arXiv subjects

Shuowen Zhang

Publications and source records attributed to Shuowen Zhang.

At least 73 records · Page 4Linked to original sources

arXiv2022

Massive MIMO Communication with Intelligent Reflecting Surface

This paper studies the feasibility of deploying intelligent reflecting surfaces (IRSs) in massive MIMO (multiple-input multiple-output) systems to improve the performance of users in the service dead zone. To reduce the channel training overhead, we advocate a novel protocol for the uplink communication in the IRS-assisted massive MIMO systems. Under this protocol, the IRS reflection coefficients are optimized based on the channel covariance matrices, which are generally fixed for many coherence blocks, to boost the long-term performance. Then, given the IRS reflecting coefficients, the BS beamforming vectors are designed in each coherence block based on the effective channel of each user, which is the superposition of its direct and reflected user-IRS-BS channels, to improve the instantaneous performance. Since merely the user effective channels are estimated in each coherence block, the training overhead of this protocol is the same as that in the legacy wireless systems without IRSs. Moreover, in the asymptotic regime that the numbers of IRS elements and BS antennas both go to infinity with a fixed ratio, we manage to first characterize the minimum mean-squared error (MMSE) estimators of the user effective channels and then quantify the closed-form user achievable rates as functions of channel covariance matrices with channel training overhead and estimation error taken into account. Interestingly, it is shown that the properties of channel hardening and favorable propagation still hold for the user effective channels, and satisfactory user rates are thus achievable even if simple BS beamforming solutions, e.g., maximal-ratio combining, are employed. Finally, thanks to the rate characterization, we design a low-complexity algorithm to optimize the IRS reflection coefficients based on channel covariance matrices.

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arXiv2022

Exploiting Temporal Side Information in Massive IoT Connectivity

This paper considers the joint device activity detection and channel estimation problem in a massive Internet of Things (IoT) connectivity system, where a large number of IoT devices exist but merely a random subset of them become active for short-packet transmission in each coherence block. In particular, we propose to leverage the temporal correlation in device activity, e.g., a device active in the previous coherence block is more likely to be still active in the current coherence block, to improve the detection and estimation performance. However, it is challenging to utilize this temporal correlation as side information (SI), which relies on the knowledge about the exact statistical relation between the estimated activity pattern for the previous coherence block (which may be imperfect with unknown error) and the true activity pattern in the current coherence block. To tackle this challenge, we establish a novel SI-aided multiple measurement vector approximate message passing (MMV-AMP) framework. Specifically, thanks to the state evolution of the MMV-AMP algorithm, the correlation between the activity pattern estimated by the MMV-AMP algorithm in the previous coherence block and the real activity pattern in the current coherence block is quantified explicitly. Based on the well-defined temporal correlation, we further manage to embed this useful SI into the denoiser design under the MMV-AMP framework. Specifically, the SI-based soft-thresholding denoisers with binary thresholds and the SI-based minimum mean-squared error (MMSE) denoisers are characterized for the cases without and with the knowledge of the channel distribution, respectively. Numerical results are given to show the significant gain in device activity detection and channel estimation performance brought by our proposed SI-aided MMV-AMP framework.

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arXiv2021

Intelligent Reflecting Surface Aided Multi-User Communication: Capacity Region and Deployment Strategy

Intelligent reflecting surface (IRS) is a new promising technology that is able to reconfigure the wireless propagation channel via smart and passive signal reflection. In this paper, we investigate the capacity region of a two-user communication network with one access point (AP) aided by $M$ IRS elements for enhancing the user-AP channels, where the IRS incurs negligible delay, thus the user-AP channels via the IRS follow the classic discrete memoryless channel model. In particular, we consider two practical IRS deployment strategies that lead to different effective channels between the users and AP, namely, the distributed deployment where the $M$ elements form two IRSs, each deployed in the vicinity of one user, versus the centralized deployment where all the $M$ elements are deployed in the vicinity of the AP. First, we consider the uplink multiple-access channel (MAC) and derive the capacity/achievable rate regions for both deployment strategies under different multiple access schemes. It is shown that the centralized deployment generally outperforms the distributed deployment under symmetric channel setups in terms of achievable user rates. Next, we extend the results to the downlink broadcast channel (BC) by leveraging the celebrated uplink-downlink (or MAC-BC) duality framework, and show that the superior rate performance of centralized over distributed deployment also holds. Numerical results are presented that validate our analysis, and reveal new and useful insights for optimal IRS deployment in wireless networks.

