Sub-connected Architecture Research Articles

Sum-Rate Maximization for a Hybrid Precoding-Based Massive MIMO NOMA System with Simultaneous Wireless Information and Power Transmission

Non-orthogonal multiple access (NOMA) has emerged as a key enabling technology in the realm of millimeter-wave (mmWave) massive MIMO (mMIMO) systems for enhancing spectral efficiency (SE). Furthermore, it is believed that simultaneous wireless information and power transmission (SWIPT) will allow for the system’s energy efficiency (EE) to be maximised. The effectiveness of the mmWave mMIMO-NOMA system along with SWIPT has been examined in this article under multi-user (MU) scenarios. This paper’s major goal is to construct a low-complexity hybrid-precoder (HP) while taking into account the sub-connected (SC) architecture. The linear precoder is a computationally demanding technique as a result of the matrix inversion. The authors of this paper have suggested a symmetric sequential over-relaxation (SSOR) complex regularised zero-forcing (CRZF) linear precoder. The power distribution for the mmWave mMIMO-NOMA system and power splitting factors for SWIPT are jointly tuned to maximize the sum rate along with the suggested SSOR-CRZF precoder. In regards to complexity, SE, and EE, the SSOR-CRZF-HP surpasses conventional linear precoders.

Joint Precoding Design for Sub-Connected Hybrid Beamforming System

Hybrid beamforming has been widely considered in millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) system, which can greatly reduce power consumption and hardware cost of data paths. Compared to the fully-connected hybrid beamforming architecture, the sub-connected architecture is more practical for its reduced complexity. However, optimal precoding design for the sub-connected architecture is not straightforward due to the specific block-diagonal structure of analog phase shifter network. Algorithms on fully-digital or fully-connected hybrid beamforming architecture cannot be directly applied to sub-connected architecture. Meanwhile, most existing precoding algorithms in such case can only solve the approximate problem, which results in significant performance loss. In this paper, we study the sum rate maximization problem in the sub-connected architecture. We first relax the objective function and derive a relaxed upper bound of the original problem. Then we propose an algorithm to solve the original problem with a local-optimal solution. Simulation results show that the proposed local-optimal algorithm outperforms the baseline algorithms with better sum rate and energy efficiency performance. Besides, the proposed algorithm also converges quickly and is robust.

Review and Analysis of Beamforming's Power Allocation Studies for (5G) Networks

The necessity to investigate viable spectrum areas for satisfying the anticipated needs has been prompted by the rising cellular data traffic demands. As a result, the scientific community has given (mmWave) communication a lot of attention. In (5G) wireless networks, massive (mmWave MIMO) communications are often accomplished using hybrid transceivers that combine lower and higher as a digital and analog processing units phase shifters and power amplifiers respectfully. This hybrid beamforming (HB) architecture lowers costs and power use, which is consistent with the (5G) network's objective for (EE) of design. In this work, using system models of hybrid transceiver architectures, both digital (DB) and analog (AB) beamforming arrays with possibilities for antenna design, and hybrid beamforming (HB) in heterogeneous wireless networks, they trace the development of (HB) for massive MIMO connections. Authors widen in discussion by focusing on hybrid beamforming to solve resource management issue and examine the applicability of hybrid beamforming (HB) techniques while also highlighting the fascinating difficulties that lie ahead for this field. The main aim of this work is to analyse the many studies and show the advantages and weak points of each single study based on the general and famous parameters as well as explaining the efficiency of all studies. We got an overview of the hybrid beamforming, which covers topics such as application areas; In order to reduce upfront costs and continuing operating costs, the fully-connected architecture can achieve full beamforming gain per RF chain, but at the expense of increased hardware complexity. On the other hand, the low-complexity sub-connected architecture offers reduced beamforming gain.

