Publications

The paper addresses the problems about calibration of vector modulation error, especially of Error Vector Magnitude (EVM): first, signal generator calibrates VSA and vice versa, it forms a closed-loop, so it lacks traceability. Second, signal setting of vector modulation error has always been neglected in the VSA calibration. To solve the problems, we propose new solutions to calibrate EVM, namely, by setting RF attenuation and phase shifting to construct an ellipse on IQ coordinates, get an "equivalent MPSK signals" with certain EVM. The equivalent MPSK signals' EVM are solely decided by RF attenuation and phase shifting, and thus traceable, analytically, calculable and also adjustable within a wide range. The above solutions have been proven through experiments. As a result, these solutions could be used to calibrate vector signal analyzer (VSA) and communications tester.

In cognitive radio (CR) communication systems, achieving high secondary user (SU) rate in the presence of strong cross-link interference with the primary user (PU) is challenging. In this letter, we exploit the emerging intelligent reflecting surface (IRS) technology to tackle this problem. Specifically, we investigate an IRS-assisted CR communication system where an IRS is deployed to assist in the spectrum sharing between a PU link and an SU link. We aim to maximize the achievable SU rate subject to a given signal-to-interference-plus-noise ratio target for the PU link, by jointly optimizing the SU transmit power and IRS reflect beamforming. Since the formulated problem is difficult to solve due to its non-convexity and coupled variables, we propose an efficient algorithm based on alternating optimization and successive convex approximation techniques to solve it sub-optimally, along with some heuristic designs for lower complexity. Simulation results show that IRS is able to significantly improve the SU rate, even for the scenarios deemed most challenging in conventional CR systems without using IRS.

Recently, simultaneous wireless information and power transfer (SWIPT) has attracted significant research attention. By exploiting an efficient use of radio frequency (RF) signals for both wireless information transfer (WIT) and wireless power transfer (WPT) at the same time, SWIPT is a promising technology to provide cost-effective information and energy access to wireless devices in various applications in future. To improve the efficiency of SWIPT, transmit beamforming has been recognized as a key technique, where multiple antennas are used at the transmitter to adaptively form transmit beams toward multiple information and energy user receivers to effectively compensate the path loss as well as mitigate the inter-user interference. However, it is a challenging task to optimally design the beamforming in SWIPT due to the new rate-energy trade-off between information and energy receivers, their practical designs, as well as the potential security risk that energy receivers may eavesdrop the signals for information receivers. This chapter provides an overview on recent advances in transmit beamforming design for SWIPT by tackling the above challenges. In particular, we focus on transmit beamforming optimization problems in a multiuser multiple-input single-output (MISO) broadcast channel setup, under different types of practical receiver structures (e.g., time-switching and power-splitting receivers). Extensions to other setups and practical considerations are also discussed.

Millimeter wave (mmWave) communication is a promising technology for future wireless systems, while one practical challenge is to achieve its large-antenna gains with only limited radio frequency (RF) chains for cost-effective implementation. To this end, we study in this paper a new lens antenna array enabled mmWave multiple-input multiple-output (MIMO) communication system. We first show that the array response of lens antenna arrays follows a "sinc" function, where the antenna element with the peak response is determined by the angle of arrival (AoA)/departure (AoD) of the received/transmitted signal. By exploiting this unique property along with the multi-path sparsity of mmWave channels, we propose a novel low-cost and capacity-achieving spatial multiplexing scheme for both narrow-band and wide-band mmWave communications, termed path division multiplexing (PDM), where parallel data streams are transmitted over different propagation paths with simple per-path processing. We further propose a simple path grouping technique with group-based small-scale MIMO processing to effectively mitigate the inter-stream interference due to similar AoAs/AoDs. Numerical results are provided to compare the performance of the proposed mmWave lens MIMO against the conventional MIMO with uniform planar arrays (UPAs) and hybrid analog/digital processing. It is shown that the proposed design achieves significant throughput gains as well as complexity and cost reductions, thus leading to a promising new paradigm for mmWave MIMO communications.

In this letter, we study a wireless communication system with a fixed-wing unmanned aerial vehicle (UAV) employed to collect information from a group of ground nodes (GNs). Our objective is to maximize the UAV's energy efficiency (EE), which is defined as the achievable throughput among all GNs per unit propulsion energy consumption of the UAV. To efficiently solve this problem with continuous-time functions, we propose a new method based on receding horizon optimization (RHO), which significantly reduces the computational complexity compared to the conventional time discretization method. Specifically, we sequentially solve the EE maximization problem over a moving time-window of finite duration, for each of which the number of optimization variables is greatly reduced. Simulation results are provided to show the effectiveness of the proposed method.

