Minjian Zhao

Bio: Minjian Zhao is an academic researcher from Zhejiang University. The author has contributed to research in topics: MIMO & Channel state information. The author has an hindex of 21, co-authored 194 publications receiving 1587 citations. Previous affiliations of Minjian Zhao include University of Toronto.

Joint Offloading and Trajectory Design for UAV-Enabled Mobile Edge Computing Systems

Abstract: Unmanned aerial vehicles (UAVs) have been considered in wireless communication systems to provide high-quality services for their low cost and high maneuverability. This paper addresses a UAV-aided mobile edge computing system, where a number of ground users are served by a moving UAV equipped with computing resources. Each user has computing tasks to complete, which can be separated into two parts: one portion is offloaded to the UAV and the remaining part is implemented locally. The UAV moves around above the ground users and provides computing service in an orthogonal multiple access manner over time. For each time period, we aim to minimize the sum of the maximum delay among all the users in each time slot by jointly optimizing the UAV trajectory, the ratio of offloading tasks, and the user scheduling variables, subject to the discrete binary constraints, the energy consumption constraints, and the UAV trajectory constraints. This problem has highly nonconvex objective function and constraints. Therefore, we equivalently convert it into a better tractable form based on introducing the auxiliary variables, and then propose a novel penalty dual decomposition-based algorithm to handle the resulting problem. Furthermore, we develop a simplified ${l}_{0}$ -norm algorithm with much reduced complexity. Besides, we also extend our algorithm to minimize the average delay. Simulation results illustrate that the proposed algorithms significantly outperform the benchmarks.

Dual-UAV-Enabled Secure Communications: Joint Trajectory Design and User Scheduling

Abstract: In this paper, we investigate a novel unmanned aerial vehicle (UAV)-enabled secure communication system. Two UAVs are applied in this system where one UAV moves around to communicate with multiple users on the ground using orthogonal time-division multiple access while the other UAV in the area jams the eavesdroppers on the ground to protect communications of the desired users. Specifically, we maximize the minimum worst-case secrecy rate among the users within each period by jointly adjusting UAV trajectories and user scheduling under the maximum UAV speed constraints, the UAV return constraints, the UAV collision avoidance constraints, and the discrete binary constraints on user scheduling variables. Since the resulting optimization problem is very difficult to solve due to its highly nonlinear objective function and nonconvex constraints, we first equivalently transform it into a more tractable problem. In particular, the binary constraints are equivalently converted to a number of equality constraints. Then, we develop a novel joint optimization algorithm to handle the converted problem. In order to further improve the secrecy rate performance, we also extend the developed algorithm to the case with multiple jamming UAVs. The simulation results show that the proposed joint optimization algorithm achieves significantly better performance than the conventional algorithms.

Intelligent Reflecting Surface Enhanced Wireless Networks: Two-Timescale Beamforming Optimization

Abstract: Intelligent reflecting surface (IRS) has drawn a lot of attention recently as a promising new solution to achieve high spectral and energy efficiency for future wireless networks. By utilizing massive low-cost passive reflecting elements, the wireless propagation environment becomes controllable and thus can be made favorable for improving the communication performance. Prior works on IRS mainly rely on the instantaneous channel state information (I-CSI), which, however, is practically difficult to obtain for IRS-associated links due to its passive operation and large number of reflecting elements. To overcome this difficulty, we propose in this paper a new two-timescale (TTS) transmission protocol to maximize the achievable average sum-rate for an IRS-aided multiuser system under the general correlated Rician channel model. Specifically, the passive IRS phase shifts are first optimized based on the statistical CSI (S-CSI) of all links, which varies much slowly as compared to their I-CSI; while the transmit beamforming/precoding vectors at the access point (AP) are then designed to cater to the I-CSI of the users’ effective fading channels with the optimized IRS phase shifts, thus significantly reducing the channel training overhead and passive beamforming design complexity over the existing schemes based on the I-CSI of all channels. Besides, for ease of practical implementation, we consider discrete phase shifts at each reflecting element of the IRS. For the single-user case, an efficient penalty dual decomposition (PDD)-based algorithm is proposed, where the IRS phase shifts are updated in parallel to reduce the computational time. For the multiuser case, we propose a general TTS stochastic successive convex approximation (SSCA) algorithm by constructing a quadratic surrogate of the objective function, which cannot be explicitly expressed in closed-form. Simulation results are presented to validate the effectiveness of our proposed algorithms and evaluate the impact of S-CSI and channel correlation on the system performance.

