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A collocated phased-subarray multiple-input and multiple-output (MIMO) radar system that reduces the number of MIMO channels to implement a large antenna array is proposed. Both the transmitter (TX) and receiver (RX) arrays are divided into phased subarrays, which consist of several RF beamforming antenna elements. Each phased subarray is exploited as an MIMO channel. To estimate the angular information of targets, a hybrid beamforming technique that incorporates RF beamforming of the phased subarrays and digital beamforming of the MIMO virtual array is used. Antenna configurations of the phased-subarray MIMO radar and the corresponding virtual array are also presented. A space between the TX phased subarrays larger than one TX subarray aperture is introduced to facilitate the larger antenna aperture. Because this involves unwanted overall grating lobes, a grating lobe cancellation method is proposed using sum and difference RF subarray beams. This provides a higher angular resolution with the same number of antenna elements. The proposed phased-subarray MIMO radar concept with the corresponding grating lobe cancellation method is demonstrated by implementing a 60-GHz 16-element phased-subarray 2 ${\times }$ 2 MIMO frequency-modulated continuous-wave (FMCW) radar system. [ABSTRACT FROM AUTHOR]

In this treatise, we propose to design a transmit precoder at the base station (BS) for a hybrid fully connected sub-array (FCSA) architecture based on gradient projection (GP) and alternating minimization techniques. A random complex multiplicative factor comprehensively models the non-ideal nature of the power amplifier (PA), data converters (ADCs/DACs), and phase shifters (PSs) used in the transmitter radio frequency (RF) chain at the BS with the ideal mmWave channel. An effective noise term in the received signal with impairments is introduced, and the power of the same is derived. The effect of scattering of mmWave due to rainfall is taken into consideration. Two separate cases with the above-discussed impairments of (A) single user (SU) multiple-input multiple-output (MIMO) system and (B) multi-user MIMO (MU-MIMO) system are conceived. Both perfect and imperfect knowledge of the scattering parameters is considered. A linear minimum mean square error (LMMSE) channel estimation with the above impairments at the BS in the presence of rainfall scattering is performed. For both SU-MIMO and MU-MIMO systems, the spectral efficiency (SE) is evaluated for different rainfall rates at two carrier frequencies, viz. 38 GHz and 75 GHz. The channel estimate’s mean square error (MSE) for ideal and hardware impaired systems is evaluated at various rainfall rates. The performance of channel estimation is evaluated with numerical simulation. [ABSTRACT FROM AUTHOR]

One novel design considering two practical application constraints based on the interleaved subarray architecture is proposed to achieve multi-beam multiplexing for arbitrary directions to serve corresponding users. One is an equal magnitude constraint imposed on the analogue coefficients so that beamforming can be achieved by pure phase shifters after the normalisation of magnitudes; the other one is a robust constraint against steering vector errors. Designed examples are provided to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this letter, we consider hybrid beamforming for millimeter wave (mmWave) MIMO integrated sensing and communications (ISAC). We design the transmit beam of a dual-functional radar-communication (DFRC) base station (BS), aiming at approaching the objective radar beam pattern, subject to the constraints of the signal to interference-plus-noise ratio (SINR) of communication users and total transmission power of the DFRC BS. To provide additional degree of freedom for the beam design problem, we introduce a phase vector to the objective beam pattern and propose an alternating minimization method to iteratively optimize the transmit beam and the phase vector, which involves second-order cone programming and constrained least squared estimation, respectively. Then based on the designed transmit beam, we determine the analog beamformer and digital beamformer subject to the constant envelop constraint of phase shifter network in mmWave MIMO, still using the alternating minimization method. Simulation results show that under the same SINR constraint of communication users, larger antenna array can achieve better radar beam quality. [ABSTRACT FROM AUTHOR]

Cell-free massive MIMO (CF-mMIMO) systems represent a promising approach to increase the spectral efficiency of wireless communication systems. However, near-optimal beamforming solutions require a large amount of signaling exchange between access points (APs) and the network controller (NC). In this letter, we propose two unsupervised deep neural networks (DNN) architectures, fully and partially distributed, that can perform decentralized coordinated beamforming with zero or limited communication overhead between APs and NC, for both fully digital and hybrid precoding. The proposed DNNs achieve near-optimal sum-rate while also reducing complexity by $10-24\times $ compared to conventional near-optimal solutions. [ABSTRACT FROM AUTHOR]

This paper considers a reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) downlink communication system where hybrid analog-digital beamforming is employed at the base station (BS). We formulate a power minimization problem by jointly optimizing hybrid beamforming at the BS and the response matrix at the RIS, under the signal-to-interference-plus-noise ratio (SINR) constraints at all users. The problem is highly challenging to solve due to the non-convex SINR constraints as well as the unit-modulus phase shift constraints for both the RIS reflection coefficients and the analog beamformer. A two-layer penalty-based algorithm is proposed to decouple variables in SINR constraints, and manifold optimization is adopted to handle the non-convex unit-modulus constraints. We also propose a low-complexity sequential optimization method, which optimizes the RIS reflection coefficients, the analog beamformer, and the digital beamformer sequentially without iteration. Furthermore, the relationship between the power minimization problem and the max-min fairness (MMF) problem is discussed. Simulation results show that the proposed penalty-based algorithm outperforms the state-of-the-art semidefinite relaxation (SDR)-based algorithm. Results also demonstrate that the RIS plays an important role in the power reduction. [ABSTRACT FROM AUTHOR]

We consider the downlink (DL) of a multi-user hybrid beamforming system having receiver IQ imbalance (Rx-IQI). For a generic channel model and any IQI unaware hybrid precoder, we derive a generalized expression of the DL signal-to-interference-and-noise-ratio (SINR) using which we show that the corresponding DL sum-rate is limited by the Rx-IQI parameters. Therefore, we propose a novel Rx-IQI pre-compensation algorithm and we derive a generalized DL SINR expression for the Rx-IQI pre-compensation scenario. Using the derived SINR expressions, we provide closed-form sum-rate expressions of the zero-forcing hybrid precoder for uncorrelated Rayleigh fading and sparse mm-wave channels. Through analysis and numerical simulations, we show that the proposed algorithm almost achieves the performance of an IQI-free system. [ABSTRACT FROM AUTHOR]