In an ultra-dense cloud radio access network (UD-CRAN), a large number of remote radio heads (RRHs), typically employed as simple relay nodes, are distributed in a target area, which could even outnumber their served users. However, one major challenge is that the large amount of information required to be transferred between each RRH and the central processor (CP) for joint signal processing can easily exceed the capacity of the fronthaul links connecting them. This motivates our study in this paper on a new hybrid decoding scheme where in addition to quantizing and forwarding the received signals for joint decoding at the CP, i.e. forward-and-decode (FaD) as in the conventional CRAN, the RRHs can locally decode-and-forward (DaF) the user messages to save the fronthaul capacity. In particular, we consider the uplink transmission in an orthogonal frequency division multiple access (OFDMA)-based UD-CRAN, where the proposed hybrid decoding is performed on each OFDMA sub-channel (SC). We study a joint optimization of the processing mode selections (DaF or FaD), user-SC assignments and the users' transmit power allocations over all SCs to maximize their weighted-sum-rate subject to the RRHs' individual fronthaul capacity constraints and the users' individual power constraints. Although the problem is non-convex, we propose a Lagrange duality based solution, which can be efficiently computed with good accuracy. Further, we propose a low-complexity greedy algorithm which is shown to achieve close to the optimal performance under practical setups. Simulation results show the promising throughput gains of the proposed designs with hybrid decoding, compared to the existing schemes that perform either DaF or FaD at all SCs.

In wireless networks, communication links may be subject to random fatal attacks: for example, sensor networks under sudden power losses or cognitive radio networks with unpredictable primary user spectrum occupancy. Under such circumstances, it is critical to quantify how fast and reliably information can be collected over attacked links. In our previous work, we studied such channels by considering the single point-to-point case and introduced the notion of dying channels, where a single dying channel is modeled as a random delay-limited channel. In this paper, we extend the single point-to-point dying channel case to the parallel multi-channel case where each sub-channel is a dying channel. According to different attack models, we investigate the outage performance for the parallel dying channels with two setups: (1) the independent-attack case where the attacks on different sub-channels are independent of each other; (2) the m-dependent-attack case where the random attacks on different sub-channels are correlated in such a manner that only the random attacks within m adjacent sub-channels are correlated. For a target sum rate, we further characterize the asymptotic behavior of the outage probabilities for the above two cases. By the central limit theorems for independent and m-dependent sequences, we show that the outage probability diminishes to zero for both cases when the number of sub-channels increases, where the outage exponents are studied to reveal how fast the outage probability improves.

According to the large scale of the fruit planting, orchard anti-theft alarm system based on ZigBee was proposed in this paper. The system could quickly and accurately alarm by infrared sensor and transmit data to monitoring PC with wireless transmission module which made orchard security personnel received alarming message rapidly. System structure chart, hardware circuit and software flow chart were given. The system was able to resolve the problems of orchard anti-theft efficiently for its easy installation and rapidly alarm.

Many nanometer-scale devices have been proposed and fabricated recently. Several can implement threshold and majority logic efficiently. Research has also begun on design methodologies to keep pace with the development of these devices. Specifically, a threshold logic synthesis tool (TELS) and a majority/minority logic synthesis tool (MALS) have been developed recently. In this paper, we discuss several factorization methods to enhance the efficacy of these two tools significantly. We then augment the design methodology to allow the tools to produce totally self-checking (TSC) circuits which can efficiently implement concurrent error detection. Such circuits can be used to detect run-time errors. Two schemes are used to synthesize TSC circuits - one based on the Berger code and the other on the parity code. We compare and contrast these two schemes. Experimental results establish the effectiveness of the proposed approaches.

Intelligent reflecting surface (IRS) can be densely deployed in complex environment to create cascaded line-of-sight (LoS) paths between multiple base stations (BSs) and users via tunable IRS reflections, thereby significantly enhancing the coverage performance of wireless networks. To achieve this goal, it is vital to optimize the deployed locations of BSs and IRSs in the wireless network, which is investigated in this paper. Specifically, we divide the coverage area of the network into multiple non-overlapping cells and decide whether to deploy a BS/IRS in each cell given a total number of BSs/IRSs available. We show that to ensure the network coverage/communication performance, i.e., each cell has a direct/cascaded LoS path with at least one BS, as well as such LoS paths have the average number of IRS reflections less than a given threshold, there is a fundamental trade-off with the deployment cost or the number of BSs/IRSs needed. To optimally characterize this trade-off, we formulate a joint BS and IRS deployment problem based on graph theory, which, however, is difficult to be optimally solved due to the combinatorial optimization involved. To circumvent this difficulty, we first consider a simplified problem with given BS deployment and propose the optimal as well as an efficient suboptimal IRS deployment solution to it, by applying the branch-and-bound method and iteratively removing IRSs from the candidate locations, respectively. Next, an efficient sequential update algorithm is proposed for solving the joint BS and IRS deployment problem. Numerical results are provided to show the efficacy of the proposed design approach and optimization algorithms for the joint BS and IRS deployment. The trade-off between the network coverage performance and the number of deployed BSs/IRSs with different cost ratios is also unveiled.

