and divide the signals in power domain by using successive interference cancellation (SIC). In this paper, the adoption of NOMA shows increase of spectral efficiency in a low-resolution RF precoder. To apply NOMA, two users with high channel correlation are selected according to the proposed criterion. Then, the RF precoder and digital precoder that are applied to both users at the same time are determined. Finally, intra-pair user fairness is achieved by power allocation. The ratio of channel power is compared for each user in each pair. Then, users with high channel power are assigned with low power. It is shown that the spectral efficiency of the proposed scheme is higher than the conventional scheme in heavily quantized RF precoder.