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Yong Zeng

Publications and source records attributed to Yong Zeng.

At least 109 records · Page 6Linked to original sources
arXiv2024

A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G

Sixth-generation (6G) mobile communication networks are expected to have dense infrastructures, large antenna size, wide bandwidth, cost-effective hardware, diversified positioning methods, and enhanced intelligence. Such trends bring both new challenges and opportunities for the practical design of 6G. On one hand, acquiring channel state information (CSI) in real time for all wireless links becomes quite challenging in 6G. On the other hand, there would be numerous data sources in 6G containing high-quality location-tagged channel data, e.g., the estimated channels or beams between base station (BS) and user equipment (UE), making it possible to better learn the local wireless environment. By exploiting this new opportunity and for tackling the CSI acquisition challenge, there is a promising paradigm shift from the conventional environment-unaware communications to the new environment-aware communications based on the novel approach of channel knowledge map (CKM). This article aims to provide a comprehensive overview on environment-aware communications enabled by CKM to fully harness its benefits for 6G. First, the basic concept of CKM is presented, followed by the comparison of CKM with various existing channel inference techniques. Next, the main techniques for CKM construction are discussed, including both environment model-free and environment model-assisted approaches. Furthermore, a general framework is presented for the utilization of CKM to achieve environment-aware communications, followed by some typical CKM-aided communication scenarios. Finally, important open problems in CKM research are highlighted and potential solutions are discussed to inspire future work.

cs.IT↗
arXiv2024

Spatial Channel State Information Prediction with Generative AI: Towards Holographic Communication and Digital Radio Twin

As 5G technology becomes increasingly established, the anticipation for 6G is growing, which promises to deliver faster and more reliable wireless connections via cutting-edge radio technologies. However, efficient management method of the large-scale antenna arrays deployed by those radio technologies is crucial. Traditional management methods are mainly reactive, usually based on feedback from users to adapt to the dynamic wireless channel. However, a more promising approach lies in the prediction of spatial channel state information (spatial-CSI), which is an all-inclusive channel characterization and consists of all the feasible line-of-sight (LoS) and non-line-of-sight (NLoS) paths between the transmitter (Tx) and receiver (Rx), with the three-dimension (3D) trajectory, attenuation, phase shift, delay, and polarization of each path. Advances in hardware and neural networks make it possible to predict such spatial-CSI using precise environmental information, and further look into the possibility of holographic communication, which implies complete control over every aspect of the radio waves emitted. Based on the integration of holographic communication and digital twin, we proposed a new framework, digital radio twin, which takes advantages from both the digital world and deterministic control over radio waves, supporting a wide range of high-level applications. As a preliminary attempt towards this visionary direction, in this paper, we explore the use of generative artificial intelligence (AI) to pinpoint the valid paths in a given environment, demonstrating promising results, and highlighting the potential of this approach in driving forward the evolution of 6G wireless communication technologies.

cs.IT↗
arXiv2024

Integrated Sensing, Communication, and Powering (ISCAP): Towards Multi-functional 6G Wireless Networks

This article presents a novel multi-functional system for a sixth-generation (6G) wireless network with integrated sensing, communication, and powering (ISCAP), which unifies integrated sensing and communication (ISAC) and wireless information and power transfer (WIPT) techniques. The multi-functional ISCAP network promises to enhance resource utilization efficiency, reduce network costs, and improve overall performance through versatile operational modes. Specifically, a multi-functional base station (BS) can enable multi-functional transmission, by exploiting the same radio signals to perform target/environment sensing, wireless communication, and wireless power transfer (WPT), simultaneously. Besides, the three functions can be intelligently coordinated to pursue mutual benefits,i.e., wireless sensing can be leveraged to enable light-training or even training-free WIPT by providing side-channel information, and the BS can utilize WPT to wirelessly charge low-power devices for ensuring sustainable ISAC. Furthermore, multiple multi-functional BSs can cooperate in both transmission and reception phases for efficient interference management, multi-static sensing, and distributed energy beamforming. For these operational modes, we discuss the technical challenges and potential solutions, particularly focusing on the fundamental performance tradeoff limits, transmission protocol design, as well as waveform and beamforming optimization. Finally, interesting research directions are identified.

eess.SP↗
arXiv2023

How Much Data is Needed for Channel Knowledge Map Construction?