Intelligent reflecting surface (IRS) is envisioned to play a significant role in future wireless communication systems as an effective means of reconfiguring the radio signal propagation environment. In this paper, we study a new multi-IRS aided massive multiple-input multiple-output (MIMO) system, where a multi-antenna BS transmits independent messages to a set of remote single-antenna users using orthogonal beams that are subsequently reflected by different groups of IRSs via their respective multi-hop passive beamforming over pairwise line-of-sight (LoS) links. We aim to select optimal IRSs and their beam routing path for each of the users, along with the active/passive beamforming at the BS/IRSs, such that the minimum received signal power among all users is maximized. This problem is particularly difficult to solve due to a new type of path separation constraints for avoiding the IRS-reflected signal induced interference among different users. To tackle this difficulty, we first derive the optimal BS/IRS active/passive beamforming solutions based on their practical codebooks given the reflection paths. Then we show that the resultant multi-beam multi-hop routing problem can be recast as an equivalent graph-optimization problem, which is however NP-complete. To solve this challenging problem, we propose an efficient recursive algorithm to partially enumerate the feasible routing solutions, which is able to effectively balance the performance-complexity trade-off. Numerical results demonstrate that the proposed algorithm achieves near-optimal performance with low complexity and outperforms other benchmark schemes. Useful insights into the optimal multi-beam multi-hop routing design are also drawn under different setups of the multi-IRS aided massive MIMO network.

This paper studies a novel mobile relaying technique, where relays of high mobility are employed to assist the communications from source to destination. By exploiting the predictable channel variations introduced by relay mobility, we study the throughput maximization problem in a mobile relaying system via dynamic rate and power allocations at the source and relay. An optimization problem is formulated for a finite time horizon, subject to an information-causality constraint, which results from the data buffering employed at the relay. It is found that the optimal power allocations across the different time slots follow a "stair-case" water filling (WF) structure, with non-increasing and non- decreasing water levels at the source and relay, respectively. For the special case where the relay moves unidirectionally from source to destination, the optimal power allocations reduce to the conventional WF with constant water levels. Numerical results show that with appropriate trajectory design, mobile relaying is able to achieve tremendous throughput gain over the conventional static relaying.

This paper investigates an orthogonal frequency division multiplexing (OFDM)-based wireless powered communication system, where one user harvests energy from an energy access point (EAP) to power its information transmission to a data access point (DAP). The channels from the EAP to the user, i.e., the wireless energy transfer (WET) link, and from the user to the DAP, i.e., the wireless information transfer (WIT) link, vary over both time slots and sub-channels (SCs) in general. To avoid interference at DAP, WET and WIT are scheduled over orthogonal SCs at any slot. Our objective is to maximize the achievable rate at the DAP by jointly optimizing the SC allocation over time and the power allocation over time and SCs for both WET and WIT links. Assuming availability of full channel state information (CSI), the structural results for the optimal SC/power allocation are obtained and an offline algorithm is proposed to solve the problem. Furthermore, we propose a low-complexity online algorithm when causal CSI is available.

Intelligent reflecting surface (IRS) has been deemed as a transformative technology to achieve smart and reconfigurable environment for wireless communication. This letter studies a new IRS-aided communication system, where multiple IRSs assist in the communication between a multi-antenna base station (BS) and a remote single-antenna user by multi-hop signal reflection. Specifically, by exploiting the line-of-sight (LoS) link between nearby IRSs, a multi-hop cascaded LoS link between the BS and user is established where a set of IRSs are selected to successively reflect the BS's signal, so that the received signal power at the user is maximized. To tackle this new problem, we first present the closed-form solutions for the optimal active and cooperative passive beamforming at the BS and selected IRSs, respectively, for a given beam route. Then, we derive the end-to-end channel power, which unveils a fundamental trade-off in the optimal beam routing design between maximizing the multiplicative passive beamforming gain and minimizing the multi-reflection path loss. To reconcile this trade-off, we recast the IRS selection and beam routing problem as an equivalent shortest simple-path problem in graph theory and solve it optimally. Numerical results show significant performance gains of the proposed algorithm over benchmark schemes and also draw useful insights into the optimal beam routing design.