In this paper, a 36×36 design of a thinned phased array antenna with subarrays is proposed. Binary-genetic learning particle swarm optimization (GLPSO) was used to design a thinned phased array antenna with a thinning factor of 60 % to 65 %, and continuous-GLPSO was used to apply 36 subarray shapes. The RF beam steering range was an azimuth/elevation of 40°, the sidelobe levels (SLLs) were 26.67 dB and 28.43 dB, and the half-power beamwidths (HPBWs) were 4.6° and 4.7°. When the subarrays were applied, the digital beam steering range was HPBW/2° in the azimuth/elevation direction. SLLs were 22.52 dB and 22.53 dB respectively, and the HPBWs were all 3.5°. When RF beam and digital beam steering are simultaneously performed in the azimuth/elevation direction by 40°+HPBW/2°, the SLLs are 20.16 dB and 21.25 dB respectively, and the HPBWs are all 5°. [ABSTRACT FROM AUTHOR]

Frequencies above 100 GHz are the promising frequency bands for 6G wireless communication systems because of the abundant unexplored and unused spectrum. The increasing global demand for ultra-high spectral efficiencies, data rates, speeds and bandwidths in next-generation wireless networks motivates the exploration of peak capabilities of massive MIMO (Multi–Input–Multi–Output) wireless access technology at THz bands (0.1–10 THz). The smaller wavelengths (order of microns) of these frequencies give an advantage of making high gain antennas with smaller physical dimensions and allows massive spatial multiplexing. This paper presents the design of ultra-massive MIMO (ultra-mMIMO) hybrid beamforming system for multi users and its feasibility to function at THz frequency bands. The functionality of the proposed system is verified at higher order modulation schemes to achieve higher spectral efficiencies using performance metrics that includes error vector magnitude, symbol constellations, and antenna array radiation beams. The performance results suggest to use a particular mMIMO antenna configuration based on number of independent data streams per user and strongly recommended to use higher number of data streams per user in order to achieve higher throughputs that satisfy the needs of 6G wireless systems. Also the performance of the proposed system at 0.14 THz is compared with mmWave systems that operate at 28 GHz and 73 GHz bands to justify the feasibility of the proposed work. [ABSTRACT FROM AUTHOR]

Achieving high channel estimation accuracy and reducing hardware cost as well as power dissipation constitute substantial challenges in the design of massive multiple-input multiple-output (MIMO) systems. To resolve these difficulties, sophisticated pilot designs have been conceived for the family of energy-efficient hybrid analog-digital (HAD) beamforming architecture relying on adaptive-resolution analog-to-digital converters (RADCs). In this paper, we jointly optimize the pilot sequences, the number of RADC quantization bits and the hybrid receiver combiner in the uplink of multiuser massive MIMO systems. We solve the associated mean square error (MSE) minimization problem of channel estimation in the context of correlated Rayleigh fading channels subject to practical constraints. The associated mixed-integer problem is quite challenging due to the nonconvex nature of the objective function and of the constraints. By relying on advanced fractional programming (FP) techniques, we first recast the original problem into a more tractable yet equivalent form, which allows the decoupling of the fractional objective function. We then conceive a pair of novel algorithms for solving the resultant problems for codebook-based and codebook-free pilot schemes, respectively. To reduce the design complexity, we also propose a simplified algorithm for the codebook-based pilot scheme. Our simulation results confirm the superiority of the proposed algorithms over the relevant state-of-the-art benchmark schemes. [ABSTRACT FROM AUTHOR]

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In this letter, we provide a practical framework to resolve whether code-domain NOMA (CD-NOMA) is beneficial when integrated with massive MIMO systems. In order to realize this integration, first, we develop a novel code-beamspace wideband signal model for uplink CD-NOMA in mmWave hybrid massive MIMO systems employing single-carrier (SC) transmission. Then, we apply a state-of-the-art SC frequency domain equalization (SC-FDE) based iterative receiver where the number of radio frequency (RF) chains is limited. Simulation results verify the effectiveness of the proposed architecture in overloaded scenarios. Furthermore, we show that CD-NOMA can enhance the performance of mmWave hybrid beamforming based massive MIMO systems by effectively decreasing the correlation between closely separated user channels in joint code-beamspace. [ABSTRACT FROM AUTHOR]

The emergence of highly directional beamforming technology makes millimeter wave frequency band communication possible in future wireless communication networks. Based on the multipath characteristics of millimeter wave frequency communication, a high-precision multipath channel estimation algorithm based on signal subspace is proposed. In the mobile terminal, an iterative heuristic radio frequency combination algorithm based on spatial points is proposed. The analog precoding at the base station uses deep learning to accelerate the calculation, and then the multi-user communication is modeled to design the digital precoding. The simulation results show that the multi-channel estimation algorithm can estimate 4 paths with an error of no more than 0.3 rad. The proposed DL algorithm takes only 20% of the time when it is close to the 87% spectral efficiency of the traditional algorithm.

A feeding network crossing method is proposed to form an N $\times $ N dual-polarized two-dimensional multibeam antenna array (2D-MBAA) with analog-digital hybrid beamforming, which solves the problem of needing additional connection structure between the dual-polarized antenna array and analog feeding network for large-scale antenna arrays. The main design work is focused on the analog-beamformer (ABF) and two structures are proposed. One is vertical-crossed (VC) ABF and the other is parallel-crossed (PC) ABF, and the latter is a more complex design in exchange for a significantly reduced profile. As a verification, a $4\times 4$ dual-polarized 2D-MBAA with PC-ABF has been fabricated and tested. It is operated at 3.45GHz with a bandwidth of about 10%, and it can realize a scanning angle of ±45°.

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.

