Limited Radio Frequency Chains Research Articles

Sparse Multiuser Receiver Design in Large Scale Array System

This paper focuses on the problem of utilizing millimeter wave (mmWave) and Terahertz (THz) massive hybrid arrays to serve multiple users simultaneously. The mmWave and THz massive arrays are characterized by wide bandwidth and high gain, leading to extensive application prospects. Moreover, a hybrid structure array can combine multiple antenna signals through the phase shifter network. Compared with the full digital array, it is a cost-effective technique that can be functional with fewer radio frequency (RF) chains. However, owing to the adoption of a discrete Fourier transform (DFT) structure, most traditional massive hybrid arrays, which allocate one chain to each user, are restricted to scenarios where the number of RF chains is more than that of users. Otherwise, even users with ideal channel conditions and short distances are inherently difficult to assign an independent chain. Thus, it will limit the scale of users that the base station (BS) can support. Inspired by the above analysis, this paper develops a method to provide service for more users with limited RF chains. Firstly, an analog matrix designing method based on the minimax criterion, which enables arrays access to multiple users, is proposed to guarantee each user a good array gain. Secondly, we establish a receiver designing scheme by the GAMP algorithm to receive signals from multiple users at the same time. Additionally, good bit error rate (BER) performance can be obtained under the condition that the observation matrix is not of full rank. Finally, numerical simulations demonstrate the effectiveness of our proposed method.

Machine Learning-Inspired Hybrid Precoding for HAP Massive MIMO Systems with Limited RF Chains

Energy efficiency (EE) is the main target of wireless communication nowadays. In this paper, we investigate hybrid precoding (HP) and massive multiple-input multiple-output (MIMO) systems for a high-altitude platform (HAP). The HAP is an emerging solution operating in the stratosphere at an amplitude of up to 20–40 km to provide communication facilities that can achieve the best features of both terrestrial and satellite systems. The existing hybrid beamforming solution on a HAP requires a large number of high-resolution phase shifters (PSs) to realize analog beamforming and radio frequency (RF) chains associated with each antenna and achieve better performance. This leads to enormous power consumption, high costs, and high hardware complexity. To address such issues, one possible solution that has to be tweaked is to minimize the number of PSs and RFs or reduce their power consumption. This study proposes an HP sub-connected low-resolution bit PSs to address these challenges while lowering overall power consumption and achieving EE. To significantly reduce the RF chain in a massive MIMO system, HP is a suitable solution. This study further examined adaptive cross-entropy (ACE), a machine learning-based optimization that optimizes the achievable sum rate and energy efficiency in the Rician fading channel for HAP massive MIMO systems. ACE randomly generates several candidate solutions according to the probability distribution (PD) of the elements in HP. According to their sum rate, it adaptively weights these candidates’ HP and improves the PD in HP systems by minimizing the cross-entropy. Furthermore, this work suggests energy consumption analysis performance evaluation to unveil the fact that the proposed technique based on a sub-connected low-bit PS architecture can achieve near-optimum EE and sum rates compared with the previously reported methods.

Angle-Domain Hybrid Beamforming-Based mmWave Massive MIMO-NOMA Systems

The millimeter-wave (mmWave) large-scale antenna arrays (LSAAs) systems play a vital role in increasing the beamforming (BF) gain and acquiring highly directional propagation. Recently, non-orthogonal multiple access (NOMA) has been integrated into these systems to manage massive connectivity and achieve spectral-efficient communications. This paper focuses on angle-domain (AD) hybrid beamforming (BF) for mmWave LSAAs and NOMA systems, thanks to the low complexity, power consumption, and channel estimation overhead. However, with limited radio-frequency chains, the hybrid BF-based single-beam (SB)-NOMA scheme generating a single beam to serve the NOMA users fails to exploit the multi-user diversity due to narrow beams with LSAAs. To tackle this limitation, we design schemes offering additional degrees of freedom. More importantly, they require only the knowledge of angular information and are suitable for either linear or rectangular antenna arrays, unlike those proposed in the literature. The first scheme exploits the time-domain resources to schedule groups having high spatial interference within distinct time slots. To minimize the need for fast and precise synchronization when applying time division multiple access (TDMA) with mmWave NOMA, we leverage the multi-beam (MB)-NOMA framework. And we propose a joint SB-and MB-NOMA scheme to benefit from NOMA multi-user diversity, whatever the cell load and the users’ positions. Using the New York University channel simulator (NYUSIM), we further validate the performance of the proposed schemes compared to the solution proposed in the literature and others using fully digital BF. Specifically, the proposed TDMA-based scheme achieves a sum-rate gain of up to 83% over the TDMA-based one existing in the literature. Moreover, we verify the superiority of applying both SB-and MB-NOMA instead of only MB-NOMA.

