Search

State-of-the-art drone technologies have severe flight time limitations due to weight constraints, which inevitably lead to a relatively small amount of available energy. Therefore, frequent battery replacement or recharging is necessary in applications such as delivery, exploration, or support to the wireless infrastructure. Mobile charging stations (i.e., mobile stations with charging equipment) for outdoor ad-hoc battery charging is one of the feasible solutions to address this issue. However, the ability of these platforms to charge the drones is limited in terms of the number and charging time. This paper designs an auction-based mechanism to control the charging schedule in multi-drone setting. In this paper, charging time slots are auctioned, and their assignment is determined by a bidding process. The main challenge in developing this framework is the lack of prior knowledge on the distribution of the number of drones participating in the auction. Based on optimal second-price-auction, the proposed formulation, then, relies on deep learning algorithms to learn such distribution online. Numerical results from extensive simulations show that the proposed deep-learning-based approach provides effective battery charging control in multi-drone scenarios.

Intelligent reflecting surfaces (IRS) have been proposed as a revolutionary technology owing to its capability of adaptively reconfiguring the propagation environment in a cost-effective and hardware-efficient fashion. While the application of IRS as a passive reflector to enhance the performance of wireless communications has been widely investigated in the literature, using IRS as a passive transmitter recently is emerging as a new concept and attracting steadily growing interest. In this paper, we propose two novel IRS-based passive information transmission systems using advanced symbol-level precoding. One is a standalone passive information transmission system, where the IRS operates as a passive transmitter serving multiple receivers by adjusting its elements to reflect unmodulated carrier signals. The other is a joint passive reflection and information transmission system, where the IRS not only enhances transmissions for multiple primary information receivers (PIRs) by passive reflection, but also simultaneously delivers additional information to a secondary information receiver (SIR) by embedding its information into the primary signals at the symbol level. Two typical optimization problems, i.e., power minimization and quality-of-service (QoS) balancing, are investigated for the proposed IRS-based passive information transmission systems. Simulation results demonstrate the feasibility of IRS-based passive information transmission and the effectiveness of our proposed algorithms, as compared to other benchmark schemes.

In this paper we investigate energy-optimized wireless video transmission employing a hybrid automatic repeat request. We formulate the problem as maximizing the video quality, subject to a constraint on the wireless transmission energy consumption. We consider multiple parameters in multiple layers in a wireless video transmission system: Transmit power, alphabet size, FEC code rate, maximum number of transmissions, and unequal video data importance. An analytical framework is proposed to include these parameters, which allows us to divide this problem into two sub-problems: data transmission and unequal error protection. The problem is tackled by solving the two sub-problems, which are done by exhaustive search and convex optimization, respectively. Simulations of different videos show that the proposed scheme outperforms methods using conventional data transmission and/or unequal error protection.

In this paper we investigate energy-optimized wireless video transmission employing a hybrid automatic repeat request. We formulate the problem as maximizing the video quality, subject to a constraint on the wireless transmission energy consumption. We consider multiple parameters in multiple layers in a wireless video transmission system: Transmit power, alphabet size, FEC code rate, maximum number of transmissions, and unequal video data importance. An analytical framework is proposed to include these parameters, which allows us to divide this problem into two sub-problems: data transmission and unequal error protection. The problem is tackled by solving the two sub-problems, which are done by exhaustive search and convex optimization, respectively. Simulations of different videos show that the proposed scheme outperforms methods using conventional data transmission and/or unequal error protection.

With the attractive feature of guaranteeing maximum network throughput, backpressure routing has been widely used in wireless communication networks. Motivated by the backpressure routing, in this paper, we propose a delay-based traffic signal control algorithm in transportation networks. We prove that this delay-based control achieves optimal throughput performance, same as the queue-based traffic signal control in the literature. However, a vehicle at a lane whose queue length remains very small may be excessively delayed under the queue-based signal control. Our delay-based backpressure control can deal with the excessive delays and achieve better fairness with respect to delay while still guaranteeing throughput optimality. Moreover, a general weighted control scheme combining the queue-based and delay-based schemes is also investigated, to provide a more flexible control according to the quality of service requirements. Numerical results explore their performance under both homogeneous and heterogeneous traffic scenarios.

With the attractive feature of guaranteeing maximum network throughput, backpressure routing has been widely used in wireless communication networks. Motivated by the backpressure routing, in this paper, we propose a delay-based traffic signal control algorithm in transportation networks. We prove that this delay-based control achieves optimal throughput performance, same as the queue-based traffic signal control in the literature. However, a vehicle at a lane whose queue length remains very small may be excessively delayed under the queue-based signal control. Our delay-based backpressure control can deal with the excessive delays and achieve better fairness with respect to delay while still guaranteeing throughput optimality. Moreover, a general weighted control scheme combining the queue-based and delay-based schemes is also investigated, to provide a more flexible control according to the quality of service requirements. Numerical results explore their performance under both homogeneous and heterogeneous traffic scenarios.