This paper brings the concept of wireless edge caching (WEC) to the realm of integrated access and backhaul (IAB) networks. Traditionally, both access and backhaul are allocated fixed spectrum. However, when WEC is applied to the IAB nodes to pre-fetch some popular files, the backhaul traffic can be drastically reduced. Accordingly, an analytical framework is developed for a cache-enabled IAB network for the wide-band frequency range 2 (FR2) (> 24.25 GHz) channel model. In particular, the optimal spectrum resource allocation policy between access and backhaul for IAB networks is investigated by utilizing WEC. For the considered WEC enabled IAB network, a mathematical framework is presented under the content delivery phase for the calculation of three key performance metrics, namely the average success probability (ASP), throughput, and latency of file delivery with respect to various network parameters such as different caching placement strategies, node density, cache size, antenna number, blockage density, and resource partitioning factor. Extensive numerical results are provided to both verify the theoretical derivations and to demonstrate the precedency of using WEC as a cost-effective measure for the improvement of the IAB network’s performance. In particular, under a normalized cache size of 0.6, compared to the baseline no caching scenario, the percentages of backhaul spectrum that can be saved are 48% (26%), 66% (48%), and 47% (27%) when caching the most popular files (caching uniformly) with respect to the ASP, latency, and throughput of file delivery, respectively. The saved backhaul spectrum can then be shifted to the access for its performance enhancements. Our findings also show that there exists an optimal spectrum partition for IAB with respect to different network parameter settings. Further, there exists a tradeoff in the selection of the optimal partitioning factor with respect to ASP/ throughput and latency of file delivery for varying IAB node densities.

In the context of a massive MIMO system, the number of RF chains increases prohibitively. To solve the problem, hybrid beamforming (BF) has been proposed. Nevertheless, the amplitude constraint of the analog part in hybrid BF limits achieving the optimal performance given by the optimal digital BF. Some studies have been conducted recently to relax the amplitude constraint by deploying multiple phase shifters and switches (that select phase shifters) in the hybrid beamforming structures. The tremendous number of switches in the structure makes the problem of switch state selection very complex. In this paper, we propose an efficient low-complexity algorithm to obtain the optimal switch states. In the proposed algorithm, we decompose the main problem into sub-ones to reduce the complexity and solve the problem using a finite search space. The results of our study suggest that the optimal solution will be attainable by searching through less than 1% of the search space, if the number of phase levels exceeds 14. Moreover, the proposed algorithm has the potential to perfectly implement linear beamformers, designed for fully digital architectures, in a hybrid structure, while applying the minimum number of RF chains. Finally, the simulation results prove the efficiency of the algorithm and confirm the sufficiency of the analog part of the BF to achieve the optimal digital BF performance just by applying a few number of fixed phase shifters.

This paper proposes a new digital predistortion (DPD) scheme for linearizing millimeter-wave hybrid beamforming transmitters using observation receivers with low-bit resolution analog-to-digital converters (ADCs). To train the DPD function required to compensate for the distortions exhibited by a given sub-array (also called main sub-array), an error signal is produced by out-of-phase combining the main sub-array transmitted signal and the one generated by another sub-array (also called auxiliary sub-array) using anti-beamforming modules. The error signal is then frequency down-converted and digitized using a low-bit resolution ADC. Proof-of-concept validation experiments are conducted by applying the proposed DPD system to linearize an off-the-shelf hybrid-beamforming array comprised of four 64-element sub-arrays, operating at 28 GHz and driven with up to 800 MHz orthogonal frequency-division multiplexing modulated signals. Using the proposed DPD scheme, an observation receiver with a 4-bit ADC was sufficient to improve the adjacent channel power ratio by 10 dB and the error vector magnitude was reduced from 5.8% to 1.6%. These results are similar to those obtained using an observation receiver with 16-bit ADC.

In this paper, we consider the decode-and-forward (DF) relay system in millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems, and propose a hybrid beamforming design method for the mixed structure, which contains the fully-connected and sub-connected structures. To satisfy constant-modulus and block-diagonalization (BD) constraints, the analog beamforming is designed by the idea of sorted successive interference cancellation (SSIC). More specifically, the proposed method first sorts the capacities of different analog sub-channels in descending order, and then designs the analog beamforming serially according to the order of the capacities. To efficiently mitigate the inner-user and inter-user interference, we propose a modified baseband BD technology to reduce the information loss in digital beamforming design, thereby improving the system capacity. In addition, the proposed hybrid beamforming algorithm is designed by considering both uniform linear arrays (ULAs) and uniform planar arrays (UPAs). Simulation results demonstrate that the proposed hybrid beamforming design scheme can obtain good performance in terms of the achievable sum-rate and power efficiency in ULAs and UPAs.

In traditional multiple-input multiple-output (MIMO) receivers, radio frequency (RF) front-ends are exposed to interference as no analog spatial filtering is employed before the digital beamforming stage. Therefore the RF front-end is power-hungry, and analog to digital converters require a high dynamic range. In this paper, we consider an analog beamforming system in case of narrowband signals to cancel interference early in the analog domain, thus reducing the required ADC resolution. In contrast to existing analog beamformers with only phase shifts, our proposed design employs vector modulators where the coefficients can be selected from a set of weights with variable phases and amplitudes. We also propose an efficient and fast Euclidean distance algorithm to determine the analog beamformer coefficients while being suitable for realistic scenarios. Finally, an expression is introduced to estimate the interference rejection achieved by employing the proposed algorithm and a vector modulator in the RF domain. The introduced algorithm leads to considerable improvement in computational complexity by slightly sacrificing interference rejection.