Joint Spatial and Reflecting Modulation for Active IRS Assisted MIMO Communications

This paper proposes an active-intelligent reflecting surface (IRS) assisted joint spatial and reflecting modulation (A-IRS-JSRM). The proposed A-IRS-JSRM engages an active IRS with sub-connected architecture in which several reflecting elements are grouped into panels, and the elements within a panel have the same amplification factor but different reflection angles. Transmit and receive spatial modulation are employed at the base station (BS) and user equipment (UE) by activating a set of transmit antennas and selecting a receiver antenna. Further, only a subset of panels is switched to the ON state in the active IRS, which is determined by the information bits and thus invokes reflecting modulation. The active IRS panels in the ON state also aid in maximizing the received energy in the chosen receiver antenna at the UE. We present the theoretical analysis of the performance of the proposed A-IRS-JSRM system and validate them with simulation results. Further, we have discussed a low-complexity approach for receiver processing in the compressive sensing framework. We compare the proposed A-IRS-JSRM with closely related IRS-assisted spatial modulation schemes in the literature and demonstrate the outperformance of our proposed approach in terms of achievable rate, bit error rate, and outage probability.

Joint SIC-Based Precoding and Sub-Connected Architecture Design for MIMO VLC Systems

Densely deployed light emitting diodes (LEDs) typically lead to high spatial correlation in multiple-input multiple-output (MIMO) visible light communication (VLC) systems. Although precoding can effectively alleviate the spatial correlation issue, most existing precoding algorithms require a dedicated baseband chain for each LED, leading to high energy consumption and hardware complexity when a large number of LEDs are used. In this paper, a successive interference cancellation (SIC)-based precoding scheme with sub-connected architecture (SIC-SA) is proposed. In the considered model, each baseband chain is connected to an LED sub-array containing multiple LEDs to reduce the complexity. Since, in this case, SIC-based precoding can only determine the signal of each baseband chain for an LED sub-array, while its target is to mitigate the spatial correlation between individual LEDs, the electrical/optical power of each LED must also be jointly optimized to accomplish the target. This joint SIC-based precoding, power allocation, and direct current offset design problem is formulated as an achievable sum rate maximization problem under dimming control and electrical power constraints. A two-step iterative algorithm is proposed to solve this problem. In the first step, the SIC-based precoding is designed to alleviate the multi-user interference. In the second step, the power allocation of LEDs is optimized by matrix decomposition and convex optimization, and a closed-form solution of DC offset is derived. Furthermore, considering the dynamic scenarios, a SIC-based precoding scheme with dynamic sub-connected architecture (SIC-DSA) is proposed, in which a switch network is used to adaptively adjust the LED sub-array structure based on the channel state information. Simulation results show that the proposed SIC-SA and SIC-DSA respectively achieve 0.1340 bps/Hz/W and 0.1305 bps/Hz/W energy efficiency gains over the zero-forcing precoding scheme with SA, when the signal-to-noise ratio is 30 dB.

Joint hybrid-precoding design for MU-MISO systems with a subconnected architecture

In this study, we propose a joint hybrid-precoding algorithm for multiuser multiple-input single-output downlink systems. Specifically, we consider that the base station employs an energy-efficient hybrid-precoding subconnected (SC) architecture with fixed equal subarrays (FESA) (SC-FESA). Optimizing the analog precoding matrix in an SC-FESA architecture is challenging due to its unique constraint structure. In this study, to maximize system sum rate, we propose an efficient method to transform the system’s sum-rate optimization problem into a continuous and differentiable objective function wherein only the nonzero elements of the analog precoding matrix are optimized. For the formulated problem, we develop an alternating optimization (AO) approach to jointly optimize the digital and analog precoders in succession by maximizing the system’s sum rate. Specifically, in the proposed AO method, when the digital precoder is fixed, we employ the Riemannian conjugate gradient algorithm to generate the analog precoder. Furthermore, when the analog precoder is fixed, we use the minimum mean squared error method to obtain the digital precoder. Numerical simulation results show that the proposed AO algorithm improves the sum rate and energy efficiency of the SC-FESA architecture compared to existing algorithms.

