Beamforming Training Research Articles

Effective Acoustic Model-Based Beamforming Training for Static and Dynamic Hri Applications.

Human-robot collaboration will play an important role in the fourth industrial revolution in applications related to hostile environments, mining, industry, forestry, education, natural disaster and defense. Effective collaboration requires robots to understand human intentions and tasks, which involves advanced user profiling. Voice-based communication, rich in complex information, is key to this. Beamforming, a technology that enhances speech signals, can help robots extract semantic, emotional, or health-related information from speech. This paper describes the implementation of a system that provides substantially improved signal-to-noise ratio (SNR) and speech recognition accuracy to a moving robotic platform for use in human-robot interaction (HRI) applications in static and dynamic contexts. This study focuses on training deep learning-based beamformers using acoustic model-based multi-style training with measured room impulse responses (RIRs). The results show that this approach outperforms training with simulated RIRs or matched measured RIRs, especially in dynamic conditions involving robot motion. The findings suggest that training with a broad range of measured RIRs is sufficient for effective HRI in various environments, making additional data recording or augmentation unnecessary. This research demonstrates that deep learning-based beamforming can significantly improve HRI performance, particularly in challenging acoustic environments, surpassing traditional beamforming methods.

Fingerprint Based Codebook for RIS Passive Beamforming Training

In this article, we propose a new RIS passive beamforming scheme in two main stages. First, a fingerprint-based codebook (FP-CB) design phase occurs, where the area of interest is divided into a number of points and the optimal reflection patterns (RPs) corresponding to these points are determined and stored alongside the coordinates of these points in the codebook database (DB). Second, there is the searching and learning online stage, in which, based on the receiver (RX) and FP points’ locations, the system determines a group of candidate RPs. Then, it just searches through them instead of examining the entire CB RPs to select the best RP that can be used for configuring RIS during the data transmission period. The proposed mechanism proves that designing a positioning information-based CB can highly reduce the system overhead computational complexity and enhance performance comparable to the conventional CB-based scheme and the full channel estimation (CE)-based scheme. For example, selecting only 10 candidate RPs from the FP-CB can obtain a better effective achievable rate than a CE-based scheme in a rapidly changing channel.

Performance Analysis and Optimization of Network-Assisted Full-Duplex Systems Under Low-Resolution ADCs

Network-assisted full-duplex (NAFD) distributed massive multiple-input–multiple-output (M-MIMO) enables the in-band full-duplex with existing half-duplex devices at the network level, which exceptionally improves spectral efficiency. This article analyzes the impact of low-resolution analog-to-digital converters (ADCs) on NAFD distributed M-MIMO and designs an efficient bit allocation algorithm for low-resolution ADCs. The beamforming training mechanism relieves the heavy pilot overhead for channel estimation, which remarkably enhances the system performance by guiding the interference cancellation and coherence detection. Furthermore, closed-form expressions for spectral and energy efficiency with low-resolution ADCs are derived. The multiobjective optimization problem for spectral and energy efficiency is solved by the deep Q network and the nondominated sorting genetic algorithm II. The simulation results corroborate the theoretical derivation and verify the effectiveness of introducing low-resolution ADCs in NAFD distributed M-MIMO systems. Meanwhile, a set of Pareto-optimal solutions for ADC accuracy flexibly provide guidelines for deploying in a practical NAFD distributed M-MIMO system.

Performance analysis of full-duplex densely distributed MIMO with wireless backhaul

In this paper, the spectral efficiency of a full-duplex (FD) densely distributed multi-input multi-output (MIMO) system with wireless backhaul is considered. In-band full-duplex (IBFD) backhauling, in which the backhaul and access transmissions take place on the same spectrum, is exploited for wireless backhauling to enable an efficient spectrum reuse. However, the severe cross-tier interference and cross-link interference reduce the gains of IBFD backhauling. To evaluate the achievable spectral efficiency with imperfect channel state information (CSI), we propose a two-phase channel estimation scheme to estimate the CSI for two wireless links, and the scheme estimates an effective interference CSI between access points (APs) based on beamforming training to perform interference cancelation at APs. Given the estimated CSI, the closed-form expressions of the uplink and downlink achievable rates with maximum ratio transmission beamforming and maximum ratio combining receivers, respectively, are derived with Gamma approximation. Numerical results verify the accuracy of the derived closed-form expressions and the effectiveness of the two-phase channel estimation scheme for interference cancelation. Moreover, compared with half-duplex densely distributed MIMO systems, FD systems with interference cancelation have a better performance.

