Publications

Enabling high-rate, low-latency and ultra-reliable wireless communications between UAVs and their associated ground pilots/users is of paramount importance to realize their large-scale usage in the future. To achieve this goal, cellular- connected UAV, whereby UAVs for various applications are integrated into the cellular network as new aerial users, is a promising technology that has drawn significant attention recently. Compared to conventional cellular communication with terrestrial users, cellular-connected UAV communication possesses substantially different characteristics that present new research challenges as well as opportunities. In this article, we provide an overview of this emerging technology, by first discussing its potential benefits, unique communication and spectrum requirements, as well as new design considerations. We then introduce promising technologies to enable the future generation of 3D heterogeneous wireless networks with coexisting aerial and ground users. Last, we present simulation results to corroborate our discussions and highlight key directions for future research.

Six-dimensional movable antenna (6DMA) is an emerging technology that is able to fully exploit the spatial variation of wireless channels by controlling the 3D positions and 3D rotations of distributed antennas/antenna surfaces at the transmitter/receiver. In this letter, we apply 6DMA at the base station (BS) to enhance its wireless sensing performance over a given set of regions. To this end, we first divide each region into a number of equal-size subregions and select one typical target location within each subregion. Then, we derive an expression for the Cramer-Rao bound (CRB) for estimating the directions of arrival (DoAs) from these typical target locations in all regions, which sheds light on the sensing performance of 6DMA enhanced systems in terms of a power gain and a geometric gain. Next, we minimize the CRB for DoA estimation via jointly optimizing the positions and rotations of all 6DMAs at the BS, subject to practical movement constraints, and propose an efficient algorithm to solve the resulting non-convex optimization problem sub-optimally. Finally, simulation results demonstrate the significant improvement in DoA estimation accuracy achieved by the proposed 6DMA sensing scheme as compared to various benchmark schemes, for both isotropic and directive antenna radiation patterns.

This paper selects per capita GDP of Jiangsu Province from 2000 to 2020 as the indicator of economic development, and uses chemical oxygen demand (COD) emissions, sulfur dioxide (SO2) emissions, general industrial solid waste generation, and hazardous waste generation as indicators of environmental pollution. Based on the theoretical foundation of the Environmental Kuznets Curve (EKC), the paper conducts an empirical analysis of the relationship between economic development and environmental pollution using Eviews software. The results show that:(1) The EKC for SO2 emissions fits an inverted N-shaped curve and has surpassed the second inflection point of this curve.(2) The EKC for general industrial solid waste generation fits an inverted U-shaped curve and has not yet reached its inflection point.(3) The EKC for hazardous waste generation fits a U-shaped curve and has surpassed the inflection point of this curve.

Due to the constraints of satellite platform and inter-satellite links, navigation satellite network has a characteristic of time-division multiplexing. Since the information exchange in time division-multiplexing navigation satellite network is very complex, it is generally necessary to use network simulation software to evaluate the design of navigation satellite network protocol. In this paper, the time-division multiplexing navigation satellite network is modeled based on the OPNET network simulation software. The satellite network is modeled from three levels: network domain, node domain and process domain. Also, the process models of link layer, network layer and transport layer are designed and described, respectively. Finally, the simulation results of network throughput with regard to different sliding window sizes and packet drop rates are given. It can be seen from the simulation results that the proposed simulation model of time-division multiplexing navigation satellite network based on OPNET is sufficient to evaluation the network performance.

Multiple-input multiple-output (MIMO) or multi-antenna communication is a key technique to achieve high spectral efficiency in wireless systems. For the point-to-point MIMO channel, it is a well-known result that the channel singular value decomposition (SVD) based linear precoding and decoding achieves the channel capacity, which also diagonalizes the MIMO channel into parallel single-input single-output (SISO) sub-channels for independent encoding and decoding. However, in multi-user MIMO systems, the optimal transmit covariance of each MIMO link is generally not its channel SVD based as a result of the control and balance of the co-channel interference among users. Thus, it remains unknown whether the linear precoding/decoding strategy is still able to achieve the capacity of each MIMO link and yet diagonalize its MIMO channel, with a given set of optimal transmit covariance of all users. This letter solves this open problem by providing a closed-form capacity-achieving linear precoder/decoder design that diagonalizes a MIMO channel with arbitrary transmit covariance. Numerical examples are also provided to validate the proposed solution in various multi-user MIMO systems.