Multiple Access for Mobile-UAV Enabled Networks: Joint Trajectory Design and Resource Allocation

Abstract: In this paper, we investigate joint trajectory design and resource allocation algorithms to maximize the minimum average rate among ground users for unmanned aerial vehicle (UAV) communication systems, where both the orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) modes are considered. We first formulate the problems for UAV communications with the OMA and NOMA modes, respectively, which contain binary variables and highly coupled nonconvex objective functions and constraints. In order to handle the challenging problems, we transform the original problems into more tractable forms and then develop novel algorithms based on penalty dual-decomposition technique to solve them. Simulation results show that the proposed algorithms outperform the benchmarks.

Adaptive Reduced-Rank Receive Processing Based on Minimum Symbol-Error-Rate Criterion for Large-Scale Multiple-Antenna Systems

Abstract: In this work, we propose a novel adaptive reduced-rank receive processing strategy based on joint preprocessing, decimation and filtering (JPDF) for large-scale multiple-antenna systems. In this scheme, a reduced-rank framework is employed for linear receive processing and multiuser interference suppression based on the minimization of the symbol-error-rate (SER) cost function. We present a structure with multiple processing branches that performs a dimensionality reduction, where each branch contains a group of jointly optimized preprocessing and decimation units, followed by a linear receive filter. We then develop stochastic gradient (SG) algorithms to compute the parameters of the preprocessing and receive filters, along with a low-complexity decimation technique for both binary phase shift keying (BPSK) and $M$ -ary quadrature amplitude modulation (QAM) symbols. In addition, an automatic parameter selection scheme is proposed to further improve the convergence performance of the proposed reduced-rank algorithms. Simulation results are presented for time-varying wireless environments and show that the proposed JPDF minimum-SER receive processing strategy and algorithms achieve a superior performance than existing methods with a reduced computational complexity.

An Overview of Signal Processing Techniques for Millimeter Wave MIMO Systems

Abstract: Communication at millimeter wave (mmWave) frequencies is defining a new era of wireless communication. The mmWave band offers higher bandwidth communication channels versus those presently used in commercial wireless systems. The applications of mmWave are immense: wireless local and personal area networks in the unlicensed band, 5G cellular systems, not to mention vehicular area networks, ad hoc networks, and wearables. Signal processing is critical for enabling the next generation of mmWave communication. Due to the use of large antenna arrays at the transmitter and receiver, combined with radio frequency and mixed signal power constraints, new multiple-input multiple-output (MIMO) communication signal processing techniques are needed. Because of the wide bandwidths, low complexity transceiver algorithms become important. There are opportunities to exploit techniques like compressed sensing for channel estimation and beamforming. This article provides an overview of signal processing challenges in mmWave wireless systems, with an emphasis on those faced by using MIMO communication at higher carrier frequencies.

Intelligent Reflecting Surface-Aided Wireless Communications: A Tutorial

Abstract: Intelligent reflecting surface (IRS) is an enabling technology to engineer the radio signal propagation 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 communications 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.

Millimeter Wave Communications for Future Mobile Networks

Abstract: Millimeter wave (mmWave) communications have recently attracted large research interest, since the huge available bandwidth can potentially lead to the rates of multiple gigabit per second per user Though mmWave can be readily used in stationary scenarios, such as indoor hotspots or backhaul, it is challenging to use mmWave in mobile networks, where the transmitting/receiving nodes may be moving, channels may have a complicated structure, and the coordination among multiple nodes is difficult To fully exploit the high potential rates of mmWave in mobile networks, lots of technical problems must be addressed This paper presents a comprehensive survey of mmWave communications for future mobile networks (5G and beyond) We first summarize the recent channel measurement campaigns and modeling results Then, we discuss in detail recent progresses in multiple input multiple output transceiver design for mmWave communications After that, we provide an overview of the solution for multiple access and backhauling, followed by the analysis of coverage and connectivity Finally, the progresses in the standardization and deployment of mmWave for mobile networks are discussed

Nonlinear Fractional Programming

Abstract: In this chapter we deal with the following nonlinear fractional programming problem: $$P:\mathop{{\max }}\limits_{{x \in s}} q(x) = (f(x) + \alpha )/((x) + \beta )$$ where f, g: R n → R, α, β ∈ R, S ⊆ R n . To simplify things, and without restricting the generality of the problem, it is usually assumed that, g(x) + β + 0 for all x ∈ S,S is non-empty and that the objective function has a finite optimal value.