IEEE 802.11ay enabled integrated mmWave radar detection and wireless communications

Integrated sensing and communication (ISAC) under the same frequency band and hardware has emerged as a key technology in future wireless networks. Meanwhile, the harmonious coexistence between detection and communication modules has also attracted tremendous research interest in both academia and industry, currently. In this work, we propose a novel Integrated detection and communication (IDAC) scheme in the promising IEEE 802.11ay enabled millimeter wave (mmWave) networks. Particularly, we first introduce a mixing personal basic service set control point or access point (PCP/AP) architecture to support the IDAC function. Thereafter, we embed the radar detection into the IEEE 802.11ay beamforming training (BFT) period by utilizing its directional scanning feature. Furthermore, a BFT and energy allocation strategy is derived to balance the performance between detection and communication. Numerical results demonstrate the effectiveness of the proposed scheme.

Budgeted Thompson Sampling for IRS Enabled WiGig Relaying

Intelligent reconfigurable surface (IRS) is a competitive relaying technology to widen the WiGig coverage range, as it offers an effective means of addressing blocking issues. However, selecting the optimal IRS relay for maximum attainable data rate is a time-consuming process, as it requires WiGig beamforming training (BT) to tune the phase shifts (PSs) for WiGig base station (WGBS) and IRS relays. This paper proposes a self-learning-based budgeted Thomson sampling approach for IRS relay probing (BTS-IRS) to address this challenge. The BT time cost of probing the IRS relay is incorporated into the main BTS formula, where both payoff and cost posterior distributions are sampled separately, their ratio is estimated, and the arm/IRS relay with the highest ratio is decided. This enables the IRS relay to be chosen with the lowest BT time cost. Numerical results demonstrate the improved performance of the BTS-IRS relaying technique regarding BT time consumption/cost, spectral efficiency, and attainable data rate when compared to other benchmarks.

3-D Beamforming Training Using Mutually Unbiased Bases for Cell Discovery in mm-Wave Systems

3-D Beamforming Training Using Mutually Unbiased Bases for Cell Discovery in mm-Wave Systems

A deep neural network based MU-MIMO beamforming training protocol for IEEE 802.11ay

IEEE 802.11ay supports a multi-user multiple-input-multiple-output (MU-MIMO) beamforming training (BFT); hence, an access point (AP) can perform data transmissions simultaneously with multiple stations (STAs). During MU-MIMO BFT, the AP sends a significant number of action frames to the STAs to determine appropriate directional antenna patterns. However, if transmit sectors (TSs) used in the transmission of action frames are selected inefficiently, the AP could perform redundant transmissions; this could eventually lead to the poor performance of the MU-MIMO BFT in terms of signaling and latency overhead. To select the appropriate TSs, the AP is required to accurately estimate the link qualities measured at the STAs when certain TSs are used simultaneously to transmit an action frame. Therefore, in this study, we propose a deep neural network (DNN)-based scheme that performs efficient MU-MIMO BFT. It achieves this by accurately estimating link qualities measured at the STAs when an action frame is transmitted through multiple TSs. Moreover, it performs this function without collecting any additional channel information that is not specified in the standard. For performance evaluation, we conducted extensive simulations with realistic mmWave channel and antenna array models in four indoor and outdoor propagation scenarios. The simulation results demonstrate that our scheme transmits fewer action frames and achieves a lower BFT time than existing schemes.

Survey on positioning information assisted mmWave beamforming training

mmWave communication is considered a major player for 5 G and beyond networks due to its wide unlicensed bandwidth. However, it comes with some challenges such as propagation and penetration losses. To overcome these issues, antenna beamforming is adopted. But it is difficult to estimate massive mmWave multiple input multiple output channel for active beamforming, so training using switched antenna arrays with structured codebook is used to perform mmWave beamforming training (BT). The conventional exhaustive search BT scheme wastes a long time and power, especially in pencil beams scenarios, hence, for a better communication, these issues have to be handled. Depending on available out off band information and motivated by the idea that mmWave is a location driven communication network, several mmWave BT schemes had been proposed in literature based on positioning information (PI). In this paper, we will make a survey on those works, clarifying how different positioning services contribute to reduce and relax BT complexity in mmWave communication. In this context, we will divide those studies into two main categories: (1) Straightforward positioning based BT schemes, which use PI for minimizing the searching space of a predefined codebook or reducing the complexity of channel estimation, (2) Positioning-based BT schemes using machine learning approaches, which classified as, first, statistical and probabilistic learning based schemes, secondly, other ML approaches based schemes. Moreover, we will discuss the effect of positioning and orientation errors, existence of obstacles, user mobility and storing information on the performance of BT. In addition, a comparison between all studies, will be presented considering several aspects such as the implementation cost. Finally, challenges facing these schemes will be discussed and several solutions will be suggested as possible future works.