Deep Learning-Based Channel Estimation for Massive MIMO With Hybrid Transceivers

Accurate and efficient estimation of the high dimensional channels is one of the critical challenges for practical applications of massive multiple-input multiple-output (MIMO). In the context of hybrid analog-digital (HAD) transceivers, channel estimation becomes even more complicated due to information loss caused by limited radio-frequency chains. The conventional compressive sensing (CS) algorithms usually suffer from unsatisfactory performance and high computational complexity. In this paper, we propose a novel deep learning (DL) based framework for uplink channel estimation in HAD massive MIMO systems. To better exploit the sparsity structure of channels in the angular domain, a novel angular space segmentation method is proposed, where the entire angular space is segmented into many small regions and a dedicated neural network is trained offline for each region. During online testing, the most suitable network is selected based on the information from the global positioning system. Inside each neural network, the region-specific measurement matrix and channel estimator are jointly optimized, which not only improves the signal measurement efficiency, but also enhances the channel estimation capability. Simulation results show that the proposed approach significantly outperforms the state-of-the-art CS algorithms in terms of estimation performance and computational complexity.

Intelligent Reflecting Surface-Assisted mmWave Communication With Lens Antenna Array

In millimeter wave (mmWave) communication, lens antenna array has been considered as a potential technique to combat the severe path loss. However, blockage effect still exists. To address this issue, we design an intelligent reflecting surface (IRS) assisted wideband mmWave communication system with lens antenna array with limited radio frequency (RF) chains. This is the first attempt to design a mmWave communication system with the integration of IRS and lens antenna array. The designed system can realize high robust, low-complexity and cost-effective mmWave communication. To achieve desired performance, we first derive the signal-to-interference-plus-noise ratio (SINR) expression with the intersymbol interference, then we formulate a SINR maximization problem, by jointly considering the beamforming vector at base station with limited RF chains and reflection coefficient matrices at IRS with discrete phase levels. This problem is intractable and non-convex. To solve it, we optimize the reflection coefficient matrices by quadratic transformation with low-complexity closed-form expression, and the beamforming vector is handled by sparse beamforming optimization, where the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{0}$ </tex-math></inline-formula> norm is approximated by the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula> norm. Finally, simulation results demonstrate the advantages of our proposed algorithm compared with benchmark schemes, e.g., 5 dB SINR gain can be achieved compared with existing algorithm.

Beamforming design with fully connected analog beamformer using deep learning

SummaryBeamforming (BF) architecture, an emerging approach for large‐scale antenna arrays, is a key task for the next decade's communication systems. The proposed approach in millimeter wave transmission is a fully connected analog phase‐shifter‐based BF architecture with limited radio frequency chains and imperfect channel state information (CSI). Deep learning (DL) is a powerful method for channel estimation and signal identification in wireless communications. Hence, this research proposes a DL‐enabled beamforming neural network (BFNN) which can be programmed to optimize the beamformer to attain better spectral efficiency. Simulation findings reveal that the proposed BFNN achieves significant performance gain and high robustness to imperfect CSI. The proposed BFNN greatly decreases the computational complexity by 0.16 million floating point operations (FLOPs) over 0.26 million FLOPs by conventional BF algorithms.

Higher Spectral Efficiency for mmWave MIMO: Enabling Techniques and Precoder Designs

Millimeter wave (mmWave) communications can support huge data rate growth and wide bandwidth in the new 5G era, and a hybrid precoding architecture can effectively handle the high hardware cost and energy consumption problems. A crucial goal is to optimize the system's spectral efficiency (SE) by effectively designing the digital precoder and the analog precoder. In this article, we review the conventional hybrid precoding schemes in mmWave systems, and then investigate several novel systems with index modulation (IM) techniques as well as their hybrid precoder designs. With the advantages of IM, higher SE can be achieved with limited radio frequency chains in mmWave systems. Simulation results demonstrate that two types of IM-aided systems both outperform conventional mmWave systems, which proves that IM is an enabling technique in practical SE-driven mmWave systems. Finally, we discuss the limiting factors and outlook of IM-aided mmWave systems in beyond-5G wireless communications.