Low-complexity beamformer design with practical constraints is an attractive research area for hybrid analog/digital systems in mm-wave massive multiple-input multiple-output (MIMO). This paper investigates interference-aware pre-beamformer (analog beamformer) design for joint spatial division and multiplexing (JSDM) which is a user-grouping based two-stage beamforming method. Single-carrier frequency domain equalization (SC-FDE) is employed in uplink frequency-selective channels. First, unconstrained slowly changing statistical analog beamformer of each group, namely, generalized eigenbeamformer (GEB) which has strong interference suppression capability is designed by maximizing the mutual information in reduced dimension. Then, constant-modulus constrained approximations of unconstrained beamformer are obtained by utilizing alternating minimization algorithms for fully connected arrays and fixed subarrays. In addition, a dynamic subarray algorithm is proposed where the connections between radio frequency (RF) chains and antennas are changed with changing channel statistics. Convergence of the proposed alternating minimization-based algorithms is provided along with their complexity analysis. It is observed that the additional complexity of proposed algorithms is insignificant for the overall system design. Although most of the interference is suppressed with the help of proposed constrained beamformers, there may be some residual interference after analog beamforming stage. Thus, minimum mean square error (MMSE) criterion based iterative block decision feedback equalization (IB-DFE) method, which takes the residual interference in reduced dimension into account, is promoted for digital beamforming stage. Simulation results verify the superiority of the proposed interference-aware constrained design over existing approaches in terms of beampattern, spectral efficiency, outage capacity, bit-error rate (BER), and channel estimation accuracy.

Hardware complexity reduction is a key concept towards the design and implementation of next generation broadband wireless networks. To this end, the goal of the study presented in this paper is to evaluate the performance of an adaptive hybrid analog-digital beamforming approach in fifth-generation (5G) massive multiple input multiple output (MIMO) millimeter wave (mmWave) wireless cellular orientations. In this context, generated beams are formed dynamically according to traffic demands, via an on-off analog activation of radiating elements per vertical antenna array, in order to serve active users requesting high data rate services without requiring any expensive and mechanical complex steering antenna system. Each vertical array, which constitutes a radiating element of a circular array configuration, has a dedicated radio frequency chain (digital part). The performance of our proposed approach is evaluated statistically, by executing a sufficient number of independent Monte Carlo simulations per MIMO configuration, via a developed system-level simulator incorporating the latest 5G-3GPP channel model. According to the presented results, the adaptive beamforming approach can improve various key performance indicators (KPIs) of the wireless orientation, such as total downlink transmission power and blocking probability. In particular, when studying/analyzing a MIMO configuration with 15 vertical antenna arrays and 10 radiating elements per array, then, depending on the tolerable amount of transmission overhead, the proposed adaptive algorithm can significantly reduce the number of active radiating antenna elements compared to the static grid of beams case. In the same context, when keeping the number of radiating elements constant, then the total downlink transmission power as well as the blocking probability can be significantly reduced. It is important to note that all the KPIs have been extracted when deploying the developed array configuration in complex cellular orientations (two tiers of cells around the central cell).

In this paper, we propose a new uplink hybrid beamforming architecture for multi-user multiple-input–multiple-output (MU-MIMO) millimeter-wave (mmWave) systems. The proposed architecture employs constant phase shifters (CPSs) to control the phases of the signals in conjunction with double switches for signal selection in the radio frequency (RF) beamforming part. By using double switches to approximate the optimal double variable phase shifter (VPS)-based scheme, the proposed scheme can achieve near-optimal sum rates when the number of CPSs is large enough. In addition, with the support of the active/inactive switches, it selectively combines the antenna signals, which can further improve the sum rate while reducing power consumption. The simulation results show that the proposed scheme outperforms conventional VPS-based and fully digital combiners in terms of energy efficiency.

Hybrid beamforming is used to leverage the benefits of both massive multiple input and multiple output (MIMO) systems and millimeter waves for significantly increasing the capacity of wireless networks. Existing schemes for hybrid beamforming in multiple radio frequency (RF) chains optimize a global measure of performance and ignore quality of service (QoS) per data stream. In this paper, we propose a novel scheme for hybrid beamforming to minimize the transmit power while satisfying QoS defined as the mean square error (MSE) per data stream. We propose a two-stage cascade structure for the baseband precoder and combiner, and obtain their respective matrices in both single- and multi-user systems. We also propose a simplified scheme for designing hybrid beamformers with no nested loops in the alternating optimization method. Simulations show less than 2 dBW optimality gap for the multi-user scenario, with significantly less transmit power as compared to other existing schemes. Our simplified scheme consumes a negligible amount of 0.1 dBW more transmit power than our scheme with nested loops.

Hybrid precoding has a great potential to achieve near-optimal performance with few radio-frequency (RF) chains in comparison to the number of antennas. This paper proposes a novel hybrid beamforming solution for sum rate maximization in a wideband multi-user multi-input multi-output (MU-MIMO) relay system. The problem at hand is non-convex and hence mathematically intractable due to the constant modulus constraints associated with the analog beamforming design at communicating nodes shared by all sub-carriers. Furthermore, relaying in MU-MIMO systems under frequency-selective channels is also a challenging task as it requires complicated signal processing. To address this problem, source analog beamformer, relay RF combiner and phase-only processing component at each destination are obtained by deriving the common RF processing matrix which enables beamforming gain over all sub-carriers. This process involves reconstruction loss minimization for designing the required analog beamforming vectors having ability to maximize end-to-end signal-to-interference plus noise ratio (SINR). The relay analog precoder is designed by maximizing the received power at each destination through gain maximization of the equivalent baseband channel over the allocated frequency spectrum. To reduce the computational complexity of the iterative approach used to find the relay RF precoding matrix, the closed form expression is derived for achieving the desired objective. Finally, digital baseband processing components are obtained from the equivalent baseband channels by suppressing the interference among transmitted signals indirectly. Simulation results for different system configurations reveal that the proposed algorithm can achieve near-optimal performance.