Adaptive Hybrid Precoding With High Energy Efficiency for Millimeter Wave Communication Systems

Conventional hybrid precoding architectures for millimeter wave systems can be classified into two categories: fully-connected (FC) and subconnected (SC). However, due to the large number of phase shifters (PSs), the conventional hybrid precoding architectures often suffer from high power consumptions. To this end, two novel energy-efficient architectures are proposed, which are, respectively, named as adaptive FC (AFC) architecture and adaptive SC (ASC) architecture. The proposed architectures can adapt the connections between the radio frequency chains and antenna elements according to the channel environment, so that the hardware overhead can be reduced remarkably compared with the conventional counterparts. Afterwards, two novel hybrid precoding algorithms are proposed based on the AFC and ASC architectures. Particularly, in the proposed algorithm, a subspace projection-based branch-and-bound method is developed to derive the switch precoding matrix, thereby the computational burden can be reduced by more than two orders of magnitude in comparison to the traditional branch-and-bound method. Additionally, the problem of PS precoding matrix is reformulated as a positive semidefinite quadratic form via an elegant diagonal loading factor, so that the PS precoding matrix can be obtained easily. Simulation results verify that the proposed architectures and algorithms can offer both high energy efficiencies and spectral efficiencies.

Active Reconfigurable Intelligent Surface: Fully-Connected or Sub-Connected?

To overcome the “multiplicative fading effect” introduced by passive reconfigurable intelligent surface (RIS), the concept of active RIS has been recently proposed to amplify the radiated signals. However, the existing fully-connected architecture of active RIS consumes high power due to the additionally integrated active components. To address this issue, we propose the sub-connected architecture of active RIS. Different from fully-connected architecture, where each element integrates a dedicated power amplifier, in the sub-connected architecture, multiple elements control their phase shifts independently but share a same power amplifier, which significantly reduces the number of power amplifiers for power saving at the cost of fewer degrees of freedom (DoFs) for beamforming design. Fortunately, our analysis reveals that performance loss introduced by the sub-connected architecture is slight, indicating that it can achieve much higher energy efficiency (EE). Furthermore, we formulate the EE maximization problem in the active RIS-aided system for both architectures and develop a corresponding joint beamforming design. Simulation results verify the proposed sub-connected architecture as an energy-efficient realization of active RIS.

Precoding and Beamforming Techniques in mmWave-Massive MIMO: Performance Assessment

Massive MIMO and mmWave communication are the technologies for achieving 5G design goals. Fortunately, these two technologies share a symbiotic integration. As a result, amalgamating mmWave communications with massive MIMO forms,mmWave-massive MIMO,” which significantly improves spectral and energy efficiency. It also achieves high multiplexing gains and increases mobile network capacity. However, massive MIMO, mmWave communications, and mmWave-massive MIMO systems have been studied independently. Consequently, this article explores the ideas, performances, comparisons, and discussions of these three 5G technologies jointly, considering their precoding and beamforming methods. On the other hand, the complexity of these technologies increases when a large number of antennas and radio frequencies (RFs) are used. Thus, several investigations are going on to search for the appropriate precoding and beamforming strategies with low cost, power, and complexity. Therefore, massive MIMO linear precoding techniques such as zero-forcing, maximum ratio transmission, regularized zero-forcing, truncated polynomial expansion and phased zero forcing are addressed in this work. In addition, the most common non-linear precoding schemes: dirty-paper coding, Tomlinson-Harashima, and vector perturbation, are presented. Furthermore, a detailed discussion of the beamforming techniques called analog, digital, and hybrid analog-digital beamforming schemes is included. We also examine the potential of hybrid analog-digital beamforming with its fully-connected and sub-connected architecture approaches in making mmWave massive MIMO a reality. We evaluate their performance with the parameters: bit error rate, signal to noise ratio, complexity, spectral efficiency, and energy efficiency. According to the analytical and simulation results, the partially-connected hybrid analog/digital beamforming architecture offers better all-over performance for mmWave-Massive MIMO communications by compromising: power consumption, cost, complexity, and performance. Finally, the potential future directions in mmWave-massive MIMO precoding and beamforming challenges are addressed.