Consider a wireless network that has two tiers with different priorities: a primary tier vs. a secondary tier, which is an emerging network scenario with the advancement of cognitive radio technologies. The primary tier consists of randomly distributed legacy nodes of density $n$, which have an absolute priority to access the spectrum. The secondary tier consists of randomly distributed cognitive nodes of density $m=n^\beta$ with $\beta\geq 2$, which can only access the spectrum opportunistically to limit the interference to the primary tier. Based on the assumption that the secondary tier is allowed to route the packets for the primary tier, we investigate the throughput and delay scaling laws of the two tiers in the following two scenarios: i) the primary and secondary nodes are all static; ii) the primary nodes are static while the secondary nodes are mobile. With the proposed protocols for the two tiers, we show that the primary tier can achieve a per-node throughput scaling of $\lambda_p(n)=\Theta(1/\log n)$ in the above two scenarios. In the associated delay analysis for the first scenario, we show that the primary tier can achieve a delay scaling of $D_p(n)=\Theta(\sqrt{n^\beta\log n}\lambda_p(n))$ with $\lambda_p(n)=O(1/\log n)$. In the second scenario, with two mobility models considered for the secondary nodes: an i.i.d. mobility model and a random walk model, we show that the primary tier can achieve delay scaling laws of $\Theta(1)$ and $\Theta(1/S)$, respectively, where $S$ is the random walk step size. The throughput and delay scaling laws for the secondary tier are also established, which are the same as those for a stand-alone network.

Intelligent reflecting surface (IRS) is an emerging technology to enhance the spectral and energy efficiency of wireless communications cost-effectively. This letter considers a new multi-IRS aided wireless network where a cascaded line-of-sight (LoS) link is established between the base station (BS) and a remote user by leveraging the multi-hop signal reflection of selected IRSs. As compared to the conventional single-/double-hop IRS system, multi-hop IRS system provides more pronounced path diversity and cooperative passive beamforming gains, especially in the environment with dense obstacles. However, a more challenging joint active/passive beamforming and multi-hop beam routing problem also arises for maximizing the end-to-end channel gain. Furthermore, the number of IRS-associated channel coefficients increases drastically with the number of IRS hops. To tackle the above issues, in this letter we propose a new and efficient beam training based solution by considering the use of practical codebook-based BS/IRS active/passive beamforming without the need of explicit channel estimation. Instead of exhaustively or sequentially searching over all combinations of active and passive beam patterns for each beam route, a distributed beam training scheme is proposed to reduce the complexity, by exploiting the (nearly) time-invariant BS-IRS and inter-IRS channels and the cooperative training among the BS and IRSs' controllers. Simulation results show that our proposed design achieves the end-to-end channel gain close to that of the sequential beam search, but at a much lower training overhead and complexity.

In cognitive radio (CR) communication systems, it is challenging to achieve high rate for the secondary user (SU) in the presence of strong cross-link interference with the primary user (PU). In this letter, we exploit the recently proposed intelligent reflecting surface (IRS) to tackle this problem. Specifically, we investigate an IRS-assisted CR 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 (SINR) target for the PU link, by jointly optimizing the SU transmit power and IRS reflect beamforming. Although 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, as well as some heuristic designs of lower complexity. Simulation results show that IRS is able to significantly improve the SU rate, even for the scenarios deemed highly challenging in conventional CR systems without the IRS.