Terahertz (THz) communications with multi-GHz bandwidth are envisioned as a key technology for 6G systems. Ultra-massive (UM) MIMO with hybrid beamforming architectures are widely investigated to provide a high array gain to overcome the huge propagation loss at THz band. However, most of the existing hybrid beamforming architectures can only utilize the multiplexing offered by the multipath components, i.e., inter-path multiplexing, which is very limited due to the spatially sparse THz channel. In this paper, a widely-spaced multi-subarray (WSMS) hybrid beamforming architecture is proposed, which improves the multiplexing gain by exploiting a new type of intra-path multiplexing provided by the spherical-wave propagation among $k$ widely-spaced subarrays, in addition to the inter-path multiplexing. The resulting multiplexing gain of WSMS architecture is $k$ times of the existing architectures. For WSMS architecture, a novel design problem is formulated by optimizing the number of subarrays, subarray spacing, and hybrid beamforming matrices to maximize the spectral efficiency, which is decomposed into two subproblems. An optimal closed-form solution is derived for the first hybrid beamforming subproblem, while a dominant-line-of-sight-relaxation algorithm is proposed for the second array configuration subproblem. Extensive simulation results demonstrate that the WSMS architecture and proposed algorithms enhance the spectral efficiency substantially. [ABSTRACT FROM AUTHOR]

Massive multiple-input multiple-output (MIMO) systems are considered a promising solution to minimize multiuser interference (MUI) based on simple precoding techniques with a massive antenna array at a base station (BS). This paper presents a novel approach of beam division multiple access (BDMA) which BS transmit signals to multiusers at the same time via different beams based on hybrid beamforming and user-beam schedule. With the selection of users whose steering vectors are orthogonal to each other, interference between users is significantly improved. While, the efficiency spectrum of proposed scheme reaches to the performance of fully digital solutions, the multiuser interference is considerably reduced. [ABSTRACT FROM AUTHOR]

This paper studies the cross-layer challenges and performance of Hybrid Beamforming (HBF) and Multi-User Multiple-Input Multiple-Output (MU-MIMO) in 5G millimeter wave (mmWave) cellular networks with full-stack TCP/IP traffic and MAC scheduling. While previous research on HBF and MU-MIMO has focused on link-level analysis of full-buffer transmissions, this work reveals the interplay between HBF techniques and the higher layers of the protocol stack. To this aim, prior work on the full-stack evaluation of mmWave cellular networks has been extended by including the modeling of MU-MIMO and HBF. Our results reveal novel relations between the networking layers and the HBF MU-MIMO performance at the physical layer. Particularly, throughput can be increased in 5G networks by means of Space Division Multiple Access (SDMA). However, in order to achieve such benefits it is necessary to take into account certain trade-offs and the implementation complexity of a full-stack HBF solution. [ABSTRACT FROM AUTHOR]

Future 5G/6G wireless networks will be increasingly using millimeter waves (mmWaves), where fast and efficient beamforming is vital for providing continuous service to highly mobile devices in the presence of interference and signal attenuation, manifested by blockage. In this paper, we propose a novel and efficient method for mmWave beamforming in massive multiple-input multiple-output (MIMO) systems to achieve the aforementioned goals with low complexity in such scenarios. In doing so, we utilize deep reinforcement learning (DRL) to maximize the network’s energy efficiency subject to the quality of service (QoS) constraint for each user equipment (UE) and obtain its hybrid beamforming matrices. In doing so, we assume each UE is simultaneously associated with multiple access points (APs), i.e., simultaneous beamforming to/from multiple APs (coordinated multipoints) is needed for each UE. We also propose a low-complexity training algorithm, based on approximate message passing, which is well suited for the network edge. Besides, we develop a distributed scheme to reduce communications overhead via federated DRL. Extensive simulations show significant performance improvement over existing methods. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) can provide computing and storage services to user equipments (UEs) by utilizing edge nodes known as the small base stations (SBS’s) deployed at the edge of the network. The short-distance transmission nature between SBS’s and UEs makes the millimeter-wave (mmWave) communication empowered with multiple-input multiple-output (MIMO) hybrid precoding techniques particularly attractive for MEC. In this work, we consider the UE-SBS association, precoding design and power allocation for MEC networks endowed with mmWave MIMO. More specifically, the user association problem is first formulated as a max-k-cut (M $k$ C) problem and then, solved by a distributed local-search algorithm. Next, the joint optimization of precoding and power allocation is cast into the difference of two convex functions (D.C.) programming framework before an iterative rank-constrained D.C. programming algorithm is developed to maximize the weighted sum-rate (WSR) of all UEs while taking into account the quality of service (QoS) requirement of each UE. Furthermore, the monotonic convergence of the proposed iterative algorithm is analytically proven. Finally, extensive computer simulation is conducted to demonstrate the effectiveness of the proposed iterative algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) non-orthogonal multiple access (NOMA) system is analyzed. In particular, we consider an RIS-aided mmWave-NOMA downlink system with a hybrid beamforming structure. To maximize the achievable sum-rate under a minimum rate constraint for the users and a maximum transmit power constraint, a joint RIS phase shifts, hybrid beamforming, and power allocation problem is formulated. To solve this non-convex optimization problem, we develop an alternating optimization (AO) algorithm. Specifically, first, the non-convex problem is transformed into three subproblems, i.e., power allocation, joint phase shifts and analog beamforming optimization, and digital beamforming design. Then, we solve the power allocation problem by keeping fixed the phase shifts of the RIS and the hybrid beamforming. Finally, given the power allocation matrix, an alternating manifold optimization (AMO)-based method and a successive convex approximation (SCA)-based method are utilized to design the phase shifts, analog beamforming, and transmit beamforming, respectively. Numerical results reveal that the proposed AO algorithm outperforms existing schemes in terms of sum-rate. Moreover, compared to a conventional mmWave-NOMA system without RIS, the proposed RIS-aided mmWave-NOMA system is capable of improving the achievable sum-rate. [ABSTRACT FROM AUTHOR]