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arXiv2021

A New Channel Estimation Strategy in Intelligent Reflecting Surface Assisted Networks

Channel estimation is the main hurdle to reaping the benefits promised by the intelligent reflecting surface (IRS), due to its absence of ability to transmit/receive pilot signals as well as the huge number of channel coefficients associated with its reflecting elements. Recently, a breakthrough was made in reducing the channel estimation overhead by revealing that the IRS-BS (base station) channels are common in the cascaded user-IRS-BS channels of all the users, and if the cascaded channel of one typical user is estimated, the other users' cascaded channels can be estimated very quickly based on their correlation with the typical user's channel \cite{b5}. One limitation of this strategy, however, is the waste of user energy, because many users need to keep silent when the typical user's channel is estimated. In this paper, we reveal another correlation hidden in the cascaded user-IRS-BS channels by observing that the user-IRS channel is common in all the cascaded channels from users to each BS antenna as well. Building upon this finding, we propose a novel two-phase channel estimation protocol in the uplink communication. Specifically, in Phase I, the correlation coefficients between the channels of a typical BS antenna and those of the other antennas are estimated; while in Phase II, the cascaded channel of the typical antenna is estimated. In particular, all the users can transmit throughput Phase I and Phase II. Under this strategy, it is theoretically shown that the minimum number of time instants required for perfect channel estimation is the same as that of the aforementioned strategy in the ideal case without BS noise. Then, in the case with BS noise, we show by simulation that the channel estimation error of our proposed scheme is significantly reduced thanks to the full exploitation of the user energy.

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arXiv2021

On Massive IoT Connectivity with Temporally-Correlated User Activity

This paper considers joint device activity detection and channel estimation in Internet of Things (IoT) networks, where a large number of IoT devices exist but merely a random subset of them become active for short-packet transmission at each time slot. In particular, we propose to leverage the temporal correlation in user activity, i.e., a device active at the previous time slot is more likely to be still active at the current moment, to improve the detection performance. Despite the temporally-correlated user activity in consecutive time slots, it is challenging to unveil the connection between the activity pattern estimated previously, which is imperfect but the only available side information (SI), and the true activity pattern at the current moment due to the unknown estimation error. In this work, we manage to tackle this challenge under the framework of approximate message passing (AMP). Specifically, thanks to the state evolution, the correlation between the activity pattern estimated by AMP at the previous time slot and the real activity pattern at the previous and current moment is quantified explicitly. Based on the well-defined temporal correlation, we further manage to embed this useful SI into the design of the minimum mean-squared error (MMSE) denoisers and log-likelihood ratio (LLR) test based activity detectors under the AMP framework. Theoretical comparison between the SI-aided AMP algorithm and its counterpart without utilizing temporal correlation is provided. Moreover, numerical results are given to show the significant gain in activity detection accuracy brought by the SI-aided algorithm.

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arXiv2021

Double-IRS Aided MIMO Communication under LoS Channels: Capacity Maximization and Scaling