An Efficient Radio Resource Allocation Scheme considering Terminal Mobility in Dense mmWave Cellular Networks

In millimeter wave (mmWave) communication systems, beamforming-enabled directional transmission and network densification are commonly used to reduce high path loss and improve signal coverage quality. The combination of the two approaches will pose a challenge to radio resource allocation, which is especially true when terminals move frequently. The existing works presented some effective solutions for resource allocation in dense mmWave cellular networks, but they assumed that terminals move infrequently. So, these works cannot be directly applied to the dense mmWave cellular networks where terminals move frequently. In this paper, based on the results of the existing beamforming training (BFT) information-aided radio resource allocation algorithm, we propose a relay selection method to select a set of reasonable relays to take over the terminals whose performance deteriorates due to movement, which can ensure that each selected relay is as close as possible to the original performance of the corresponding moved terminal. Then, the resource allocation problem between the Device to Device (D2D) links from the selected relays to the corresponding moved terminals is formulated as a potential game model. By designing the utility function reasonably, the resource allocation results on the D2D links can converge to a Nash equilibrium solution. The simulation results show that the proposed scheme adapts to the scenario with frequent terminal movement, restrains the sharp performance decline caused by terminal movement, and outperforms the existing related algorithms in terms of average energy efficiency and throughput per link.

Cost-Effective MAB Approaches for Reconfigurable Intelligent Surface Aided Millimeter Wave Relaying

Recently, reconfigurable intelligent surface (RIS) relaying is suggested as a talented technology for extending the millimeter wave (mmWave) coverage. However, finding out the best RIS relay maximizing the achievable data rate is a too time-consuming process due to the beamforming training (BT) procedure needed for adjusting the antenna phase shifts (PSs) of both mmWave base station (BS) and the probed RIS relay. Thus, finding out the best RIS relay with the minimum BT time cost seems challenging. In this paper, a cost-effective online learning approach is proposed by means of multi-armed bandit (MAB) hypothesis to address this problem. In this context, two MAB schemes with time-cost efficiency, MAB-CE1, and MAB-CE2, are proposed. In MAB-CE1, the BT time cost of selecting the RIS relay is included in the exploitation term of the MAB algorithm. However, in MAB-CE2, lower and upper confidence bounds (LCB, UCB) values of the expected RISs’ achievable spectral efficiencies are utilized to support the selection of the RIS relay characterized by the minimum BT time cost. Numerical analysis shows the superior performance of the proposed cost-effective MAB schemes for RIS mmWave relaying over other benchmarks in terms of BT time cost and the achievable throughput.

Markov Multi-Beamtracking on 60 GHz Mobile Channel Measurements

Beamforming training refers to the exhaustive scan over which the transmitter and receiver jointly steer their beams along a predefined set of double-directional angles to determine the beam pairs that coincide with the dominant propagation paths of the channel, for spatial multiplexing at millimeter-wave. When mobile, training necessitates a high refresh rate to maintain connectivity and so, to reduce overhead, beamtracking algorithms exploit the spatial-temporal consistency of the channel to localize the scan around the beam pairs determined at a previous time. The algorithms&#x2019; true performance, however, is still unknown since results reported to date are based on oversimplified channel models. In this paper, we propose a novel beamtracking algorithm formulated as a first-order Markov process that supports multiple beam pairs. The algorithm is evaluated through actual channel measurements &#x2013; <i>not a channel model</i> &#x2013; recorded with our high-precision 3D double-directional 60 GHz channel sounder. The measurement campaign, to our knowledge, is unprecedented: with 10, 895 large-scale measurements, spaced 8.8 cm apart on average to emulate continuous motion, over which the mobile receiver traversed a total of 900.2 m. We demonstrate that four beam pairs can be sustained always and that eight pairs can be sustained 57&#x0025; of the time.