Hybrid beamforming (HBF) is an attractive technology for practical massive multiple-input and multiple-output (MIMO) millimeter wave (mmWave) systems. Compared with the fully-connected HBF architecture, the partially-connected one can further reduce the hardware cost and power consumption. However, the special block diagonal structure of its analog beamforming matrix brings additional design challenges. In this paper, we develop effective HBF algorithms for spectral efficiency maximization (SEM) in wideband mmWave massive MIMO systems with the partially-connected architecture. One main contribution is that we prove the equivalence of the SEM problem and a weighted mean square error minimization (WMMSE) problem, which leads to a convenient algorithmic approach to directly tackle the SEM problem. Specifically, we decompose the equivalent WMMSE problem into the hybrid precoding and hybrid combining subproblems, for which both the optimal digital precoder and combiner have closed-form solutions. For the more challenging analog precoder and combiner, we propose an element iteration based algorithm and a manifold optimization based algorithm. Finally, the hybrid precoder and combiner are alternatively updated. The overall HBF algorithms are proved to monotonously increase the spectral efficiency and converge. Furthermore, we also propose modified algorithms with reduced computational complexity and finite-resolution phase shifters. Simulation results demonstrate that the proposed HBF algorithms achieve significant performance gains over conventional algorithms. [ABSTRACT FROM AUTHOR]

Full-duplex millimeter wave (mmWave) communication has shown increasing promise for self-interference cancellation via hybrid precoding and combining. This paper proposes a novel mmWave multiple-input multiple-output (MIMO) design for configuring the analog and digital beamformers of a full-duplex transceiver. This work is the first to holistically consider the key practical constraints of analog beamforming codebooks, a minimal number of radio frequency (RF) chains, limited channel knowledge, beam alignment, and a limited receive dynamic range. To prevent self-interference from saturating receive components, such as LNAs and ADCs, a design framework is developed that limits the degree of self-interference on a per-antenna and per-RF chain basis. We present a means for constructing analog beamforming candidates from beam alignment measurements to afford our design greater flexibility in its aim to reduce self-interference. Numerical results evaluate the design in a variety of settings and validate the need to prevent receiver-side saturation. These results and corresponding insights serve as useful design references and benchmarks for practical full-duplex mmWave transceivers. [ABSTRACT FROM AUTHOR]

We propose a new method to tackle the predistortion problem for the hybrid beamforming structure of Millimeter-Wave (MM-Wave) Massive MIMO in 5G communication systems in order to widen the linearized angle of beams without any requirement or retuning on the nonlinearities of power amplifiers (PA). The new method adopts one common Digital Predistortion (DPD) block per subarray followed by a post-weighting block, and the predistorted signals are re-distributed to different subarrays to form a pattern to compensate for the distortions in different directions simultaneously. Two implementations with low complexity are introduced in this letter. [ABSTRACT FROM AUTHOR]

This paper proposes two algorithms for hybrid (Analog–Digital) beamforming in a single-user millimeter-wave (mm-wave) multi-input multi-output (MIMO) systems under frequency selective channels. Hybrid architecture has a potential to reduce the number of radio frequency (RF) chains to far less than the number of antennas and, it results in low hardware complexity, power efficiency and cost-saving to the system. Since practical broadband systems are frequency selective in nature therefore, the challenging aspect of this problem is to have only one analog RF processing matrix at source and destination over the entire frequency band. To address this problem for spectral efficiency maximization, the first algorithm performs multi-linear singular value decomposition on different sub-carriers to derive the common analog combiner over the allocated frequency-spectrum. Then, a problem is formulated for designing the common analog precoder that attempts to extract the optimal phase values for gain maximization of the equivalent channel observed from baseband processing units at transmitter and receiver. To significantly reduce the computational complexity in comparison to the first algorithm, the second algorithm employs classification of beamforming vectors for gain maximization of the equivalent baseband channel over all sub-carriers to find the analog RF precoding/combining matrices. Digital baseband processing components are derived by diagonalizing the equivalent baseband channel corresponding to each frequency-carrier. Computational complexity of the second algorithm is significantly less than the several existing designs. Computer simulations reveal that the proposed algorithms show near-optimal performance under diverse scenarios of system configuration parameters. [ABSTRACT FROM AUTHOR]

With the advent of massive MIMO and mmWave, Antenna selection is the new frontier in hybrid beamforming employed in 5G base stations. Tele-operators are reworking on the components while upgrading to 5G where the antenna is a last-mile device. The burden on the physical layer not only demands smart and adaptive antennas but also an intelligent antenna selection mechanism to reduce power consumption and improve system capacity while degrading the hardware cost and complexity. This work focuses on reducing the power consumption and finding the optimal number of RF chains for a given millimeter wave massive MIMO system. At first, we investigate the power scaling method for both perfect Channel State Information (CSI) and imperfect CSI where the power is reduced by ��/���� and ��/√���� respectively. We further propose to reduce the power consumption by emphasizing on the subdued resolution of Analog-to-Digital Converters (ADCs) with quantization awareness. The proposed algorithm selects the optimal number of antenna elements based on the resolution of ADCs without compromising on the quality of reception. The performance of the proposed algorithm shows significant improvement when compared with conventional and random antenna selection methods.

Millimeter-wave (mm-wave) communication is a key technology for future wireless networks. To combat significant path loss and exploit the abundant mm-wave spectrum, effective beamforming is crucial. Nevertheless, conventional fully digital beamforming techniques are inapplicable, as they demand a separate radio frequency (RF) chain for each antenna element, which is costly and consumes too much energy. Hybrid beamforming is a cost-effective alternative, which can significantly reduce the hardware cost and power consumption by employing a small number of RF chains. This paper presents a holistic view on hybrid beamforming for 5G and beyond mm-wave systems, based on a new taxonomy for different hardware structures. We take a pragmatic approach and compare different proposals from three key aspects: 1) hardware efficiency, i.e., the required hardware components; 2) computational efficiency of the associated beamforming algorithm; and 3) achievable spectral efficiency, a main performance indicator. Through systematic comparisons, the interplay and trade-off among these three design aspects are demonstrated, and promising candidates for hybrid beamforming in future wireless networks are identified.

