Publications

Cooperative communications has long been proposed as an effective method for reducing the energy consumption of the mobile terminals (MTs) in wireless cellular networks. However, it is hard to implement due to the lack of incentives for the MTs to cooperate. In this paper, we propose a pricing mechanism to incentivize the uplink cooperative communications for the energy saving of MTs. We first consider the ideal case of MTs' full cooperation under complete information. For this scenario as the benchmark case, where the private information of the helping MTs such as the channel and battery conditions is completely known by the source MT, the problem is formulated as a relay selection problem. Then, for the practical case of partial cooperation with incomplete information, the MTs need to cooperate under the uncertainties of the helping MTs' channel and battery conditions. For this scenario, we propose a partial cooperation scheme with pricing where a source MT in low-battery level or bad channel condition is allowed to select and pay another MT in proximity to help forward its data to the base station (BS). We formulate the source MT's pricing and load sharing problem as an optimization problem. Efficient algorithms based on dichotomous search and alternative optimization are proposed to solve the problem for the cases of splittable and nonsplittable data at the source MT, respectively. Finally, extensive numerical results are provided to show that our proposed cooperative communications scheme with pricing can significantly decrease both the communications and battery outages for the MTs, and can also increase the average battery level during the MTs' operation.

In this paper, we consider a UAV-enabled wireless sensor network (WSN), where a UAV is dispatched to collect data from multiple sensor nodes (SNs) at known locations within a given duration. In urban areas, the signal propagation between the UAV and SNs can be occasionally blocked (or at least severely attenuated) by high and dense buildings. To address this issue, we first establish a probabilistic line-of-sight (LoS) channel model for a Manhattan-type city by using simulation and data regression methods, which is shown in the form of a generalized logistic function of the UAV-SN elevation angle. Based on the obtained channel model, an off-line optimization problem is then formulated to maximize the minimum expected (average) data-collection rate from all SNs by jointly designing the UAV three-dimensional (3D) trajectory and transmission scheduling of SNs. Since the expected rate is a highly complex function of the 3D UAV trajectory, we approximate it by a tractable lower bound based on the dominant rate in the LoS channel state. The resultant optimization problem is still non-convex and thus difficult to solve. As such, we further propose an efficient algorithm to solve it sub- optimally by applying the techniques of block coordination descent and successive convex approximation. Simulation results are presented to demonstrate the effectiveness of the proposed algorithm and reveal useful properties of the optimized 3D UAV trajectory for data harvesting in WSNs.

Intelligent reflecting surface (IRS) has emerged as an enabling technology to achieve smart and reconfigurable wireless communication environment cost-effectively. Prior works on IRS mainly consider its passive beamforming design and performance optimization without the inter-IRS signal reflection, which thus do not unveil the full potential of multi-IRS assisted wireless networks. In this paper, we study a double-IRS assisted multi-user communication system with the \emph{cooperative} passive beamforming design that captures the multiplicative beamforming gain from the inter-IRS channel. Under the general channel setup with the co-existence of both double- and single-reflection links, we jointly optimize the (active) receive beamforming at the base station (BS) and the cooperative (passive) reflect beamforming at the two distributed IRSs (deployed near the BS and users, respectively) to maximize the minimum signal-to-interference-plus-noise ratio (SINR) of all users. Moreover, for the single-user and multi-user setups, we analytically show the superior performance of the double-IRS cooperative system over the conventional single-IRS system in terms of the maximum signal-to-noise ratio (SNR) and multi-user effective channel rank, respectively. Simulation results validate our analytical results and show the practical advantages of the proposed double-IRS system with cooperative passive beamforming designs.

The characteristics of the typical microstrip discontinuities, such as right-angle bends, T junctions and steps in width, are investigated based on the Finite-Difference Time-Domain (FDTD) method in a wide-frequency range. The compensation methods of the microstrip discontinuities are also discussed and compensated discontinuities can improve the circuit performance as well as the bandwidth compared with the uncompensated. The electric field varying with signal traveling in the spatial and time domain at the microstrip discontinuities are also presented.