Sleeping Contextual/Non-Contextual Thompson Sampling MAB for mmWave D2D Two-Hop Relay Probing

Millimeter wave (mmWave) band, i.e., 30 to 300 GHz, is characterized by short range transmissions and vulnerability to path blockage necessitating the use of relaying. Probing more relays finds out the best relay having the highest spectral efficiency but at the expense of increasing the probing overhead due to excessive beamforming training (BT) causing a decrease in the overall throughput. In this paper, mmWave two-hop relaying will be formulated as a single player multi-armed bandit (MAB) problem enabling one relay probing while maximizing the achievable spectral efficiency. Moreover, the relays could not establish the mmWave link due to blockage for instance will be identified as sleeping relays and eliminated from the rest of the MAB game. Thus, sleeping non contextual MAB (S-MAB) algorithm, namely sleeping Thompson sampling (S-TS) will be proposed to handle the problem. Furthermore, by utilizing the multiband capability of standardized WiGig devices containing both 2.4/5 GHz WiFi and 60 GHz mmWave bands, WiFi information will be used as contexts of the MAB game. Therefore, sleeping contextual MAB (S-CMAB) algorithm, namely S-CTS, will be proposed as well. Numerical and regret analysis ensure the superior performance of the S-CMAB algorithm over the S-MAB counterpart and the existing mmWave relay probing solutions accompanied with high convergence rates.

A Novel Channel Model and Optimal Beam Tracking Schemes for Mobile Millimeter-Wave Massive MIMO Communications

A novel channel model has been proposed for mobile millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) communications to evaluate the effect of end-user mobility. In this model the variance of number of clusters and number of rays generated from each cluster is taken into account that is novel and different from widely used channel models. Two optimum codebook based beam-tracking schemes-multi-objective joint optimization codebook (MJOC) and linear hybrid combiner (LHC)- have been proposed for the novel channel model and their performance for spectral efficiency (SE) is presented. Performance for the two most commonly used channel state information (CSI) estimation approaches is investigated. Finally, the relationship between the beamforming training blocks and optimal beam tracking scheme is presented.

Two-Hop Relay Probing in WiGig Device-to-Device Networks Using Sleeping Contextual Bandits

Millimeter wave (mmWave) relaying has been introduced recently as a solution to extend the coverage of mmWave communication systems and to deal with the blockage problem as well. In order for the relay probing process to identify the most suitable relays, we should maintain an intelligent trade-off between the number of probed relays and the overhead due to beamforming training (BT). This letter leverages an online learning tool, namely sleeping contextual multi-armed bandits (S-CMAB), to effectively address this problem. Thanks to the multi-band capability of WiGig devices that supports both mmWave and WiFi, the characteristics of the WiFi signal centered at 5.25 GHz are used as contexts for the candidate WiGig relays operating at 60 GHz. Moreover, the sleeping relays that are unable to construct a WiGig link, due to blockages for instance, could be identified during the online learning process and accordingly excluded. Extensive simulations prove that the proposed S-CMAB approach integrated with the proposed sleeping linear upper confidence bound (S-LinUCB) algorithm outperform the legacy approaches and the context-free UCB algorithm in terms of both the average throughput and energy efficiency.

EFFICIENT mmWAVE BEAM TRACKING FOR 5G AND BEYOND WIRELESS SYSTEMS

Recently, 60 GHz wireless networks have drawn much attention due to availability of huge bandwidth around 60 GHz frequency band which has ability to support very high data rate, i.e., up to 6.7 Gbps. This is also called millimetre wave (mmWave) networking which paves the way for realization of Gigabit WLANs. However, heavy attenuation at 60 GHz frequency band limits the range of transmitted signal up to few meters. In order to extend the range of 60 GHz signal, use of directional antennas have been suggested as the most viable solution. Directional antennas use array of antennas to beamform in a particular direction. The difficulty arises when a user is moving. It is a challenging task to track the movement of a user and thus direct the beam in intended direction. According to the IEEE802.11 ad standard, each deployed mmWave AP in the target environment performs an exhaustive beam forming training (BT) to track the moving user to maintain the link. In this paper, we propose a mmWave user tracking scheme in outdoor environment based on the previous user equipment (UE) context information’s. Where, the preceding beam ID directions used to predict a group of mmWave Tx beams to search on them to find out the best beam ID for the next UE location. Simulations show that the proposed scheme highly reduces BT complexity comparable to conventional scheme with conversing almost same performance.