In this article, we propose an orthogonal frequency-division multiplexing system supported by the compressed sensing assisted index modulation, termed as (OFDM-CSIM), applied to millimeter-wave (mmWave) communications. In the OFDM-CSIM mmWave system, information is conveyed not only by the classic constellation symbols but also by the on/off status of subcarriers, where the size of constellation symbols and the number of active subcarriers can be beneficially configured for maximizing the system's throughput. We conceive a machine learning (ML) assisted adaptive OFDM-CSIM mmWave system, which simultaneously benefits from the OFDM with index modulation (IM), compressed sensing (CS) and the hybrid beamforming techniques. Specifically, a ML-assisted link adaptation scheme is designed based on the k -nearest neighbors (k -NN) algorithm with the objective to maximize the system's throughput. Our studies show that the proposed ML-assisted link adaptation is capable of providing higher throughput than the conventional threshold-based link adaptation when different antenna structures are considered. Furthermore, the achievable data rates of four types of antenna arrays, including uniform linear array (ULA), uniform rectangular planar array (URPA), uniform circle planar array (UCPA) and uniform cylindrical array (UCYA), are investigated and compared over mmWave channels. The simulation results show that the UCYA achieves the highest data rate among these antenna arrays.

In this article, we propose a hybrid beamforming design for multiuser mmWave massive MIMO systems. We adopt a two-stage approach for designing the analog and digital beamforming separately. The analog beamforming design is based on a constrained low-rank channel decomposition and aims to simultaneously harvest the array gain and reduce the intra- and inter-user interference. The digital beamforming design is conducted by using the regularized channel diagonalization method, which provides a better trade-off between multiuser interference suppression and transmit diversity, thus attaining a better performance in low-SNR scenarios or when communicating to many users or through many data streams. We validate the effectiveness of the proposed design through numerical simulations, which have shown that our design outperforms several other hybrid beamforming designs in the literature.

The conventional fully-digital implementation of massive-MIMO systems is not efficient due to the large required number of radio-frequency (RF) chains. To address this issue, hybrid analog/digital (A/D) beamforming was proposed and to date remains a topic of ongoing research. In this paper, we explore the hybrid A/D structure as a general framework for signal processing in massive and ultra-massive-MIMO systems. To exploit the full potential of the analog domain, we first focus on the analog signal processing (ASP) network. We investigate a mathematical representation suitable for any arbitrarily connected feed-forward ASP network comprised of the common RF hardware elements in the context of hybrid A/D systems, i.e., phase-shifter and power-divider/combiner. A novel ASP structure is then proposed which is not bound to the unit modulus constraint, thereby facilitating the hybrid A/D systems design. We then study MIMO transmitter and receiver designs to exploit the full potential of digital processing as well. It is shown that replacing the linear transformation in the digital domain with a generic mapping can improve the system performance. In some cases, the performance of optimal fully-digital MIMO systems can be achieved without extra calculations compared to sub-optimal hybrid A/D techniques. An optimization model based on the proposed structure is presented that can be used for hybrid A/D system design. Specifically, precoding and combining designs under different conditions are discussed as examples. Finally, simulation results are presented which illustrate the superiority of the proposed architecture to the conventional hybrid designs for massive-MIMO systems.

In this paper, we propose practical yet effective statistically-aided codebook-based hybrid precoding schemes for massive multiple-input multiple-output systems in millimeter wave bands. Particularly, we develop novel low-overhead hybrid precoding algorithms for selecting the baseband digital and radio frequency analog precoders from statistically skewed DFT-based codebooks. The proposed algorithms aim at maximizing the spectral efficiency based on minimizing the chordal distance between the optimal unconstrained precoder and the hybrid beamformer and maximizing the signal to the interference noise ratio for the single-user and multi-user cases, respectively. We investigate the performance of the proposed algorithms by considering the mutual information of the analog beamforming procedure (the common stage among the proposed algorithms) as a performance evaluation metric. We derive lower and upper bounds on the mutual information of the channel given the proposed algorithms. Moreover, we show that the performance gap between the lower and upper bounds depends heavily on how many DFT columns are aligned to the largest eigenvectors of the transmit antenna array response of the millimeter wave channel or equivalently the transmit channel covariance matrix when only statistical channel knowledge is available at the transmitter. Then, we show that the proposed algorithms are asymptotically optimal as the number of transmit antennas M goes to infinity and the millimeter wave channel has a limited number of paths P, i.e., P

Millimeter-wave (mmWave) communication with a large bandwidth can result in a significantly improved data rate in wireless communications. To overcome high path-loss in the mmWave frequency band, beamforming technology is necessary. Especially, there has been widespread interest in development of hybrid beamforming (HB) technologies, in view of reducing cost and power consumption in massive multiple input multiple output (MIMO) systems. Some of existing researches on HB algorithms assumed perfect channel state information (CSI) and the others used beam training process in case of assuming imperfect CSI. When beam training process is used, enough beam training has to be conducted to achieve sufficient system performance in massive MIMO systems, which results in significant training overhead. Thus, it is necessary to reduce beam training complexity. Compared to state-of-the-art technology, we propose a multi-user HB system using codebooks based on a deep neural network (DNN) in this paper. In our proposed scheme, beam codewords for the base station (BS) and all users can be inferred using limited beam training in cases when the channel state information (CSI) is unknown. In order to apply the proposed scheme to situations where the CSI is unknown, reference radio frequency (RF) beamformers were introduced. Also, the proposed DNN structure is designed considering introduced reference RF beamformers. By using the proposed DNN with reference RF beamformers, the proposed system can inferred optimal beam codewords with limited beam training. Results obtained from simulations indicate that the proposed scheme can achieve almost the same performance as a conventional scheme with less beam training complexity. We also show that the performances achieved by the proposed scheme are gradually increased as training epoch is increased, eventually converging to a steady-state value.