Intelligent reflecting surface (IRS) is a promising technology to extend the wireless signal coverage and support the high performance communication. By intelligently adjusting the reflection coefficients of a large number of passive reflecting elements, the IRS can modify the wireless propagation environment in favour of signal transmission. Different from most of the prior works which did not consider any cooperation between IRSs, in this work we propose and study a cooperative double-IRS aided multiple-input multiple-output (MIMO) communication system under the line-of-sight (LoS) propagation channels. We investigate the capacity maximization problem by jointly optimizing the transmit covariance matrix and the passive beamforming matrices of the two cooperative IRSs. Although the above problem is non-convex and difficult to solve, we transform and simplify the original problem by exploiting a tractable characterization of the LoS channels. Then we develop a novel low-complexity algorithm whose complexity is independent of the number of IRS elements. Moreover, we analyze the capacity scaling orders of the double-IRS aided MIMO system with respect to an asymptotically large number of IRS elements or transmit power, which significantly outperform those of the conventional single-IRS aided MIMO system, thanks to the cooperative passive beamforming gain brought by the double-reflection link and the spatial multiplexing gain harvested from the two single-reflection links. Extensive numerical results are provided to show that by exploiting the LoS channel properties, our proposed algorithm can achieve a desirable performance with low computational time. Also, our capacity scaling analysis is validated, and the double-IRS system is shown to achieve a much higher rate than its single-IRS counterpart as long as the number of IRS elements or the transmit power is not small.

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arXiv2020

Radio Map Based 3D Path Planning for Cellular-Connected UAV

In this paper, we study the three-dimensional (3D) path planning for a cellular-connected unmanned aerial vehicle (UAV) to minimize its flying distance from given initial to final locations, while ensuring a target link quality in terms of the expected signal-to-interference-plus-noise ratio (SINR) at the UAV receiver with each of its associated ground base stations (GBSs) during the flight. To exploit the location-dependent and spatially varying channel as well as interference over the 3D space, we propose a new radio map based path planning framework for the UAV. Specifically, we consider the channel gain map of each GBS that provides its large-scale channel gains with uniformly sampled locations on a 3D grid, which are due to static and large-size obstacles (e.g., buildings) and thus assumed to be time-invariant. Based on the channel gain maps of GBSs as well as their loading factors, we then construct an SINR map that depicts the expected SINR levels over the sampled 3D locations. By leveraging the obtained SINR map, we proceed to derive the optimal UAV path by solving an equivalent shortest path problem (SPP) in graph theory. We further propose a grid quantization approach where the grid points in the SINR map are more coarsely sampled by exploiting the spatial channel/interference correlation over neighboring grids. Then, we solve an approximate SPP over the reduced-size SINR map (graph) with reduced complexity. Numerical results show that the proposed solution can effectively minimize the flying distance/time of the UAV subject to its communication quality constraint, and a flexible trade-off between performance and complexity can be achieved by adjusting the grid quantization ratio in the SINR map. Moreover, the proposed solution significantly outperforms various benchmark schemes without fully exploiting the channel/interference spatial distribution in the network.

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arXiv2020

A Two-Stage Radar Sensing Approach based on MIMO-OFDM Technology

Recently, integrating the communication and sensing functions into a common network has attracted a great amount of attention. This paper considers the advanced signal processing techniques for enabling the radar to sense the environment via the communication signals. Since the technologies of orthogonal frequency division multiplexing (OFDM) and multiple-input multiple-output (MIMO) are widely used in the legacy cellular systems, this paper proposes a two-stage signal processing approach for radar sensing in an MIMO-OFDM system, where the scattered channels caused by various targets are estimated in the first stage, and the location information of the targets is then extracted from their scattered channels in the second stage. Specifically, based on the observations that radar sensing is similar to multi-path communication in the sense that different targets scatter the signal sent by the radar transmitter to the radar receiver with various delay, and that the number of scatters is limited, we show that the OFDM-based channel training approach together with the compressed sensing technique can be utilized to estimate the scattered channels efficiently in Stage I. Moreover, to tackle the challenge arising from range resolution for sensing the location of closely spaced targets, we show that the MIMO radar technique can be leveraged in Stage II such that the radar has sufficient spatial samples to even detect the targets in close proximity based on their scattered channels. Last, numerical examples are provided to show the effectiveness of our proposed sensing approach which merely relies on the existing MIMO-OFDM communication techniques.