On the Performance of Fully-Connected and Sub-Connected Hybrid Beamforming System

When the wireless communication system evolves to massive multiple-input multiple-output (MIMO) system, the system becomes more and more complex. Hybrid beamforming architecture provides excellent trade-off between the system performance and the complexity for the massive MIMO system. Hybrid beamforming architecture can be classified as fully-connected (FC) and sub-connected (SC) architecture. Existing studies on the system performance comparison of the two architectures are limited. In this paper, we investigate the performance of the FC and SC architecture in the multi-user multiple-input single-output (MU-MISO) system. We derive the closed-form sum rate of two architectures under the same total transmission power constraint. From the results, we conclude that the system performance of FC architecture is always better than that of SC architecture. When the number of users is small or the signal noise ratio (SNR) is low, the performance difference between the FC and SC architecture is small. Our analysis can serve as a guideline of choosing hybrid beamforming architectures.

Energy-Efficient Multi-Antenna Hybrid Block Diagonalization Precoding and Combining for MmWave Massive Multi-User MIMO Systems

The millimeter-wave (mmWave) massive multi-user multiple-input multiple-output (MU-MIMO) can significantly improve the throughput and spectral efficiency by equipping multi-antenna at both the base station (BS) and the users, which is greatly promising technologies for wireless communication systems. However, the large hardware requirement and high energy consumption are great hindrances. To effectively reduce the hardware and computational complexity and achieve a better trade-off between system complexity and performance, we propose an energy-efficient split sub-connected architecture based multi-user hybrid block diagonalization (BD) precoding and combining (Sub MU-HBD) scheme. The proposed Sub MU-HBD scheme employs the split sub-connected architecture to reduce the hardware complexity and improve spectral efficiency. Additionally, to significantly reduce the inter-user interference and further lessen the computational complexity, a low-dimensional digital precoding and combining scheme based on the BD is proposed. Furthermore, we propose a proportional water-filling power allocation algorithm to ensure optimal power distribution to users and improve energy efficiency. The simulation results demonstrate that the proposed Sub MU-HBD scheme can achieve near-optimal, and at times even better performance than some existing schemes.

Hierarchical Energy Efficient Hybrid Precoding for Configurable Sub-Connected MIMO Systems

This paper proposed a configurable sub-connected architecture with a framework that dynamically activates the near-optimal subset of antennas and RF chains to implement energy-efficient hybrid precoding in millimeter wave multiple-input multiple-output system. Since the exhaust search is computational intractable, we propose a two-stage hybrid precoding algorithm, where the digital precoder is designed to eliminate the inter-user interference by zero-forcing rule. Specifically, in the first stage, we introduce an extended cross-entropy algorithm that adaptively updates the probability distribution of potential states in the analog precoder matrix, which can generate a solution that is close to the optimal with a sufficiently high probability. In the second stage, a QR-based subset selection algorithm is proposed to pick the near-optimal subset of the RF chains to further cut down on the energy cost. Simulation results show that proposed extend-CE algorithm gets favorable performance in terms of energy efficiency, and QR-based RF chain selection can achieve a near-optimal performance. Other hybrid precoding algorithms can also be incorporated into the proposed RF chain selection algorithm.

Unequally Sub-Connected Architecture for Hybrid Beamforming in Massive MIMO Systems

A variety of hybrid analog-digital beamforming architectures have recently been proposed for massive multiple-input multiple-output (MIMO) systems to reduce energy consumption and the cost of implementation. In the analog processing network of these architectures, the practical sub-connected structure requires lower power consumption and hardware complexity than the fully connected structure but cannot fully exploit the beamforming gains, which leads to a loss in overall performance. In this work, we propose a novel unequal sub-connected architecture for hybrid combining at the receiver of a massive MIMO system that employs unequal numbers of antennas in sub-antenna arrays. The optimal design of the proposed architecture is analytically derived, and includes antenna allocation and channel ordering schemes. Simulation results show that an enhancement of up to 10% can be attained in the total achievable rate by unequally assigning antennas to sub-arrays in the sub-connected system at the cost of a marginal increase in power consumption. Furthermore, in order to reduce the computational complexity involved in finding the optimal number of antennas connected to each radio frequency (RF) chain, we propose three low-complexity antenna allocation algorithms. The simulation results show that they can yield a significant reduction in complexity while achieving near-optimal performance.