This paper considers the use of energy harvesters, instead of conventional time-invariant energy sources, in wireless cooperative communication. For the purpose of exposition, we study the classic three-node Gaussian relay channel with decode-and-forward (DF) relaying, in which the source and relay nodes transmit with power drawn from energy-harvesting (EH) sources. Assuming a deterministic EH model under which the energy arrival time and the harvested amount are known prior to transmission, the throughput maximization problem over a finite horizon of $N$ transmission blocks is investigated. In particular, two types of data traffic with different delay constraints are considered: delay-constrained (DC) traffic (for which only one-block decoding delay is allowed at the destination) and no-delay-constrained (NDC) traffic (for which arbitrary decoding delay up to $N$ blocks is allowed). For the DC case, we show that the joint source and relay power allocation over time is necessary to achieve the maximum throughput, and propose an efficient algorithm to compute the optimal power profiles. For the NDC case, although the throughput maximization problem is non-convex, we prove the optimality of a separation principle for the source and relay power allocation problems, based upon which a two-stage power allocation algorithm is developed to obtain the optimal source and relay power profiles separately. Furthermore, we compare the DC and NDC cases, and obtain the sufficient and necessary conditions under which the NDC case performs strictly better than the DC case. It is shown that NDC transmission is able to exploit a new form of diversity arising from the independent source and relay energy availability over time in cooperative communication, termed "energy diversity", even with time-invariant channels.

This paper studies the transmission strategy for a cognitive radio (CR) that operates under spectrum sharing with a primary radio (PR). It is assumed that the CR transmitter is equipped with multi-antennas and thereby transmit beamforming and power control are jointly deployed to balance between the interference avoidance at the PR terminals and the throughput maximization of the CR link. We name such an operation as cognitive beamforming (CB). Unlike the prior study on CB that assumes perfect knowledge on the interference channels over which the CR transmitter interferes with the PR terminals, in this paper we remove this assumption and propose a practical CB scheme by utilizing a new idea of effective interference channel, which can be efficiently learned/estimated at the CR transmitter from the received PR signals. Interestingly, it is shown that the proposed CB scheme based on the effective interference channel achieves a capacity gain for the CR over the conventional scheme based on the exact channel knowledge, when the PR terminals are also equipped with multi-antennas but do not operate in a full spatial multiplexing mode. Furthermore, due to the finite learning time for the effective interference channel, we show that there exists a general learning-throughput tradeoff associated with the practical CB, for which we determine the optimal learning time to maximize the CR link throughput.

The applications of wireless power transfer technology to wireless communications can help build a wireless powered communication network (WPCN) with more reliable and sustainable power supply compared to the conventional battery-powered network. However, due to the fundamental differences in wireless information and power transmissions, many important aspects of conventional battery-powered wireless communication networks need to be redesigned for efficient operations of WPCNs. In this paper, we study the placement optimization of energy and information access points in WPCNs, where the wireless devices (WDs) harvest the radio frequency energy transferred by dedicated energy nodes (ENs) in the downlink, and use the harvested energy to transmit data to information access points (APs) in the uplink. In particular, we are interested in minimizing the network deployment cost with minimum number of ENs and APs by optimizing their locations, while satisfying the energy harvesting and communication performance requirements of the WDs. Specifically, we first study the minimum-cost placement problem when the ENs and APs are separately located, where an alternating optimization method is proposed to jointly optimize the locations of ENs and APs. Then, we study the placement optimization when each pair of EN and AP are co-located and integrated as a hybrid access point, and propose an efficient algorithm to solve this problem. Simulation results show that the proposed methods can effectively reduce the network deployment cost and yet guarantee the given performance requirements, which is a key consideration in the future applications of WPCNs.

Energy beamforming (EB) is a key technique for achieving efficient radio-frequency (RF) transmission enabled wireless energy transfer (WET). By optimally designing the waveforms from multiple energy transmitters (ETs) over the wireless channels, they are constructively combined at the energy receiver (ER) to achieve an EB gain that scales with the number of ETs. However, the optimal design of transmit waveforms requires accurate channel state information (CSI) at the ETs, which is challenging to obtain in practical WET systems. In this paper, we propose a new channel training scheme to achieve optimal EB gain in a distributed WET system, where multiple separated ETs adjust their transmit phases to collaboratively send power to a single ER in an iterative manner, based on one-bit feedback from the ER per training interval which indicates the increase/decrease of the received power level from one particular ET over two preassigned transmit phases. The proposed EB algorithm can be efficiently implemented in practical WET systems even with a large number of distributed ETs, and is analytically shown to converge quickly to the optimal EB design as the number of feedback intervals per ET increases. Numerical results are provided to evaluate the performance of the proposed algorithm as compared to other distributed EB designs.