In this paper, we propose to use vehicular ad hoc networks (VANETs) to collect and aggregate real-time speed and position information on individual vehicles to optimize signal control at traffic intersections. We first formulate the vehicular traffic signal control problem as a job scheduling problem on processors, with jobs corresponding to platoons of vehicles. Under the assumption that all jobs are of equal size, we give an online algorithm, referred to as the oldest job first (OJF) algorithm, to minimize the delay across the intersection. We prove that the OJF algorithm is 2-competitive, implying that the delay is less than or equal to twice the delay of an optimal offline schedule with perfect knowledge of the arrivals. We then show how a VANET can be used to group vehicles into approximately equal-sized platoons, which can then be scheduled using OJF. We call this the two-phase approach, where we first group the vehicular traffic into platoons and then apply the OJF algorithm, i.e., the oldest arrival first (OAF) algorithm. Our simulation results show that, under light and medium traffic loads, the OAF algorithm reduces the delays experienced by vehicles as they pass through the intersection, as compared with vehicle-actuated methods, Webster's method, and pretimed signal control methods. Under heavy vehicular traffic load, the OAF algorithm performs the same as the vehicle-actuated traffic method but still produces lower delays, as when compared with Webster's method and the pretimed signal control method. © 2012 IEEE.

In this paper, we propose to use vehicular ad hoc networks (VANETs) to collect and aggregate real-time speed and position information on individual vehicles to optimize signal control at traffic intersections. We first formulate the vehicular traffic signal control problem as a job scheduling problem on processors, with jobs corresponding to platoons of vehicles. Under the assumption that all jobs are of equal size, we give an online algorithm, referred to as the oldest job first (OJF) algorithm, to minimize the delay across the intersection. We prove that the OJF algorithm is 2-competitive, implying that the delay is less than or equal to twice the delay of an optimal offline schedule with perfect knowledge of the arrivals. We then show how a VANET can be used to group vehicles into approximately equal-sized platoons, which can then be scheduled using OJF. We call this the two-phase approach, where we first group the vehicular traffic into platoons and then apply the OJF algorithm, i.e., the oldest arrival first (OAF) algorithm. Our simulation results show that, under light and medium traffic loads, the OAF algorithm reduces the delays experienced by vehicles as they pass through the intersection, as compared with vehicle-actuated methods, Webster's method, and pretimed signal control methods. Under heavy vehicular traffic load, the OAF algorithm performs the same as the vehicle-actuated traffic method but still produces lower delays, as when compared with Webster's method and the pretimed signal control method. © 2012 IEEE.

In this paper, we consider a dual-hop Multiple Input Multiple Output (MIMO)relay wireless network, in which a source-destination pair both equipped withmultiple antennas communicates through a large number of half-duplexamplify-and-forward (AF) relay terminals. Two novel linear beamforming schemesbased on the matched filter (MF) and regularized zero-forcing (RZF) precodingtechniques are proposed for the MIMO relay system. We focus on the linearprocess at the relay nodes and design the new relay beamformers by utilizingthe channel state information (CSI) of both backward channel and forwardchannel. The proposed beamforming designs are based on the QR decomposition(QRD) filter at the destination node which performs successive interferencecancellation (SIC) to achieve the maximum spatial multiplexing gain. Simulationresults demonstrate that the proposed beamformers that fulfil both theintranode array gain and distributed array gain outperform other relayingschemes under different system parameters in terms of the ergodic capacity.

In this paper, we consider a dual-hop Multiple Input Multiple Output (MIMO)relay wireless network, in which a source-destination pair both equipped withmultiple antennas communicates through a large number of half-duplexamplify-and-forward (AF) relay terminals. Two novel linear beamforming schemesbased on the matched filter (MF) and regularized zero-forcing (RZF) precodingtechniques are proposed for the MIMO relay system. We focus on the linearprocess at the relay nodes and design the new relay beamformers by utilizingthe channel state information (CSI) of both backward channel and forwardchannel. The proposed beamforming designs are based on the QR decomposition(QRD) filter at the destination node which performs successive interferencecancellation (SIC) to achieve the maximum spatial multiplexing gain. Simulationresults demonstrate that the proposed beamformers that fulfil both theintranode array gain and distributed array gain outperform other relayingschemes under different system parameters in terms of the ergodic capacity.