Joint Multi-Beam Training and Codebook Design for Indoor High-Throughput Transmissions Under Limited Training Steps

Along with the evolution of wireless technologies, users are expected to have high-throughput and low-latency indoor mobile applications, such as augmented reality and virtual reality. Therefore, the use of millimeter-wave for low-latency communications becomes attractive. Under low-latency criteria, a fast multi-link establishment is desired. In this paper, we present a novel concurrent multi-beam training jointly with a proposed multi-beam codebook. First, we propose a progressive multi-beam estimation (PMBE). This algorithm makes multiple coarse beams emerge within a few training steps. Second, we propose a Fast Fourier Transform (FFT)-based codebook which can be considered as a Discrete Fourier Transform based codebook built in a bit-reversal scrambler, thus forming a scattering multi-beam codebook. This FFT-based codebook design is optimally implemented on a sub-connected architecture, a hardware-efficient architecture. For fast multi-link establishment, we design our concurrent multi-beam training by combining the proposed PMBE jointly with the proposed FFT-based codebook, so as to further improve multiplexing gain. Since multiplexing gain can occur immediately, even under strictly-limited training steps, the high data rate can be achieved. Simulation results show that when reaching more than 95% capacity gain of exhaustive channel estimation, our work only spends 1% training steps of the exhaustive case.

Multi-User Linear Equalizer and Precoder Scheme for Hybrid Sub-Connected Wideband Systems

Millimeter waves and massive multiple-input multiple output (MIMO) are two promising key technologies to achieve the high demands of data rate for the future mobile communication generation. Due to hardware limitations, these systems employ hybrid analog–digital architectures. Nonetheless, most of the works developed for hybrid architectures focus on narrowband channels, and it is expected that millimeter waves be wideband. Moreover, it is more feasible to have a sub-connected architecture than a fully connected one, due to the hardware constraints. Therefore, the aim of this paper is to design a sub-connected hybrid analog–digital multi-user linear equalizer combined with an analog precoder to efficiently remove the multi-user interference. We consider low complexity user terminals employing pure analog precoders, computed with the knowledge of a quantized version of the average angles of departure of each cluster. At the base station, the hybrid multi-user linear equalizer is optimized by using the bit-error-rate (BER) as a metric over all the subcarriers. The analog domain hardware constraints, together with the assumption of a flat analog equalizer over the subcarriers, considerably increase the complexity of the corresponding optimization problem. To simplify the problem at hand, the merit function is first upper bounded, and by leveraging the specific properties of the resulting problem, we show that the analog equalizer may be computed iteratively over the radio frequency (RF) chains by assigning the users in an interleaved fashion to the RF chains. The proposed hybrid sub-connected scheme is compared with a fully connected counterpart.

Energy-Efficient Hybrid Precoding With Low Complexity for mmWave Massive MIMO Systems

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) utilizes large antenna arrays and is considered a promising technology for fifth-generation (5G) and beyond wireless communication systems. However, the high-power consumption of the radio-frequency (RF) chains makes it infeasible. To solve this problem, hybrid precoding is proposed, which is a combination of analog and digital precoding. The fully connected architecture hybrid precoding still requires a large number of phase shifters (PSs). The sub-connected architecture can greatly reduce the required power consumption, and however, it cannot obtain a satisfactory achievable rate. To avoid the high energy consumption and obtain a high resolution, we propose a novel partly connected architecture in this paper. In addition, we propose an energy-efficient successive interference cancelation (SIC) hybrid precoding based on the partly connected architecture, which transforms the problem of maximizing the total achievable rate with non-convex constraints into a series of sub-rate optimization problems. Furthermore, a low-complexity energy-efficient SIC hybrid precoding based on the partly connected architecture is developed, which uses the partial singular value decomposition (SVD) to realize the sub-rate optimization and significantly reduce the complexity. Theoretical analysis demonstrates the superiority of the proposed hybrid precoding in terms of complexity. The simulation results indicate that the proposed hybrid precoding algorithms enjoy better energy efficiency and achievable rate performance than some recently proposed hybrid precoding algorithms.