Low-Complexity Channel Estimation for Intelligent Reflecting Surface-Enhanced Massive MIMO

Intelligent reflecting surface (IRS) consisting of massive passive elements is a promising technology to save the hardware cost and energy consumption, but with a challenging channel estimation of IRS transmission links. In this letter, a low-complexity channel estimation method is first proposed for IRS-enhanced multi-user massive multiple-input and multiple-output systems, which can attain the separate channel state information (CSI) of the cascaded link. The acquisition of the separate CSI contributes to the flexibility of channel estimations for the multi-user case with user mobility. The proposed channel estimation consists of direction-of-arrival and path gain estimations, which are implemented by using the limited radio frequency chains and pilots, respectively. Besides, the computational complexity is analyzed for the proposed and existing methods for comparison. Numerical results show that the proposed method can achieve accurate channel estimation with a low complexity.

A Comprehensive Survey on Millimeter Wave Communications for Fifth-Generation Wireless Networks: Feasibility and Challenges

Fifth-generation (5G) cellular networks will almost certainly operate in the high-bandwidth, underutilized millimeter-wave (mmWave) frequency spectrum, which offers the potentiality of high-capacity wireless transmission of multi-gigabit-per-second (Gbps) data rates. Despite the enormous available bandwidth potential, mmWave signal transmissions suffer from fundamental technical challenges like severe path loss, sensitivity to blockage, directivity, and narrow beamwidth, due to its short wavelengths. To effectively support system design and deployment, accurate channel modeling comprising several 5G technologies and scenarios is essential. This survey provides a comprehensive overview of several emerging technologies for 5G systems, such as massive multiple-input multiple-output (MIMO) technologies, multiple access technologies, hybrid analog-digital precoding and combining, non-orthogonal multiple access (NOMA), cell-free massive MIMO, and simultaneous wireless information and power transfer (SWIPT) technologies. These technologies induce distinct propagation characteristics and establish specific requirements on 5G channel modeling. To tackle these challenges, we first provide a survey of existing solutions and standards and discuss the radio-frequency (RF) spectrum and regulatory issues for mmWave communications. Second, we compared existing wireless communication techniques like sub-6-GHz WiFi and sub-6 GHz 4G LTE over mmWave communications which come with benefits comprising narrow beam, high signal quality, large capacity data transmission, and strong detection potential. Third, we describe the fundamental propagation characteristics of the mmWave band and survey the existing channel models for mmWave communications. Fourth, we track evolution and advancements in hybrid beamforming for massive MIMO systems in terms of system models of hybrid precoding architectures, hybrid analog and digital precoding/combining matrices, with the potential antenna configuration scenarios and mmWave channel estimation (CE) techniques. Fifth, we extend the scope of the discussion by including multiple access technologies for mmWave systems such as non-orthogonal multiple access (NOMA) and space-division multiple access (SDMA), with limited RF chains at the base station. Lastly, we explore the integration of SWIPT in mmWave massive MIMO systems, with limited RF chains, to realize spectrally and energy-efficient communications.

Beamforming Design for Large-Scale Antenna Arrays Using Deep Learning

Beamforming (BF) design for large-scale antenna arrays with limited radio frequency chains and the phase-shifter-based analog BF architecture, has been recognized as a key issue in millimeter wave communication systems. It becomes more challenging with imperfect channel state information (CSI). In this letter, we propose a deep learning based BF design approach and develop a BF neural network (BFNN) which can be trained to learn how to optimize the beamformer for maximizing the spectral efficiency with hardware limitation and imperfect CSI. Simulation results show that the proposed BFNN achieves significant performance improvement and strong robustness to imperfect CSI over the conventional BF algorithms.

Spatial Multiplexing With Limited RF Chains: Generalized Beamspace Modulation (GBM) for mmWave Massive MIMO

Millimeter wave (mmWave) massive multiple-input multiple-output (mMIMO) has been recognized as a promising candidate for 5G communications for its capability of supporting Gb/s transmission. However, it is a common exercise to deploy a limited number of radio-frequency (RF) chains at mmWave mMIMO transceivers due to hardware complexity and cost. As a result, the potential multiplexing gain (MG), which is restricted by the smaller number of RF chains at the transmitter and receiver, is markedly compromised. In order to boost the MG and spectral efficiency (SE), we innovatively develop a novel index modulation termed as the generalized beamspace modulation (GBM). The acquisition of (sub-)beamspace is owing to a natural exploitation of the unique features of mmWave mMIMO. Based on the (sub-)beamspace, a complete GBM transceiver is designed and optimized. Unlike existing alternatives that are largely digital based, our GBM is tailored for the hybrid structure of mmWave mMIMO and can, thereby, realize efficient spatial multiplexing despite the limited RF chains. Extensive analyses and simulations have demonstrated remarkable superiority of GBM over existing counterparts in terms of the error performance and SE.