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arXiv2020

Intelligent Reflecting Surface Aided Multicasting with Random Passive Beamforming

In this letter, we consider a multicast system where a single-antenna transmitter sends a common message to multiple single-antenna users, aided by an intelligent reflecting surface (IRS) equipped with $N$ passive reflecting elements. Prior works on IRS have mostly assumed the availability of channel state information (CSI) for designing its passive beamforming. However, the acquisition of CSI requires substantial training overhead that increases with $N$. In contrast, we propose in this letter a novel \emph{random passive beamforming} scheme, where the IRS performs independent random reflection for $Q\geq 1$ times in each channel coherence interval without the need of CSI acquisition. For the proposed scheme, we first derive a closed-form approximation of the outage probability, based on which the optimal $Q$ with best outage performance can be efficiently obtained. Then, for the purpose of comparison, we derive a lower bound of the outage probability with traditional CSI-based passive beamforming. Numerical results show that a small $Q$ is preferred in the high-outage regime (or with high rate target) and the optimal $Q$ becomes larger as the outage probability decreases (or as the rate target decreases). Moreover, the proposed scheme significantly outperforms the CSI-based passive beamforming scheme with training overhead taken into consideration when $N$ and/or the number of users are large, thus offering a promising CSI-free alternative to existing CSI-based schemes.

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arXiv2020

Intelligent Reflecting Surface Aided Wireless Communications: A Tutorial

Intelligent reflecting surface (IRS) is an enabling technology to engineer the radio signal prorogation in wireless networks. By smartly tuning the signal reflection via a large number of low-cost passive reflecting elements, IRS is capable of dynamically altering wireless channels to enhance the communication performance. It is thus expected that the new IRS-aided hybrid wireless network comprising both active and passive components will be highly promising to achieve a sustainable capacity growth cost-effectively in the future. Despite its great potential, IRS faces new challenges to be efficiently integrated into wireless networks, such as reflection optimization, channel estimation, and deployment from communication design perspectives. In this paper, we provide a tutorial overview of IRS-aided wireless communication to address the above issues, and elaborate its reflection and channel models, hardware architecture and practical constraints, as well as various appealing applications in wireless networks. Moreover, we highlight important directions worthy of further investigation in future work.

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arXiv2020

Cooperative Double-IRS Aided Communication: Beamforming Design and Power Scaling

Intelligent reflecting surface (IRS) is a promising technology to support high performance wireless communication. By adaptively configuring the reflection amplitude and/or phase of each passive reflecting element on it, the IRS can reshape the electromagnetic environment in favour of signal transmission. This letter advances the existing research by proposing and analyzing a double-IRS aided wireless communication system. Under the reasonable assumption that the reflection channel from IRS 1 to IRS 2 is of rank 1 (e.g., line-of-sight channel), we propose a joint passive beamforming design for the two IRSs. Based on this, we show that deploying two cooperative IRSs with in total K elements can yield a power gain of order O(K^4), which greatly outperforms the case of deploying one traditional IRS with a power gain of order O(K^2). Our simulation results validate that the performance of deploying two cooperative IRSs is significantly better than that of deploying one IRS given a sufficient total number of IRS elements. We also extend our line-of-sight channel model to show how different channel models affect the performance of the double-IRS aided wireless communication system.

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arXiv2020

Intelligent Reflecting Surface Aided Multiple Access: Capacity Region and Deployment Strategy

Intelligent reflecting surface (IRS) is a new promising technology that is able to manipulate the wireless propagation channel via smart and controllable signal reflection. In this paper, we investigate the capacity region of a multiple access channel (MAC) with two users sending independent messages to an access point (AP), aided by $M$ IRS reflecting elements. We consider two practical IRS deployment strategies that lead to different user-AP effective channels, namely, the distributed deployment where the $M$ reflecting elements form two IRSs, each deployed in the vicinity of one user, versus the centralized deployment where all the $M$ reflecting elements are deployed in the vicinity of the AP. For the distributed deployment, we derive the capacity region in closed-form; while for the centralized deployment, we derive a capacity region outer bound and propose an efficient rate-profile based method to characterize an achievable rate region (or capacity region inner bound). Furthermore, we compare the capacity regions of the two cases and draw useful insights into the optimal deployment of IRS in practical systems.

