Feedback Bits Research Articles

Self-adaptive asynchronous federated optimizer with adversarial sharpness-aware minimization

The past years have witnessed the success of a distributed learning system called Federated Learning (FL). Recently, asynchronous FL (AFL) has demonstrated its potential in concurrency compared to mainstream synchronous FL. However, the inherent systematic and statistical heterogeneity has presented several impediments to AFL: On the client side, the discrepancies in trips and local model drift impede global performance enhancement; On the server side, dynamic communication leads to significant fluctuations in gradient arrival time, while asynchronous arrival gradients with ambiguous value are not fully leveraged. In this paper, we propose an adaptive AFL framework, ARDAGH, which systematically addresses the aforementioned challenges: Firstly, to address the discrepancies in client trips, ARDAGH ensures their convergence by incorporating only 1-bit feedback information into the downlink. Secondly, to counter the drift of clients, ARDAGH generalizes the local models by employing our novel adversarial sharpness-aware minimization, which does not necessitate reliance on additional global variables. Thirdly, in the face of gradient latency issues, ARDAGH employs a communication-aware dropout strategy to adaptively compress gradients to ensure similar transmission times. Finally, to fully unleash the potential of each gradient, we establish a consistent optimal direction by conceptualizing the aggregation as an optimizer with successive momentum. In light of the comprehensive solution provided by ARDAGH, an algorithm named FedAMO is derived, and its superiority is confirmed by experimental results obtained under challenging prototype and simulation settings. Particularly in typical sentiment analysis tasks, FedAMO demonstrates an improvement of up to 5.351% with a 20.056-fold acceleration compared to conventional asynchronous methods.

Design of a Crossroad Traffic Light System

Nowadays, the situation of traffic jams is gradually increasing, and the demand for intelligent traffic lights is increasing as well. This paper designs a crossroad traffic light based on sequential logic using NI Multisim 14.0 virtual simulation software. The design includes: using 555 timer to build a second pulse oscillator circuit which outputs clock signal. Using 74LS169 counting chip to build timing circuit. Set up state control with 74LS74D trigger. Using nixie tubes and 74LS47 decoder chips to build the timing display circuit. Finally, logic gate and LED lights are used to build the display circuit of traffic lights. Next, how to design intelligent traffic lights is discussed based on this design combined with image monitoring technology. The feedback bit of image processing technology is added to the designed circuit, so that the circuit can change the green light duration with the traffic flow, and the designed circuits are also becoming smarter.

A bit rate condition for feedback stabilization of uncertain switched systems with external disturbances based on event triggering

AbstractThis paper studies the input‐to‐state stability of an uncertain switched linear system with external disturbances, whose feedback loop is closed over a digital communication network. In the network, only a finite bit rate can be provided to transmit the feedback signal and unknown network delay always exists. By using reachable set approximation and propagation methods, it can well handle with the effects of model uncertainty, external disturbances and network delay on the stability of the system. To save network resources, event‐triggered sampling is taken here. Under the event‐triggered sampling strategies, a new encoder is proposed, which consumes less feedback bits than the conventional time‐triggered encoders. It is proven that even in the existence of model uncertainty, external disturbances and network delay, the proposed event‐triggered sampling strategies require a lower feedback bit rate to stabilize the switched system than conventional periodic sampling methods.

Performance analysis of insertion loss incorporated hybrid precoding for massive MIMO

<abstract> <p>Due to an increase in the number of users and a high demand for high data rates, researchers have resorted to boosting the capacity and spectral efficiency of the next-generation wireless communication. With a limited RF chain, hybrid analog digital precoding is an appealing alternative. The hybrid precoding approach divides the beamforming process into an analog beamforming network and a digital beamforming network of a reduced size. As a result, numerous hybrid beamforming networks have been proposed. The practical effects of signal processing in the RF domain, such as the additional power loss incurred by an analog beamforming network, were not taken into account. The effectiveness of hybrid precoding structures for massive MIMO systems was examined in this study. In particular, a viable hardware network realization with insertion loss was developed. Investigating the spectral and energy efficiency of two popular hybrid precoding structures, the fully connected structure, and the subconnected structure, it was found that in a massive MIMO, the subconnected structure always performed better than the fully connected structure. Characterizing the effect of quantized analog precoding, it was shown that the subconnected structure was able to achieve better performance with fewer feedback bits than the fully connected structure.</p> </abstract>