Intelligent reflecting surface (IRS) has emerged as a promising technique to control wireless propagation environment for enhancing the communication performance cost-effectively. However, the rapidly time-varying channel in high-mobility communication scenarios such as vehicular communication renders it challenging to obtain the instantaneous channel state information (CSI) efficiently for IRS with a large number of reflecting elements. In this paper, we propose a new roadside IRS-aided vehicular communication system to tackle this challenge. Specifically, by exploiting the symmetrical deployment of IRSs with inter-laced equal intervals on both sides of the road and the cooperation among nearby IRS controllers, we propose a new two-stage channel estimation scheme with off-line and online training, respectively, to obtain the static/time-varying CSI required by the proposed low-complexity passive beamforming scheme efficiently. The proposed IRS beamforming and online channel estimation designs leverage the existing uplink pilots in wireless networks and do not require any change of the existing transmission protocol. Moreover, they can be implemented by each of IRS controllers independently, without the need of any real-time feedback from the user's serving BS. Simulation results show that the proposed designs can efficiently achieve the high IRS passive beamforming gain and thus significantly enhance the achievable communication throughput for high-speed vehicular communications.

In conventional terrestrial cellular networks, mobile terminals (MTs) at the cell edge often pose a performance bottleneck due to their long distances from the serving ground base station (GBS), especially in hotspot period when the GBS is heavily loaded. This paper proposes a new hybrid network architecture by leveraging the use of unmanned aerial vehicle (UAV) as an aerial mobile base station, which flies cyclically along the cell edge to offload data traffic for cell-edge MTs. We aim to maximize the minimum throughput of all MTs by jointly optimizing the UAV's trajectory, bandwidth allocation and user partitioning. We first consider orthogonal spectrum sharing between the UAV and GBS, and then extend to spectrum reuse where the total bandwidth is shared by both the GBS and UAV with their mutual interference effectively avoided. Numerical results show that the proposed hybrid network with optimized spectrum sharing and cyclical multiple access design significantly improves the spatial throughput over the conventional GBS-only network; while the spectrum reuse scheme provides further throughput gains at the cost of slightly higher complexity for interference control. Moreover, compared to the conventional small-cell offloading scheme, the proposed UAV offloading scheme is shown to outperform in terms of throughput, besides saving the infrastructure cost.

Aerial-ground interference mitigation has been deemed as the main challenge in realizing cellular-connected unmanned aerial vehicle (UAV) communications. Due to the line-of-sight (LoS)-dominant air-ground channels, the UAV generates/suffers much stronger interference to/from cellular base stations (BSs) over a much larger region in its uplink/downlink communication, as compared to the terrestrial users. As a result, conventional inter-cell interference coordination (ICIC) techniques catered for terrestrial networks become ineffective in mitigating the more severe UAV-induced interference. To deal with this new challenge, this letter introduces a cognitive radio based solution by treating the UAV and terrestrial users as secondary and primary users in the network, respectively. In particular, the LoS channels with terrestrial BSs/users endow the UAV with a powerful spectrum sensing capability for detecting the terrestrial signals over a much larger region than its serving BS. By exploiting this unique feature, we propose a new UAV-sensing-assisted ICIC design for both the UAV downlink and uplink communications. Specifically, the UAV senses its received interference and the transmissions of terrestrial users in the downlink and uplink, respectively, over the resource blocks (RBs) available at its serving BS to assist its RB allocation to the UAV for avoiding the interference with co-channel terrestrial communications. Numerical results demonstrate that the proposed UAV-assisted ICIC outperforms the conventional terrestrial ICIC by engaging the neighboring BSs for cooperation only.