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arXiv2019

Radio Map Based Path Planning for Cellular-Connected UAV

In this paper, we study the path planning for a cellular-connected unmanned aerial vehicle (UAV) to minimize its flying distance from given initial to final locations, while ensuring a target link quality in terms of the large-scale channel gain with each of its associated ground base stations (GBSs) during the flight. To this end, we propose the use of radio map that provides the information on the large-scale channel gains between each GBS and uniformly sampled locations on a three-dimensional (3D) grid over the region of interest, which are assumed to be time-invariant due to the generally static and large-size obstacles therein (e.g., buildings). Based on the given radio maps of the GBSs, we first obtain the optimal UAV path by solving an equivalent shortest path problem (SPP) in graph theory. To reduce the computation complexity of the optimal solution, we further propose a grid quantization method whereby the grid points in each GBS's radio map are more coarsely sampled by exploiting the spatial channel correlation over neighboring grids. Then, we solve the approximate SPP over the reduced-size radio map (graph) more efficiently. Numerical results show that the proposed solutions can effectively minimize the flying distance of the UAV subject to its communication quality constraint. Moreover, a flexible trade-off between performance and complexity can be achieved by adjusting the quantization ratio for the radio map.

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arXiv2019

IRS-Enhanced OFDMA: Joint Resource Allocation and Passive Beamforming Optimization

Intelligent reflecting surface (IRS) is an emerging technique to enhance the wireless communication spectral efficiency with low hardware and energy cost. In this letter, we consider the integration of IRS to an orthogonal frequency division multiple access (OFDMA) based multiuser downlink communication system, and study the pertinent joint optimization of the IRS reflection coefficients and OFDMA time-frequency resource block as well as power allocations to maximize the users' common (minimum) rate. Specifically, due to the lack of frequency-selective passive beamforming capability at the IRS, only one set of reflection coefficients can be designed for adapting to a large number of channels of multiple users over different frequency sub-bands. To tackle this difficulty, we propose a novel dynamic passive beamforming scheme where the IRS reflection coefficients are dynamically adjusted over different time slots within each channel coherence block to create artificial time-varying channels and select only a subset of the users to be simultaneously served in each time slot, thus achieving a higher passive beamforming gain. Although the formulated optimization problem is non-convex, we propose an efficient algorithm to obtain a suboptimal solution to it. Numerical results show that the proposed scheme significantly improves the system common rate over the setup without IRS and that with random IRS reflection coefficients. Moreover, our proposed dynamic passive beamforming outperforms the fixed passive beamforming which employs a common set of reflection coefficients in each channel coherence block, by more flexibly balancing between passive beamforming and multiuser diversity gains.

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arXiv2019

Intelligent Reflecting Surface Meets OFDM: Protocol Design and Rate Maximization

Intelligent reflecting surface (IRS) is a promising new technology for achieving both spectrum and energy efficient wireless communication systems in the future. However, existing works on IRS mainly consider frequency-flat channels and assume perfect knowledge of channel state information (CSI) at the transmitter. Motivated by this, in this paper we study an IRS-enhanced orthogonal frequency division multiplexing (OFDM) system under frequency-selective channels and propose a practical transmission protocol with channel estimation. First, to reduce the overhead in channel training and estimation and to exploit the channel spatial correlation, we propose a novel IRS elements grouping method, where each group consists of a set of adjacent IRS elements that share a common reflection coefficient. Based on this grouping method, we propose a practical transmission protocol where only the combined channel of each group needs to be estimated, thus substantially reducing the training overhead. Next, with any given grouping and estimated CSI, we formulate the problem to maximize the achievable rate by jointly optimizing the transmit power allocation and the IRS passive array reflection coefficients. Although the formulated problem is non-convex and thus difficult to solve, we propose an efficient algorithm to obtain a high-quality suboptimal solution for it, by alternately optimizing the power allocation and the passive array coefficients in an iterative manner, along with a customized method for the initialization. Simulation results show that the proposed design significantly improves the OFDM link rate performance as compared to the case without using IRS. Moreover, it is shown that there exists an optimal size for IRS elements grouping which achieves the maximum achievable rate due to the trade-off between the training overhead and IRS passive beamforming flexibility.