Channel Customization for Limited Feedback in RIS-Assisted FDD Systems

Reconfigurable intelligent surfaces (RISs) represent a pioneering technology to realize smart electromagnetic environments by reshaping the wireless channel. Jointly designing the transceiver and RIS relies on the channel state information (CSI), whose feedback has not been investigated in multi-RIS-assisted frequency division duplexing systems. In this study, the limited feedback of the RIS-assisted wireless channel is examined by capitalizing on the ability of the RIS in channel customization. By configuring the phase shifters of the surfaces using statistical CSI, we customize a sparse channel in rich-scattering environments, which significantly reduces the feedback overhead in designing the transceiver and RISs. Since the channel is customized in terms of singular value decomposition (SVD) with full-rank, the optimal SVD transceiver can be approached without a matrix decomposition and feeding back the complete channel parameters. The theoretical spectral efficiency (SE) loss of the proposed transceiver and RIS design is derived by considering the limited CSI quantization. To minimize the SE loss, a bit partitioning algorithm that splits the limited number of bits to quantize the CSI is developed. Extensive numerical results show that the channel customization-based transceiver with reduced CSI can achieve satisfactory performance compared with the optimal transceiver with full CSI. Given the limited number of feedback bits, the bit partitioning algorithm can minimize the SE loss by adaptively allocating bits to quantize the channel parameters.

Machine Learning-Based CSI Feedback With Variable Length in FDD Massive MIMO

To fully unlock the benefits of multiple-input multiple-output (MIMO) networks, downlink channel state information (CSI) is required at the base station (BS). In frequency division duplex (FDD) systems, the CSI is acquired through a feedback signal from the user equipment (UE). However, this may lead to an important overhead in FDD massive MIMO systems. Focusing on these systems, in this study, we propose a novel strategy to design the CSI feedback. Our strategy allows to optimally design variable length feedback, that is promising compared to fixed feedback since users experience channel matrices differently sparse. Specifically, principal component analysis (PCA) is used to compress the channel into a latent space with adaptive dimensionality. To quantize this compressed channel, the feedback bits are smartly allocated to the latent space dimensions by minimizing the normalized mean squared error (NMSE) distortion. Finally, the quantization codebook is determined with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -means clustering. Numerical simulations show that our strategy improves the zero-forcing beamforming sum rate by 17%, compared to CsiNetPro. The number of model parameters is reduced by 23.4 times, thus causing a significantly smaller offloading overhead. At the same time, PCA is characterized by a lightweight unsupervised training, requiring eight times fewer training samples than CsiNetPro.

Limited Feedback Design Based on Kronecker Product Codebook for Massive MIMO Systems

To realize massive multiple-input multiple-output (MIMO) systems, the uniform planar array (UPA) structure has been adopted to deploy a large number of antennas in a limited area of a radio unit at a base station. Subsequently, the Kronecker product (KP) codebook, consisting of horizontal and vertical-domain codebooks, was introduced to enable efficient quantization and feedback for the channel state information (CSI) under the UPA structure. In this paper, we propose advanced limited feedback schemes based on the KP codebook for massive MIMO systems. First, we propose one feedback bit allocation scheme to maximize the beamforming gain. Eigenvalues of spatial correlation channels are used to determine the numbers of feedback bits for horizontal and vertical-domain CSI, and the numbers of feedback bits to maximize the beamforming gain are derived as a closed-form expression. Moreover, we propose a novel feedback and pilot transmission scheme to reduce both the feedback and pilot overhead. The proposed feedback and pilot transmission scheme utilizes the property that the small-scale fading information can be compressed only to the horizontal-domain CSI when the spatial correlation of the vertical-domain channel is high. Simulation results show that, with the use of the proposed feedback bit allocation, the beamforming gain increases by up to 65% compared with the fixed feedback bit allocation when the horizontal and vertical-domain spatial correlation coefficients are equal to 0.57, and the numbers of horizontal and vertical-domain antennas are 8 and 12, respectively. In addition, it is also shown that the proposed feedback and pilot transmission scheme improves the ergodic rate by up to 48% compared with conventional feedback schemes.