Channel knowledge map (CKM) has been recently proposed to enable environment-aware communications by utilizing historical or simulation generated wireless channel data. This paper studies the construction of one particular type of CKM, namely channel gain map (CGM), by using a finite number of measurements or simulation-generated data, with model-based spatial channel prediction. We try to answer the following question: How much data is sufficient for CKM construction? To this end, we first derive the average mean square error (AMSE) of the channel gain prediction as a function of the sample density of data collection for offline CGM construction, as well as the number of data points used for online spatial channel gain prediction. To model the spatial variation of the wireless environment even within each cell, we divide the CGM into subregions and estimate the channel parameters from the local data within each subregion. The parameter estimation error and the channel prediction error based on estimated channel parameters are derived as functions of the number of data points within the subregion. The analytical results provide useful guide for CGM construction and utilization by determining the required spatial sample density for offline data collection and number of data points to be used for online channel prediction, so that the desired level of channel prediction accuracy is guaranteed.

cs.IT↗
arXiv2023

Theoretical Analysis of Impact of Delayed Updates on Decentralized Federated Learning

Decentralized Federated learning is a distributed edge intelligence framework by exchanging parameter updates instead of training data among participators, in order to retrain or fine-tune deep learning models for mobile intelligent applications. Considering the various topologies of edge networks in mobile internet, the impact of transmission delay of updates during model training is non-negligible for data-intensive intelligent applications on mobile devices, e.g., intelligent medical services, automated driving vehicles, etc.. To address this problem, we analyze the impact of delayed updates for decentralized federated learning, and provide a theoretical bound for these updates to achieve model convergence. Within the theoretical bound of updating period, the latest versions for the delayed updates are reused to continue aggregation, in case the model parameters from a specific neighbor are not collected or updated in time.

cs.DC↗
arXiv2023

Integrated Sensing and Channel Estimation by Exploiting Dual Timescales for Delay-Doppler Alignment Modulation

For integrated sensing and communication (ISAC) systems, the channel information essential for communication and sensing tasks fluctuates across different timescales. Specifically, wireless sensing primarily focuses on acquiring path state information (PSI) (e.g., delay, angle, and Doppler) of individual multi-path components to sense the environment, which usually evolves much more slowly than the composite channel state information (CSI) required for communications. Typically, the CSI is approximately unchanged during the channel coherence time, which characterizes the statistical properties of wireless communication channels. However, this concept is less appropriate for describing that for wireless sensing. To this end, in this paper, we introduce a new timescale to study the variation of the PSI from a channel geometric perspective, termed path invariant time, during which the PSI largely remains constant. Our analysis indicates that the path invariant time considerably exceeds the channel coherence time. Thus, capitalizing on these dual timescales of the wireless channel, in this paper, we propose a novel ISAC framework exploiting the recently proposed delay-Doppler alignment modulation (DDAM) technique. Different from most existing studies on DDAM that assume the availability of perfect PSI, in this work, we propose a novel algorithm, termed as adaptive simultaneously orthogonal matching pursuit with support refinement (ASOMP-SR), for joint environment sensing and PSI estimation. We also analyze the performance of DDAM with imperfectly sensed PSI.Simulation results unveil that the proposed DDAM-based ISAC can achieve superior spectral efficiency and a reduced peak-to-average power ratio (PAPR) compared to standard orthogonal frequency division multiplexing (OFDM).

cs.IT↗
arXiv2023

Delay-Doppler Alignment Modulation for Spatially Sparse Massive MIMO Communication