Secrecy enhancement in MIMO-OFDM-IM systems with limited RF chains

A physical layer security scheme is proposed to resist eavesdropping of a multiple-input multiple-output (MIMO) system using orthogonal frequency-division multiplexing with index modulation (OFDM-IM) in the presence of a multiple-antenna passive eavesdropper, where limited number of radio frequency (RF) chains is required and additional bits are conveyed through the indices of activated transmit antennas. The secure transmission against eavesdropping is achieved by injecting artificial noise (AN) in the frequency and spatial domains. When 2 RF chains are used, the structure of quadrature spatial modulation (QSM) is exploited. The generated OFDM-IM symbols are split into two orthogonal parts, and precoded ANs are injected into each part to produce combined symbols for transmission. The secure scheme is extended to general scenarios when other number of RF chains are available based on the idea of generalized spatial modulation (GSM) and AN. The secrecy rates of the proposed schemes are analyzed, and a closed-form approximation for the ergodic secrecy rate is derived to reduce the computation complexity. The power allocation ratio and design parameters are discussed through numerical simulations. Compared to the existing secure OFDM-IM scheme (modified for MIMO systems), the proposed secure MIMO OFDM-IM shows better performance against eavesdropping when the eavesdropping and legitimate links have the same channel quality and the eavesdropping-legitimate channel correlation is considered. When 2 RF chains are available, the secure scheme based on QSM is recommended since it outperforms the secure scheme based on GSM with 2 RF chains.

Mode Modulation for Wireless Communications With a Twist

Recently, orbital angular momentum (OAM) based vorticose communication has attracted much attention because of its potential to significantly increase the spectrum efficiency (SE) of wireless communications. However, the multiple radio frequency (RF) chains used for multiple OAM modes lead to an unexpected cost for wireless vorticose communications. In this paper, we first propose the mode modulation (MM) based OAM system to allow multiple OAM modes sharing a common RF chain. The proposed MM cannot only reduce the hardware cost, but also boost the SE by introducing the mode as an additional dimension for data transmission. Moreover, without the channel state information (CSI) feedback, we develop an equal-probability mode modulation (EMM) scheme to maximize the SE of MM-based OAM systems with the limited RF chains. In addition, with the CSI feedback, we develop a Huffman coding based adaptive mode modulation (AMM) scheme, which can adaptively choose the OAM modes to further increase the SEs of OAM-based wireless communications. We also develop the joint power and probability allocation policy for the AMM scheme to achieve the maximum SEs for OAM-based vorticose communications. Numerical results demonstrate that the MM can offer a new mode dimension for vorticose communications and the AMM scheme can achieve larger SE than the EMM scheme.

Secrecy Rate Analysis for Pilot Contamination Attacks in Massive MIMO With Limited RF Chains

Pilot contamination attacks in training-based massive multiple-input multiple-output systems with limited radio-frequency chains are investigated. A lower bound for the achievable secrecy rate in the presence of a multi-antenna active eavesdropper is derived in closed-form. Thereby, the secrecy rate loss due to the active attacks over the passive eavesdropping is quantified. Moreover, the secrecy rate loss due to hybrid precoding over the full-dimensional digital precoding is derived.

Channel Estimation for Millimeter-Wave MIMO Communications With Lens Antenna Arrays

Millimeter-wave (mmWave) communication is a promising technology for future wireless communication systems. By utilizing the advanced lens antenna arrays, a mmWave lens multiple-input multiple-output (MIMO) system significantly reduces the number of radio-frequency (RF) chains required, and thus, achieves cost-effective implementation. In this paper, we propose a novel channel estimation scheme for the mmWave lens MIMO system with limited RF chains. The proposed scheme is applicable for the general mmWave MIMO communications, regardless of narrow-band or wide-band, time-division-duplexing or frequency-division-duplexing operations. By exploiting the energy-focusing property of lens arrays, jointly with the multipath sparsity of mmWave channels, the proposed channel estimation scheme requires only low-complexity two-way energy-based antenna selections and reduced-size MIMO channel estimation. Numerical results show that under limited RF chains and taking the channel training overhead and imperfect estimation into account, the proposed lens MIMO system achieves significantly higher throughput compared to the more complex benchmark system with the conventional uniform planar array and MIMO-orthogonal-frequency-division-multiplexing-based transmissions.