A Codebook Design for FD-MIMO Systems With Multi-Panel Array

In this work, we study codebook designs for full-dimension multiple-input multiple-output (FD-MIMO) systems with a multi-panel array (MPA). We propose novel codebooks which allow precise beam structures for MPA FD-MIMO systems by investigating the physical properties and alignments of the panels. We specifically exploit the characteristic that a group of antennas in a vertical direction exhibit more correlation than those in a horizontal direction. This enables an economical use of feedback bits while constructing finer beams compared to conventional codebooks. The codebook is further improved by dynamically allocating the feedback bits on multiple parts such as beam amplitude and co-phasing coefficients using reinforcement learning. The numerical results confirm the effectiveness of the proposed approach in terms of both performance and computational complexity.

Physical Layer Security in Multi-Antenna Cellular Systems: Joint Optimization of Feedback Rate and Power Allocation

This paper comprehensively studies the physical layer security in frequency division duplex multi-antenna cellular systems, where the multi-antenna base stations (BSs), legitimate users (LUs), and eavesdroppers are all randomly located. Each BS employs artificial-noise (AN)-aided multi-user linear beamforming with limited channel state information feedback. Based on the stochastic geometry theory, we first derive an analytical expression of a lower bound on the ergodic secrecy rate (ESR) of the typical LU without assuming asymptotes for any system parameter. We then develop a tight closed-form approximation on the optimal number of feedback bits to maximize a lower bound on the per-user net ESR, which takes into consideration the cost of uplink spectral efficiency for limited feedback. Moreover, the power allocation coefficient between message-bearing signals and AN can be optimized by using a bisection search method. Our main finding is that, the optimum number of feedback bits scales linearly with the path-loss exponent and the number of antennas, and scales logarithmically with the channel coherence time. The derived analytical results can also provide system-level insights into the ESR performance of the multi-antenna random cellular networks with limited feedback and the optimal system design. Numerical results are also presented to verify the obtained results.

Contextual-Learning-Based Waveform Scheduling for Wireless Power Transfer With Limited Feedback

In this article, we study the waveform scheduling problem for a wireless power transfer (WPT) system consisting of a power beacon (PB) and multiple energy-harvesting-empowered Internet of Things (EH-IoT) devices. In each time slot, each device requests power to the PB if it needs power, and the PB transmits a WPT signal for which the waveform is designed based on the harvested power satisfaction rate of the power-requesting devices. Under this setup, we formulate an optimization problem that maximizes the average number of EH-IoT devices whose power requests are satisfied. We first solve this problem, assuming that the perfect channel state information (CSI) of all devices is known at the PB. Since the problem is difficult to solve even with perfect CSI, we transform it into a more tractable problem via proper approximations and propose an efficient algorithm to solve it. Next, to tackle the issue that it is practically difficult for the PB to acquire the perfect CSI of each device, we propose a contextual learning-based WPT waveform scheduling algorithm, requiring only 1-bit feedback from each device at one time. Numerical results show that our proposed waveform scheduling algorithm provides a higher satisfaction rate than existing algorithms under perfect CSI, and that with limited CSI feedback achieves performance close to the case with perfect CSI.

PCQNet: A Trainable Feedback Scheme of Precoder for the Uplink Multi-User MIMO Systems.