In cognitive radio (CR) networks, there are scenarios where the secondary (lower priority) users intend to communicate with each other by opportunistically utilizing the transmit spectrum originally allocated to the existing primary (higher priority) users. For such a scenario, a secondary user usually has to tradeoff between two conflicting goals at the same time: one is to maximize its own transmit throughput; and the other is to minimize the amount of interference it produces at each primary receiver. In this paper, we study this fundamental tradeoff from an information-theoretic perspective by characterizing the secondary user's channel capacity under both its own transmit-power constraint as well as a set of interference-power constraints each imposed at one of the primary receivers. In particular, this paper exploits multi-antennas at the secondary transmitter to effectively balance between spatial multiplexing for the secondary transmission and interference avoidance at the primary receivers. Convex optimization techniques are used to design algorithms for the optimal secondary transmit spatial spectrum that achieves the capacity of the secondary transmission. Suboptimal solutions for ease of implementation are also presented and their performances are compared with the optimal solution. Furthermore, algorithms developed for the single-channel transmission are also extended to the case of multichannel transmission whereby the secondary user is able to achieve opportunistic spectrum sharing via transmit adaptations not only in space, but in time and frequency domains as well. Simulation results show that even under stringent interference-power constraints, substantial capacity gains are achievable for the secondary transmission by employing multi-antennas at the secondary transmitter. This is true even when the number of primary receivers exceeds that of secondary transmit antennas in a CR network, where an interesting "interference diversity" effect can be exploited.

Session Initiation Protocol (SIP) plays an important role in VoIP, peer-to-peer (P2P) network has been used as virtual server in SIP, and however, it is difficult to authenticate with each other and to exchange session key because of missing central authority (CA). In this paper, the security threats in Peer-to-Peer SIP (P2PSIP) architecture are analyzed, then a new P2PSIP authenticated key exchange mechanism based on Self-Generated-Certificate Public Key Encryption scheme (SGC-PKE) is proposed, which assures the Integrity, Confidentiality and Non-repudiation of messages in P2PSIP network, and overcomes some shortcomings from traditional public key cryptography (PKC) system.

When measuring the position and orientation of some special kind of mobile vehicles, such as agricultural machinery, mining equipment by using the Strap-down Inertial Navigation System (SINS), the performance accuracy of the SINS will be heavily affected by the vibration errors generated from the vehicles. This paper presents a new methodology to compensate vibration error of a mobile vehicle by using a SINS. The method is based on a newly derived coning/sculling error models according to the vibration form of the mobile vehicle. Firstly, coning/sculling error models were derived by modeling the angular vibration and linear vibration of the mobile vehicle as sines and cosines signal. Then, SINS vibration error was compensated by using the model based three-sample and four-sample algorithms respectively. During the experiment, a SINS was mounted on agricultural machinery which is used to fertilize fields. The experimental results indicated that the resulted vibration error such as the coning error and sculling error by using four-sample algorithm is 29.8% and 28.3% lower than the errors by using three-sample algorithm. Nevertheless, both algorithms are able to significantly decrease the vibration error (64.5% and 74.7% less coning error and 37.5% and 69.6% less sculling error than without compensation) of agricultural machinery by using SINS.

In this paper, we consider a multi-unmanned aerial vehicle (UAV) relaying system where the UAVs are deployed to assist data relaying from multiple ground source nodes to their corresponding destination nodes. An optimization problem is formulated to maximize the minimum achievable rate among all pairs of nodes, by jointly designing the UAV placement and communication resource allocation. To solve this non-convex problem, we first reformulate it into two sub-problems, corresponding to a slave problem for the resource allocation given fixed UAV placement and a master problem for the UAV placement optimization. Then for the non-convex slave problem, we sub-optimally solve it by using the successive convex approximation method. The master problem, however, is intractable due to the lack of a closed-form expression for the max-min rate with respect to the UAV placement. We thus propose a new solution approach, called Gibbs-sampling-based (GSB) placement learning, to gradually learn a sub-optimal UAV placement by generating a sequence of samples for the UAV placement that constitute a Markov chain, where the transition probabilities are determined by the max-min rates of different configurations of UAV placement. Furthermore, a high-quality UAV-placement initialization scheme is proposed to accelerate the convergence speed of the proposed GSB algorithm. Numerical results are presented to demonstrate the significant rate improvement and fast convergence speed of the proposed scheme as compared to various benchmark schemes.