A Novel Equivalent Baseband Channel of Hybrid Beamforming in Massive Multiuser MIMO Systems

The conventional full radio frequency (RF) configuration appears to be unfeasible in massive multiple-input-multiple-output (MIMO) systems due to the still-exorbitant energy consumption of RF chains at millimeter wave frequencies. In this letter, we address this challenge by proposing a novel two step hybrid beamforming scheme for the downlink massive multiuser MIMO systems. Our purpose is to maximize the overall spectral efficiency with limited RF chains. In the first step, we aim to find an equivalent baseband channel by maximizing its capacity through the analog RF processing. In the second step, the inter-user interference is eliminated by the block diagonalization (BD) technique. Simulation results demonstrate that the proposed scheme can achieve a higher capacity than the traditional BD scheme with much fewer number of RF chains.

Secure Massive MIMO Systems With Limited RF Chains

In future practical deployments of massive multi-input multi-output (MIMO) systems, the number of radio frequency (RF) chains at the base stations (BSs) may be much smaller than the number of BS antennas to reduce the overall expenditure. In this paper, we propose a novel design framework for joint data and artificial noise (AN) precoding in a multiuser massive MIMO system with limited number of RF chains, which improves the wireless security performance. With imperfect channel state information (CSI), we analytically derive an achievable lower bound on the ergodic secrecy rate of any mobile terminal (MT), for both analog and hybrid precoding schemes. The closed-form lower bound is used to determine optimal power splitting between data and AN that maximizes the secrecy rate through simple one-dimensional search. Analytical and numerical results together reveal that the proposed hybrid precoder, although suffers from reduced secrecy rate compared with theoretical full-dimensional precoder, is free of the high computational complexity of large-scale matrix inversion and null-space calculations, and largely reduces the hardware cost.

Antenna Selection in RF-Chain Limited MIMO Interference Networks under Interference Alignment

In multiple-input multiple-output (MIMO) systems, the deployment of multiple radio-frequency (RF) chains is much more expensive than that of multiple antennas. Antenna selection (AS) is a low-cost low-complexity method to exploit the diversity gain in MIMO systems with sufficient antennas but limited RF chains. Among the AS algorithms, decremental AS has been shown to be near-optimal compared with exhaustive search. In this paper, we study AS in MIMO interference networks applying the interference alignment (IA) scheme. IA aligns and zero-forces the interference via transmitter–receiver beamforming and achieves a maximum degree of freedom. The feasibility and alignment topology of IA is dependent on the active antenna configuration. For users in tightly feasible systems, IA can be resumed after AS by adapting the beamformers locally, and the exact transmission rate can be known. For users in superfeasible systems, we derive the expected rate under IA conditioned on local channel state information (CSI). Adopting the rate or expected rate as selection criterion, we propose the decremental AS algorithms achieving a twofold complexity reduction. Simulation results and complexity analysis show that the algorithms achieve near-optimal performance at low complexity.

Phase Only RF Precoding for Massive MIMO Systems With Limited RF Chains

Massive MIMO systems promise high spectrum efficiency by deploying <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$M \gg 1$</tex></formula> antennas at the base station (BS). However, to achieve the full gain provided by massive MIMO, the BS requires <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$M$</tex> </formula> radio frequency (RF) chains, which are expensive. This motivates us to consider RF-chain limited massive MIMO systems with <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$M$</tex> </formula> antennas but only <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$S \ll M$</tex></formula> RF chains. We propose a two-stage precoding scheme to efficiently exploit the large spatial degree of freedom (DoF) gain in massive MIMO systems with limited RF chains and reduced channel state information (CSI) signaling overhead. In this scheme, the MIMO precoder is partitioned into a high-dimensional phase only RF precoder followed by a low-dimensional baseband precoder. The RF precoder is adaptive to the spatial correlation matrices for inter-cluster interference mitigation. The baseband precoder is adaptive to the reduced dimensional “effective” CSI for intra-cluster spatial multiplexing. We formulate the two stage precoding problem such that the minimum (weighted) average data rate of users is maximized under the phase only constraint on the RF precoder and the limited RF chain constraint. This is a combinatorial optimization problem which is in general NP-hard. We propose a low complexity solution based on a novel bi-convex approximation approach. Simulations show that the proposed design has significant gain over various baselines.