Multi-user multiple-input multiple-output (MU-MIMO) technology can significantly improve the spectral and energy efficiencies of wireless networks. In the uplink MU-MIMO systems, the optimal precoder design at the base station utilizes the Lagrange multipliers method and the centralized iterative algorithm to minimize the mean squared error (MSE) of all users under the power constraint. The precoding matrices need to be fed back to the user equipment to explore the potential benefits of the joint transceiver design. We propose a CNN-based compression network named PCQNet to minimize the feedback overhead. We first illustrate the effect of the trainable compression ratios and feedback bits on the MSE between the original precoding matrices and the recovered ones. We then evaluate the block error rates as the performance measure of the centralized implementation with an optimal minimum mean-squared error (MMSE) transceiver. Numerical results show that the proposed PCQNet achieves near-optimal performance compared with other quantized feedback schemes and significantly reduces the feedback overhead with negligible performance degradation.

Jammer-Assisted Secure Precoding and Feedback Design for MIMO IoT Networks

Great concerns on the Internet of Things (IoT) security are raised as IoT becomes an emerging paradigm to achieve ubiquitous connectivity. This article studies low-complexity secure transceiver and feedback design with the assistance of a jammer for physical-layer security in multiantenna IoT systems. We consider the general setting where a legitimate multiantenna controller broadcasts confidential messages to multiple multiantenna IoT devices in the presence of a passive external multiantenna eavesdropper. Moreover, there is only quantized downlink channel state information (CSI) at the controller and the jammer through feedback channels. We introduce several secure transceivers for different system setups, all of which employ block-diagonal precoding at the controller and null-space beamforming at the jammer but are with the different receivers at each IoT device. Considering the practical setup of IoT and for the tractability of analysis, we study the secrecy performance of the transceiver with an arbitrarily selected receive matrix independent of the channels of all devices. We derive an approximate lower bound on the ergodic secrecy rate (ESR) of each device <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">without</i> assuming any asymptotes for system parameters. The obtained result can also be viewed as a lower bound on the ESR performance of any other transceivers. We also optimize this bound to find an adaptive feedback bit allocation to the two feedback channels of each legitimate device. Numerical results are shown to illustrate the obtained analytical ESR lower bound, the feedback bit allocation algorithm, and significant ESR performance gain that results from the proposed feedback bit allocation.

Spherical-Cap Approximation of Vector Quantization for Quantization-Based Combining in MIMO Broadcast Channels with Limited Feedback

The spherical-cap approximation of vector quantization (SCVQ) is an analytical model used for the mathematical analysis of multiple-input multiple-output (MIMO) systems with limited feedback. SCVQ closely emulates the distribution of the quantization error induced by the finite-rate quantization of a channel using a simple and analytically tractable approach. However, the conventional SCVQ model is not applicable when antenna-combining schemes such as quantization-based combining (QBC) are considered. Because QBC is an effective antenna-combining method that minimizes channel quantization errors, it can be adopted for various practical MIMO broadcast systems. Nevertheless, evaluating the performance of QBC-based MIMO systems with an explicit codebook can be extremely difficult, depending on the system complexity. To resolve this, this study generalizes the conventional SCVQ to be compatible with the QBC. The proposed generalized version of the SCVQ effectively emulates the quantization error obtained using QBC, while enabling a simple simulation independent of the number of feedback bits and mathematically tractable analysis. We validate the effectiveness of the proposed model by presenting a wireless communication application based on a dense cellular network.