Delay alignment modulation (DAM) is an emerging technique for achieving inter-symbol interference (ISI)-free wideband communications using spatial-delay processing, without relying on channel equalization or multi-carrier transmission. However, existing works on DAM only consider multiple-input single-output (MISO) communication systems and assume time-invariant channels. In this paper, by extending DAM to time-variant frequency-selective multiple-input multiple-output (MIMO) channels, we propose a novel technique termed \emph{delay-Doppler alignment modulation} (DDAM). Specifically, by leveraging \emph{delay-Doppler compensation} and \emph{path-based beamforming}, the Doppler effect of each multi-path can be eliminated and all multi-path signal components may reach the receiver concurrently and constructively. We first show that by applying path-based zero-forcing (ZF) precoding and receive combining, DDAM can transform the original time-variant frequency-selective channels into time-invariant ISI-free channels. The necessary and/or sufficient conditions to achieve such a transformation are derived. Then an asymptotic analysis is provided by showing that when the number of base station (BS) antennas is much larger than that of channel paths, DDAM enables time-invariant ISI-free channels with the simple delay-Doppler compensation and path-based maximal-ratio transmission (MRT) beamforming. Furthermore, for the general DDAM design with some tolerable ISI, the path-based transmit precoding and receive combining matrices are optimized to maximize the spectral efficiency. Numerical results are provided to compare the proposed DDAM technique with various benchmarking schemes, including MIMO-orthogonal time frequency space (OTFS), MIMO-orthogonal frequency-division multiplexing (OFDM) without or with carrier frequency offset (CFO) compensation, and beam alignment along the dominant path.

cs.IT↗
arXiv2023

Achievable Rate Region and Path-Based Beamforming for Multi-User Single-Carrier Delay Alignment Modulation

Delay alignment modulation (DAM) is a novel wideband transmission technique for mmWave massive MIMO systems, which exploits the high spatial resolution and multi-path sparsity to mitigate ISI, without relying on channel equalization or multi-carrier transmission. In particular, DAM leverages the delay pre-compensation and path-based beamforming to effectively align the multi-path components, thus achieving the constructive multi-path combination for eliminating the ISI while preserving the multi-path power gain. Different from the existing works only considering single-user DAM, this paper investigates the DAM technique for multi-user mmWave massive MIMO communication. First, we consider the asymptotic regime when the number of antennas Mt at BS is sufficiently large. It is shown that by employing the simple delay pre-compensation and per-path-based MRT beamforming, the single-carrier DAM is able to perfectly eliminate both ISI and IUI. Next, we consider the general scenario with Mt being finite. In this scenario, we characterize the achievable rate region of the multi-user DAM system by finding its Pareto boundary. Specifically, we formulate a rate-profile-constrained sum rate maximization problem by optimizing the per-path-based beamforming. Furthermore, we present three low-complexity per-path-based beamforming strategies based on the MRT, zero-forcing, and regularized zero-forcing principles, respectively, based on which the achievable sum rates are studied. Finally, we provide simulation results to demonstrate the performance of our proposed strategies as compared to two benchmark schemes based on the strongest-path-based beamforming and the prevalent OFDM, respectively. It is shown that DAM achieves higher spectral efficiency and/or lower peak-to-average-ratio, for systems with high spatial resolution and multi-path diversity.

cs.IT↗
arXiv2023

Multi-User Modular XL-MIMO Communications: Near-Field Beam Focusing Pattern and User Grouping