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arXiv2019

Capacity Characterization for Intelligent Reflecting Surface Aided MIMO Communication

Intelligent reflecting surface (IRS) is a promising solution to enhance the wireless communication capacity both cost-effectively and energy-efficiently, by properly altering the signal propagation via tuning a large number of passive reflecting units. In this paper, we aim to characterize the fundamental capacity limit of IRS-aided point-to-point multiple-input multiple-output (MIMO) communication systems with multi-antenna transmitter and receiver in general, by jointly optimizing the IRS reflection coefficients and the MIMO transmit covariance matrix. First, we consider narrowband transmission under frequency-flat fading channels, and develop an efficient alternating optimization algorithm to find a locally optimal solution by iteratively optimizing the transmit covariance matrix or one of the reflection coefficients with the others being fixed. Next, we consider capacity maximization for broadband transmission in a general MIMO orthogonal frequency division multiplexing (OFDM) system under frequency-selective fading channels, where transmit covariance matrices can be optimized for different subcarriers while only one common set of IRS reflection coefficients can be designed to cater to all subcarriers. To tackle this more challenging problem, we propose a new alternating optimization algorithm based on convex relaxation to find a high-quality suboptimal solution. Numerical results show that our proposed algorithms achieve substantially increased capacity compared to traditional MIMO channels without the IRS, and also outperform various benchmark schemes. In particular, it is shown that with the proposed algorithms, various key parameters of the IRS-aided MIMO channel such as channel total power, rank, and condition number can be significantly improved for capacity enhancement.

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arXiv2019

IRS-Enhanced OFDM: Power Allocation and Passive Array Optimization

Intelligent reflecting surface (IRS) is a promising new technology for achieving spectrum and energy efficient wireless communication systems in the future. By adaptively varying the incident signals' phases/amplitudes and thereby establishing favorable channel responses through a large number of reconfigurable passive reflecting elements, IRS is able to enhance the communication performance of mobile users in its vicinity cost-effectively. In this paper, we study an IRS-enhanced orthogonal frequency division multiplexing (OFDM) system in which an IRS is deployed to assist the communication between a nearby user and its associated base station (BS). We aim to maximize the downlink achievable rate for the user by jointly optimizing the transmit power allocation at the BS and the passive array reflection coefficients at the IRS. Although the formulated problem is non-convex and thus difficult to solve, we propose an efficient algorithm to obtain a high-quality suboptimal solution for it, by alternately optimizing the BS's power allocation and the IRS's passive array coefficients in an iterative manner, along with a customized method for the initialization. Simulation results show that the proposed design significantly improves the OFDM link rate performance as compared to the cases without the IRS or with other heuristic IRS designs.

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arXiv2019

Trajectory Design for Cellular-Connected UAV Under Outage Duration Constraint

In this paper, we study the trajectory design for a cellular-connected unmanned aerial vehicle (UAV) with given initial and final locations, while communicating with the ground base stations (GBSs) along its flight. We consider delay-limited communications between the UAV and its associated GBSs, where a given signal-to-noise ratio (SNR) target needs to be satisfied at the receiver. However, in practice, due to various factors such as quality-of-service (QoS) requirement, GBSs' availability and UAV mobility constraints, the SNR target may not be met at certain time periods during the flight, each termed as an outage duration. In this paper, we aim to optimize the UAV trajectory to minimize its mission completion time, subject to a constraint on the maximum tolerable outage duration in its flight. To tackle this non-convex problem, we first transform it into a more tractable form and thereby reveal some useful properties of the optimal trajectory solution. Based on these properties, we then further simplify the problem and propose efficient algorithms to check the feasibility of the problem as well as to obtain its optimal and high-quality suboptimal solutions, by leveraging graph theory and convex optimization techniques. Numerical results show that our proposed trajectory designs outperform the conventional method based on dynamic programming, in terms of both performance and complexity.

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