Adaptive Bit Partitioning for Reconfigurable Intelligent Surface Assisted FDD Systems With Limited Feedback

In frequency division duplexing systems, the base station (BS) acquires downlink channel state information (CSI) via channel feedback, which has not been adequately investigated in the presence of RIS. In this study, we examine the limited channel feedback scheme by proposing a novel cascaded codebook and an adaptive bit partitioning strategy. The RIS segments the channel between the BS and mobile station into two sub-channels, each with line-of-sight (LoS) and non-LoS (NLoS) paths. To quantize the path gains, the cascaded codebook is proposed to be synthesized by two sub-codebooks whose codeword is cascaded by LoS and NLoS components. This enables the proposed cascaded codebook to cater the different distributions of LoS and NLoS path gains by flexibly using different feedback bits to design the codeword structure. On the basis of the proposed cascaded codebook, we derive an upper bound on ergodic rate loss with maximum ratio transmission and show that the rate loss can be cut down by optimizing the feedback bit allocation during codebook generation. To minimize the upper bound, we propose a bit partitioning strategy that is adaptive to diverse environment and system parameters. Extensive simulations are presented to show the superiority and robustness of the cascaded codebook and the efficiency of the adaptive bit partitioning scheme.

Spatial Interference Alignment with Limited Precoding Matrix Feedback in a Wireless Multi-User Interference Channel for Smart Grids

Cellular communication provides an efficient, flexible, long-lived, and reliable communication technology for smart grids to improve the automated analysis, demand response, adoptive control, and coordination between the generator and consumers. With the expansion of wireless networks and the increase of access devices, interference has become a major problem that limits the performance of cellular wireless communication systems for smart grids. Spatial interference alignment (IA) is an effective method to eliminate interference and improve the capacity of wireless communication networks. This paper provides the sufficient conditions of spatial interference alignment operating with limited precoding matrix feedback for a K-user MIMO interference channel. Each receiver feeds the matrix index of the transmitting precoder back to the corresponding transmitter through an interference-free and error-free link. We calculated the number of feedback bits required to achieve the maximum theoretical multiplexing gain for the spatial interference alignment schemes considered and demonstrate the feasibility of spatial interference alignment under the limited feedback constraint investigated. It is shown that in order to maintain the same spatial multiplexing gain as that of the idealized scheme relying on perfect channel state information, the number of feedback bits per receiver scales as Nd≥di(M−di)log2SNR, where M and di denote the number of transmit (receive) antennas and the number of data steams for user i. Finally, the analytical results were verified by simulations for practical interference alignment schemes relying on limited precoding matrix feedback indices.

A Coordinated Direct AF/DF Relay-Aided NOMA Framework for Low Outage

This paper investigates the performance of a new framework for low-outage downlink non-orthogonal multiple access (NOMA) using a coordinated direct and relay transmission (CDRT) scheme with direct links to both the near-user (NU) and the far-user (FU). Both amplify-and-forward and decode-and-forward relays are considered. In this framework, the NU combines the signals from base-station and relay at each stage of the successive interference cancellation (SIC) to attain good outage performance. For both NU and FU, the expressions for outage probability and throughput are derived in closed form. We also derive the high-SNR expressions for the outage probability to demonstrate that with the proposed framework, both users harness a diversity of two without feedback bits (this is the only framework to achieve this). We demonstrate that the choice of power allocation coefficient and target symbol rates is crucial to maximizing the NU throughput while ensuring a desired target FU throughput. We demonstrate that CDRT with the proposed framework outperforms known schemes in terms of outage probability, sum throughput, and energy efficiency. Moreover, we also show that optimal rate selection is important to maximize the energy efficiency. Monte Carlo simulations validate the accuracy of the derived analytical expressions.

Artificial-Noise-Aided Coordinated Secure Transmission Design in Multi-Cell Multi-Antenna Networks With Limited Feedback