In this paper, we investigate multi-user modular extremely large-scale multiple-input multiple-output (XL-MIMO) communication systems, where modular extremely large-scale uniform linear array (XL-ULA) is deployed at the base station (BS) to serve multiple single-antenna users. By exploiting the unique modular array architecture and considering the potential near-field propagation, we develop sub-array based uniform spherical wave (USW) models for distinct versus common angles of arrival/departure (AoAs/AoDs) with respect to different sub-arrays/modules, respectively. Under such USW models, we analyze the beam focusing patterns at the near-field observation location by using near-field beamforming. The analysis reveals that compared to the conventional XL-MIMO with collocated antenna elements, modular XL-MIMO can provide better spatial resolution by benefiting from its larger array aperture. However, it also incurs undesired grating lobes due to the large inter-module separation. Moreover, it is found that for multi-user modular XL-MIMO communications, the achievable signal-to-interference-plus-noise ratio (SINR) for users may be degraded by the grating lobes of the beam focusing pattern. To address this issue, an efficient user grouping method is proposed for multi-user transmission scheduling, so that users located within the grating lobes of each other are not allocated to the same time-frequency resource block (RB) for their communications. Numerical results are presented to verify the effectiveness of the proposed user grouping method, as well as the superior performance of modular XL-MIMO over its collocated counterpart with densely distributed users.

cs.IT↗
arXiv2023

Single-Carrier Delay Alignment Modulation for Multi-IRS Aided Communication

Delay alignment modulation (DAM) is a promising technology to achieve ISI-free wideband communication, by leveraging delay compensation and path-based beamforming, rather than the conventional channel equalization or multi-carrier transmission. In particular, when there exist a few strong time-dispersive channel paths, DAM can effectively align different propagation delays and achieve their constructive superposition, thus especially appealing for intelligent reflecting surfaces (IRSs)-aided communications with controllable multi-paths. In this paper, we apply DAM to multi-IRS aided wideband communication and study its practical design and achievable performance. We first provide an asymptotic analysis showing that when the number of base station (BS) antennas is much larger than that of IRSs, an ISI-free channel can be established with appropriate delay pre-compensation and the simple path-based MRT beamforming. We then consider the general system setup and study the problem of joint path-based beamforming and phase shifts design for DAM transmission, by considering the three classical beamforming techniques on a per-path basis, namely the low-complexity path-based MRT beamforming, the path-based ZF beamforming for ISI-free DAM communication, and the optimal path-based MMSE beamforming. As a comparison, OFDM-based multi-IRS aided communication is considered. Simulation results demonstrate that DAM outperforms OFDM in terms of spectral efficiency, BER, and PAPR.

cs.IT↗
arXiv2023

Delay Alignment Modulation: Manipulating Channel Delay Spread for Efficient Single- and Multi-Carrier Communication

The evolution of mobile communication networks has always been accompanied by the advancement of ISI mitigation techniques, from equalization in 2G, spread spectrum and RAKE receiver in 3G, to OFDM in 4G and 5G. Looking forward towards 6G, by exploiting the high spatial resolution brought by large antenna arrays and the multi-path sparsity of mmWave and Terahertz channels, a novel ISI mitigation technique termed delay alignment modulation (DAM) was recently proposed. However, existing works only consider the single-carrier perfect DAM, which is feasible only when the number of BS antennas is no smaller than that of channel paths, so that all multi-path signal components arrive at the receiver simultaneously and constructively. This imposes stringent requirements on the number of BS antennas and multi-path sparsity. In this paper, we propose a generic DAM technique to manipulate the channel delay spread via spatial-delay processing, thus providing a flexible framework to combat channel time dispersion for efficient single- or multi-carrier transmissions. We first show that when the number of BS antennas is much larger than that of channel paths, perfect delay alignment can be achieved to transform the time-dispersive channel to time non-dispersive channel with the simple delay pre-compensation and path-based MRT beamforming. When perfect DAM is infeasible or undesirable, the proposed generic DAM technique can be applied to significantly reduce the channel delay spread. We further propose the novel DAM-OFDM technique, which is able to save the CP overhead or mitigate the PAPR issue suffered by conventional OFDM. We show that the proposed DAM-OFDM involves joint frequency- and time-domain beamforming optimization, for which a closed-form solution is derived. Simulation results show that the proposed DAM-OFDM outperforms the conventional OFDM in terms of spectral efficiency, BER and PAPR.

cs.IT↗
arXiv2023

Delay Alignment Modulation with Hybrid beamforming for Spatially Sparse Communications