Hybrid Multi-User Equalizer for Massive MIMO Millimeter-Wave Dynamic Subconnected Architecture

This paper proposes a hybrid multi-user equalizer for the uplink of broadband millimeter-wave massive multiple input/multiple output (MIMO) systems with dynamic subarray antennas. Hybrid subconnected architectures are more suitable for practical applications since the number of required phase shifters is lower than in fully connected architectures. We consider a set of only analog precoded users transmitting to a base station and sharing the same radio resources. At the receiver end, the hybrid multi-user equalizer is designed by minimizing the sum of the mean square error (MSE) of all subcarriers, considering a two-step approach. In the first step, the digital part is iteratively computed as a function of the analog part. It is considered that the digital equalizers are computed on a per subcarrier basis, while the analog equalizer is constant over the subcarriers and the digital iterations due to hardware constraints. In the second step, the analog equalizer with dynamic antenna mapping is derived to connect the best set of antennas to each radio frequency (RF) chain. For each subset of antennas, one antenna and a quantized phase shifter are selected at a time, taking into account all previously selected antennas. The results show that the proposed hybrid dynamic two-step equalizer achieves a performance close to the fully connected counterpart, although it is less complex in terms of hardware and signal processing requirements.

Hardware-Efficient Hybrid Precoding for Millimeter Wave Systems With Multi-Feed Reflectarrays

Current hybrid precoding schemes for large-scale antenna array-aided millimeter wave (mmWave) wireless systems adopt either full-connected or sub-connected architectures based on phased array antennas (PAAs). The former achieves excellent precoding performance but imposes considerably high hardware complexity, while the latter is relatively simple but suffers from high performance loss. In this paper, we exploit the new technology of reflectarray antennas, which among other advantages offers significant hardware efficiency gain, and propose a novel hybrid precoding architecture based on multi-feed reflectarray antennas (MRAs) for mmWave wireless systems, which is capable of achieving considerable hardware efficiency gain even over the low-hardware-complexity sub-connected architecture with PAAs. The mathematical model is provided for this novel hybrid precoding architecture, and an efficient alternating descent algorithm is developed to jointly design the analog and digital precoders for the proposed hybrid precoding scheme. Numerical results obtained demonstrate that our proposed precoding scheme with MRAs achieves much better precoding performance than its sub-connected counterpart with PAAs.

Transmit Beamforming for Single-User Large-Scale MISO Systems With Sub-Connected Architecture and Power Constraints

This letter considers optimal transmit beamforming for a sub-connected large-scale MISO system with RF chain and per-antenna power constraints. The system is configured such that each RF chain serves a group of antennas. For the hybrid scheme, necessary and sufficient conditions to design the optimal digital and analog precoders are provided. It is shown that, in the optimum, the optimal phase shift at each antenna has to match the channel coefficient and the phase of the digital precoder. In addition, an iterative algorithm is provided to find the optimal power allocation. We study the case where the power constraint on each RF chain is smaller than the sum of the corresponding per-antenna power constraints. Then, the optimal power is allocated based on two properties: each RF chain uses full power and if the optimal power allocation of the unconstraint problem violates a per-antenna power constraint then it is optimal to allocate the maximal power for that antenna.

Iterative Multiuser Equalization for Subconnected Hybrid mmWave Massive MIMO Architecture

Millimeter waves and massive MIMO are a promising combination to achieve the multi-Gb/s required by future 5G wireless systems. However, fully digital architectures are not feasible due to hardware limitations, which means that there is a need to design signal processing techniques for hybrid analog-digital architectures. In this manuscript, we propose a hybrid iterative block multiuser equalizer for subconnected millimeter wave massive MIMO systems. The low complexity user-terminals employ pure-analog random precoders, each with a single RF chain. For the base station, a subconnected hybrid analog-digital equalizer is designed to remove multiuser interference. The hybrid equalizer is optimized using the average bit-error-rate as a metric. Due to the coupling between the RF chains in the optimization problem, the computation of the optimal solutions is too complex. To address this problem, we compute the analog part of the equalizer sequentially over the RF chains using a dictionary built from the array response vectors. The proposed subconnected hybrid iterative multiuser equalizer is compared with a recently proposed fully connected approach. The results show that the performance of the proposed scheme is close to the fully connected hybrid approach counterpart after just a few iterations.