In this paper, we consider secure communications in a multi-cell downlink multi-antenna system, where each multi-antenna base station (BS) sends confidential messages to one intended legitimate user (LU) with a passive multi-antenna eavesdropper trying to intercept the messages in this cell. To enhance the secure communications of the cell-edge users, the multiple BSs employ coordinated beamforming with the aid of artificial-noise (AN) beamforming. We conduct a mathematically rigorous secrecy performance analysis of the considered system and obtain new accurate closed-form expressions of a lower bound on the ergodic rate of each LU and an upper bound of the ergodic rate of each eavesdropper without assuming asymptotes for any system parameter (except for the “worst-case” scenario with zero noise power). Then, a lower bound on the ergodic secrecy rate (ESR) of each LU follows. Based on the derived analytical results, we propose a low-complexity algorithm to optimize the channel sate information (CSI) feedback bits allocation for the channels of target signal link and inter-cell interference links. Furthermore, we develop explicit sufficient conditions on the set of system parameters under which there are at least two BSs to coordinated secure transmission. Through both theoretical analysis and numerical results, we can verify that the obtained analytical lower bound on ESR of each LU as a function of the set of power allocation coefficients is generally <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">neither convex nor concave</i> . Thus, we propose to employ some numerical method to find a sub-optimal power allocation solution. Finally, numerical results are also provided to validate our theoretical results and the proposed feedback bits and power allocation methods.

Hybrid FSO-RF Turbulent Link Performance under Misalignment Aperture Error

A novel hybrid Free Space Optics (FSO)/Radio Frequency (RF) model is proposed which is based on the single decision threshold feedback bit switching technique. The FSO and proposed hybrid FSO-RF links performance are investigated under strong turbulence and misalignment aperture error in terms of the average symbol error rate (ASER) and outage probability. The FSO-RF link is preferred in this work over the normal FSO link due to ease in switching among the link established with the help of decision threshold during failure of FSO link. In this proposed hybrid model, Gamma-Gamma distribution is considered as free-space optical turbulence channel and Nakagami-m model is for RF link. The analytical expressions for the outage probability and ASER have been derived for various Intensity Modulation - M-phase shift keying (PSK) schemes. A performance comparative study is reported for FSO link and hybrid FSO-RF link. The obtained result indicates the enhanced system performance with the hybrid model whereas the obtained results are validated through MC simulations and results are found in good agreement.

Two-Timescale End-to-End Learning for Channel Acquisition and Hybrid Precoding

In this paper, we propose an end-to-end deep learning-based joint transceiver design algorithm for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, which consists of deep neural network (DNN)-aided pilot training, channel feedback, and hybrid analog-digital (HAD) precoding. Specifically, we develop a DNN architecture that maps the received pilots into feedback bits at the receiver, and then further maps the feedback bits into the hybrid precoder at the transmitter. To reduce the signaling overhead and channel state information (CSI) mismatch caused by the transmission delay, a two-timescale DNN composed of a long-term DNN and a short-term DNN is developed. The analog precoders are designed by the long-term DNN based on the CSI statistics and updated once in a frame consisting of a number of time slots. In contrast, the digital precoders are optimized by the short-term DNN at each time slot based on the estimated low-dimensional equivalent CSI matrices. A two-timescale training method is also developed for the proposed DNN with a binary layer. We then analyze the generalization ability and signaling overhead for the proposed DNN based algorithm. Simulation results show that our proposed technique significantly outperforms conventional schemes in terms of bit-error rate performance with reduced signaling overhead and shorter pilot sequences.

CAnet: Uplink-Aided Downlink Channel Acquisition in FDD Massive MIMO Using Deep Learning

In frequency-division duplexing systems, the downlink channel state information (CSI) acquisition scheme leads to high training and feedback overhead. In this work, we propose an uplink-aided downlink channel acquisition framework using deep learning to reduce such overhead. We consider the entire downlink CSI acquisition process, including the downlink pilot design, channel estimation, and feedback. First, we propose an adaptive pilot design module by exploiting the correlation in magnitude among bidirectional channels in the angular domain to improve channel estimation. Second, to avoid the bit allocation problem during the feedback module, we concatenate the complex channel and embed the uplink channel magnitude to the channel reconstruction at the base station. Finally, we combine the two modules and compare two popular uplink-aided downlink channel acquisition frameworks. One framework estimates and subsequently feeds back the channel at the user equipment. In the other framework, the user equipment directly feeds back the received pilot signals to the base station. Results reveal that with the help of the uplink channel, directly feeding back pilot signals can save approximately 20% of feedback bits. This work thus provides a guideline for future research.