Reconfigurable intelligent surface (RIS) is considered to be an energy-efficient approach to reshape the wireless environment for improved throughput. Its passive feature greatly reduces the energy consumption, which makes RIS a promising technique for enabling the future smart city. Existing beamforming designs for RIS mainly focus on optimizing the spectral efficiency for single carrier systems. Meanwhile, complicated bit/power allocation on different spatial domain subchannels needs to be designed for better bit error rate (BER) performance in conventional singular value decomposition-based beamforming. To avoid this, in this paper, we propose a geometric mean decomposition-based beamforming for RIS-assisted millimeter wave (mmWave) hybrid MIMO systems. In this way, multiple parallel data streams in the spatial domain can be considered to have the same channel gain, so that the better BER can be achieved without sophisticated bit/power allocation. Moreover, by exploiting the common angular-domain sparsity of mmWave massive MIMO channels over different subcarriers, a simultaneous orthogonal matching pursuit algorithm is utilized to obtain the optimal multiple beams from an oversampling 2D-DFT codebook. Besides, by only leveraging the angle of arrival and angle of departure associated with the line of sight (LoS) channels, we further design the phase shifters for RIS by maximizing the array gain for LoS channel. Simulation results show that the proposed scheme can achieve better BER performance than conventional approaches. Our work is an initial attempt to discuss the broadband beamforming for RIS-assisted mmWave massive MIMO with the hybrid architecture.

This paper considers a hybrid beamforming massive multiple-input multiple-output (MIMO) system operating in time-division duplex (TDD) mode. We investigate the combined impacts of quantized phase shifters (QPSs), channel non-reciprocity and channel estimation errors on the achievable spectral efficiency (SE) of the considered system. We first introduce an equivalent hybrid beamforming structure, based on which, a generic and practical channel non-reciprocity model is built for massive MIMO systems with hybrid structure. This model jointly considers the mismatch between transceiver and receiver circuits, as well as the mutual coupling between antennas. Then we derive the generic expression of achievable SE for both the ideal phase shifters (IPSs) and QPSs cases. Furthermore, the closed-form expressions of SE are derived for IPSs and QPSs cases, respectively. The asymptotic performance in large antenna regime is also analyzed based on these expressions. Simulation results demonstrate the validity and accuracy of the derived analytical expressions. In addition, results show that the phase quantization and channel non-reciprocity both contributes to the SE degradation and the detrimental effects cannot be canceled completely even in the large-antenna regime.

Massive MIMO is one of the promising techniques to improve spectral efficiency and network performance for reaching its targeted multi-gigabit throughput in 5G systems. For 5G New Radio (NR) systems, one of the key differences compared to 4G systems is the utilization of high frequency millimeter wave (mmWave) bands in addition to sub-6GHz bands. To keep the complexity and implementation cost low, hybrid analog-digital beam-forming with large-scale antenna array has become a common design approach to address the issue of higher propagation loss as well as to improve spectral efficiency in mmWave communication in 5G NR. The 5G NR standard is designed to adapt to different beam-forming architecture and deployment scenarios. This paper provides the overview on beam management procedure according to the current 5G standardization progress. We discuss some major challenges of millimeter-wave communications encountered in the current 5G NR standard and present some expected enhancements considered for the future beyond-5G standard.

This paper proposes a hybrid precoding technique based on multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) system to improve bit error rate (BER) performance in hybrid beamforming system. The conventional hybrid beamforming system cannot outperform the performance of full-digital beamforming system in OFDM. In the proposed algorithm, the analog precoding matrices are designed by iterative algorithm and one proper analog precoding matrix with the highest power of hybrid precoder is selected to achieve the highest performance. According to the number of analog precoding matrices, the diversity gain is increased and the performance is improved. The simulation results show that the BER performance and average sum rate for the proposed scheme are higher than the conventional hybrid beamforming system.

Millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems can obtain sufficient beamforming gains to combat severe path loss in signal propagation. The hybrid (analog/digital) beamforming with multiple data streams can be utilized to further improve mmWave spectral efficiency. In this paper, we focus on the hybrid beamforming design of a downlink mmWave massive multi-user MIMO (MU-MIMO) system based on full-connected structure, and aim to maximize the sum rate of the overall system as an objective function. In the analog beamforming stage, a piecewise successive iterative approximation (PSIA) algorithm is proposed to design the analog beamformer and combiner. This algorithm not only has a linear property, but also can obtain closed-form solutions. In the digital beamforming stage, the piecewise successive approximation method is utilized to design the digital beamforming based on the criterion to avoid the loss of information, which can help reduce the computational complexity and is also implemented simply. The results show that the proposed scheme achieves good sum-rate performance in the mmWave massive MU-MIMO system, and outperforms the state-of-the art MIMO hybrid beamforming design schemes, even when the number of base station antennas is not very large.

Design of full-duplex (FD) wireless systems faces many challenges, including self-interference cancellation (SIC), capability to provide high capacity, high flexibility for operation, best usage of resources, etc. In this paper, we propose and investigate a multicarrier-division duplex (MDD) based hybrid beamforming system operated in FD mode, which is endowed with the advantages of both time-division duplex and frequency-division duplex. It also shares some merits of FD and allows to be free of self-interference (SI) in digital domain, but faces the same challenge of SI as the FD in analog domain. Hence in this paper, we first propose an adaptive beamforming assisted SI cancellation scheme with taking into account the practical requirement of analog-to-digital conversion (ADC). It can be shown that the proposed approach is capable of jointly coping with the desired signals' transmission and SI suppression. Then, channel estimation (CEst) in MDD/MU-MIMO system is proposed by exploiting the reciprocity between the uplink and downlink subcarrier channels that is provided by MDD. Correspondingly, the orthogonality-achieving pilot symbols are designed, and the least-square (LS) CEst as well as linear minimum mean-square error (LMMSE) CEst are derived. Finally, the performance of MDD/MU-MIMO systems employing the proposed SIC method is investigated, with respect to the SI cancellation capability, sum-rate potential, CEst performance, and the effect of CEst on the achievable performance. Our studies show that MDD/MU-MIMO provides an effective option for design of future wireless transceivers.