For millimeter wave (mmWave) or Terahertz (THz) communications, by leveraging the high spatial resolution offered by large antenna arrays and the multi-path sparsity of mmWave/THz channels, a novel inter-symbol interference (ISI) mitigation technique called delay alignment modulation (DAM) has been recently proposed. The key ideas of DAM are delay pre-compensation and path-based beamforming. However, existing research on DAM is based on fully digital beamforming, where the number of radio frequency (RF) chains required is equal to the number of antennas. This leads to high hardware cost and power consumption for mmWave/THz massive multiple-input multiple-output (MIMO) communications. Thus, this paper proposes the hybrid analog/digital beamforming based DAM. The analog and digital beamforming matrices are designed to achieve performance close to DAM based on fully digital beamforming. The effectiveness of the proposed technique is verified by simulation results.

eess.SP↗
arXiv2023

Can Sparse Arrays Outperform Collocated Arrays for Future Wireless Communications?

Multiple-input multiple-output (MIMO) has become a key technology for contemporary wireless communication systems. For typical MIMO systems, antenna arrays are separated by half of the signal wavelength, which are termed collocated arrays. In this paper, we ask the following question: For future wireless communication systems, is it possible to achieve better performance than collocated arrays by using sparse arrays, whose element spacing is larger than half wavelength? The answer to this question is not immediately clear since while sparse arrays may achieve narrower beam for the main lobe, they also generate undesired grating lobes. In this paper, we show that the answer to the above question is affirmative. To this end, we first provide an insightful explanation by investigating the key properties of beam patterns of sparse and collocated arrays, together with the typical distribution of spatial angle difference Δ, which all critically impact the inter-user interference (IUI). In particular, we show that sparse arrays are less likely to experience severe IUI than collocated arrays, since the probability of Δtypically reduces with the increasing of |Δ|. This naturally helps to reject those higher-order grating lobes of sparse arrays, especially when users are densely located. Then we provide a rigorous derivation of the achievable data rate for sparse and collocated arrays, and derive the condition under which sparse arrays strictly outperform collocated counterparts. Finally, numerical results are provided to validate our theoretical studies.

cs.IT↗
arXiv2023

Near-Field Beam Management for Extremely Large-Scale Array Communications

Extremely large-scale arrays (XL-arrays) have emerged as a promising technology to achieve super-high spectral efficiency and spatial resolution in future wireless systems. The large aperture of XL-arrays means that spherical rather than planar wavefronts must be considered, and a paradigm shift from far-field to near-field communications is necessary. Unlike existing works that have mainly considered far-field beam management, we study the new near-field beam management for XL-arrays. We first provide an overview of near-field communications and introduce various applications of XL-arrays in both outdoor and indoor scenarios. Then, three typical near-field beam management methods for XL-arrays are discussed: near-field beam training, beam tracking, and beam scheduling. We point out their main design issues and propose promising solutions to address them. Moreover, other important directions in near-field communications are also highlighted to motivate future research.

eess.SP↗
arXiv2023

On the Energy-Efficiency Trade-off Between Active and Passive Communications with RIS-based Symbiotic Radio

Symbiotic radio (SR) is a promising technology of spectrum- and energy-efficient wireless systems, for which the key idea is to use cognitive backscattering communication to achieve mutualistic spectrum and energy sharing with passive backscatter devices (BDs). In this paper, a reconfigurable intelligent surface (RIS) based SR system is considered, where the RIS is used not only to assist the primary active communication, but also for passive communication to transmit its own information. For the considered system, we investigate the EE trade-off between active and passive communications, by characterizing the EE region. To gain some insights, we first derive the maximum achievable individual EEs of the primary transmitter (PT) and RIS, respectively, and then analyze the asymptotic performance by exploiting the channel hardening effect. To characterize the non-trivial EE trade-off, we formulate an optimization problem to find the Pareto boundary of the EE region by jointly optimizing the transmit beamforming, power allocation and the passive beamforming of RIS. The formulated problem is non-convex, and an efficient algorithm is proposed by decomposing it into a series of subproblems by using alternating optimization (AO) and successive convex approximation (SCA) techniques. Finally, simulation results are presented to validate the effectiveness of the proposed algorithm.