Design and Implementation for Deep Learning Based Adjustable Beamforming Training for Millimeter Wave Communication Systems

Millimeter wave (mmWave) provides extremely high throughput owing to their high bandwidth utilization over higher frequencies. To compensate for the severe loss and attenuation, beamforming training is used to determine the optimal beam direction and thereby improve directional transmission power. However, the training overhead will be significantly increased with narrower beams, particularly under conventional exhaustive schemes. Therefore, we propose a learning-based adjustable beam number training (LABNT) scheme to intelligently and flexibly select beam training candidates considering different user mobility and hybrid line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios. In LABNT, several deep learning networks are parallelly constructed and connected to a reinforcement learning network to determine training candidates dynamically based on performance rewards. A novel enhanced feature selection method, that is, uniformly distributed mutual information, is developed based on the correlation of historical results to select the beam inputs for each deep learning network. In simulations, the proposed LABNT outperforms other existing schemes in terms of beam alignment accuracy, training latency, and system throughput. Moreover, following the IEEE 802.11ad/ay protocols, we implement realistic mmWave beamforming training on a programmable hardware platform with integrated 60 GHz wireless-gigabit (WiGig) transceiver devices. The experimental results on the WiGig platform demonstrate that the proposed LABNT scheme can achieve real-time performance with approximately Gbps transmission throughput and millisecond-level training overhead.

On Assessing the Performance of IEEE 802.11ad Beamforming Training

In the race for Gigabit wireless links, IEEE introduced 802.11ad, an amendment aimed at delivering Gbps capacities in a WLAN setting by leveraging the 60 GHz band. The key innovation of the standard is its beamforming training protocol. Executed periodically at the beginning of every beacon interval, it enables the formation of directional links. To address contention during the uplink part of beamforming training, 802.11ad introduced A-BFT (Association BeamForming Training), an Aloha-inspired, two-level backoff race. While central to the functionality of 802.11ad networks, the performance of A-BFT, however, remains poorly understood. In this article, we propose an analytical finite-population model for evaluating the performance of IEEE 802.11ad A-BFT under two channel models: loss-free, and a channel introducing a constant bit error rate. After using an open-source simulator to demonstrate its accuracy, we use our model to assess the performance of A-BFT. We find that a counter-intuitive, quit-easily/be-lazy approach by the stations leads to the best overall beamforming training performance.

Joint Radio Resource Allocation for Decoupled Control and Data Planes in Densely Deployed Coordinated WLANs

While Wireless Local Area Network (WLAN) gains growing popularity during the last two decades, it faces several new challenges in meeting the requirements of emerging applications due to spectrum shortage, complicated network management and inefficient radio resource allocation (RRA) in densely deployed scenarios. The operating band has evolved from microwave band (a.k.a. sub-6 GHz) to millimeter-wave band (mmWave for short) or even multi-band to provide higher data rates. However, the existing distributed WLAN architecture works in different bands separately and doesn't support the coordination among access points (APs), making it difficult for efficient network management and improved quality of service (QoS) provisioning. In overcoming these challenges, a centralized control architecture for WLAN with decoupled control and data planes can be used to orchestrate the RRA within the whole network while achieving improved communication performance. Therefore, a new control/data plane decoupled WLAN architecture is designed in this paper to realize efficient network management and optimized RRA by separating the control plane and data plane into sub-6 GHz and mmWave respectively. Then, we propose the sub-6 GHz assisted mmWave beamforming protocol, which can significantly reduce the overhead of beamforming training (BFT). Last, we investigate joint RRA for the control/data plane decoupled WLAN since the control plane affects the RRA in the data plane. Performance analysis and simulation show that the joint RRA mechanism in the control/data plane decoupled network can significantly improve the number of successfully scheduled users and the sum-rate compared with the traditional RRA.

Efficient Beamforming Training and Channel Estimation for Millimeter Wave OFDM Systems

We study the problem of downlink beamforming training and channel estimation for millimeter wave (mmWave) OFDM systems, where a hybrid analog and digital beamforming structure is employed at the transmitter (i.e., base station) and an omni-directional antenna or an antenna array is used at the receiver (i.e., user). To efficiently probe the channel, we form multiple directional beams simultaneously at the transmitter and steer them towards different directions. The objective is to devise the beam training sequence and develop an efficient algorithm to estimate the channel. By exploiting the sparse scattering nature of mmWave channels, the above problem is formulated as one of sparse encoding and signal recovery, which involves finding a sparse sensing matrix to compress the sparse channel and an efficient channel estimation algorithm to recover the sparse channel from compressive measurements. In this article, we propose a sparse bipartite graph code-based algorithm, where a set of bipartite graphs are employed to encode the sparse channel and a simple decoding procedure that relies on the presence of a No-Multiton-graph (NM-graph) is used to reconstruct the sparse channel. Theoretical analysis shows that our proposed method can help achieve a substantial training overhead reduction. Simulations are provided to show the effectiveness of the proposed algorithm and its performance advantage over compressed sensing-based methods.