cs.IT↗
arXiv2023

Near-Field Beam Focusing Pattern and Grating Lobe Characterization for Modular XL-Array

In this paper, we investigate the near-field modelling and analyze the beam focusing pattern for modular extremely large-scale array (XL-array) communications. As modular XL-array is physically and electrically large in general, the accurate characterization of amplitude and phase variations across its array elements requires the non-uniform spherical wave (NUSW) model, which, however, is difficult for performance analysis and optimization. To address this issue, we first present two ways to simplify the NUSW model by exploiting the unique regular structure of modular XL-array, termed sub-array based uniform spherical wave (USW) models with different or common angles, respectively. Based on the developed models, the near-field beam focusing patterns of XL-array communications are derived. It is revealed that compared to the existing collocated XL-array with the same number of array elements, modular XL-array can significantly enhance the spatial resolution, but at the cost of generating undesired grating lobes. Fortunately, different from the conventional far-field uniform plane wave (UPW) model, the near-field USW model for modular XL-array exhibits a higher grating lobe suppression capability, thanks to the non-linear phase variations across the array elements. Finally, simulation results are provided to verify the near-field beam focusing pattern and grating lobe characteristics of modular XL-array.

cs.IT↗
arXiv2023

Integrated Super-Resolution Sensing and Communication with 5G NR Waveform: Signal Processing with Uneven CPs and Experiments

Integrated sensing and communication (ISAC) is a promising technology to simultaneously provide high-performance wireless communication and radar sensing services in future networks. In this paper, we propose the concept of \emph{integrated super-resolution sensing and communication} (ISSAC), which uses super-resolution algorithms in ISAC systems to achieve extreme sensing performance for those critical parameters, such as delay, Doppler, and angle of the sensing targets. Based on practical fifth generation (5G) New Radio (NR) waveforms, the signal processing techniques of ISSAC are investigated and prototyping experiments are performed to verify the achievable performance. To this end, we first study the effect of uneven cyclic prefix (CP) lengths of 5G NR orthogonal frequency division multiplexing (OFDM) waveforms on various sensing algorithms. Specifically, the performance of the standard Periodogram based radar processing method, together with the two classical super-resolution algorithms, namely, MUltiple SIgnal Classification (MUSIC) and Estimating Signal Parameter via Rotational Invariance Techniques (ESPRIT) are analyzed in terms of the delay and Doppler estimation. To resolve the uneven CP issue, a new structure of steering vector for MUSIC and a new selection of submatrices for ESPRIT are proposed. Furthermore, an ISSAC experiment platform is setup to validate the theoretical analysis, and the experimental results show that the performance degradation caused by unequal CP length is insignificant and high-resolution delay and Doppler estimation of the target can be achieved with 5G NR waveforms.

eess.SP↗
arXiv2023

Channel Estimation for Delay Alignment Modulation

Delay alignment modulation (DAM) is a promising technology for inter-symbol interference (ISI)-free communication without relying on sophisticated channel equalization or multi-carrier transmissions. The key ideas of DAM are delay precompensation and path-based beamforming, so that the multi-path signal components will arrive at the receiver simultaneously and constructively, rather than causing the detrimental ISI. However, the practical implementation of DAM requires channel state information (CSI) at the transmitter side. Therefore, in this letter, we study an efficient channel estimation method for DAM based on block orthogonal matching pursuit (BOMP) algorithm, by exploiting the block sparsity of the channel vector. Based on the imperfectly estimated CSI, the delay pre-compensations and tap-based beamforming are designed for DAM, and the resulting performance is studied. Simulation results demonstrate that with the BOMP-based channel estimation method, the CSI can be effectively acquired with low training overhead, and the performance of DAM based on estimated CSI is comparable to the ideal case with perfect CSI.

cs.IT↗