Key to the effectiveness of schedule-driven approaches to real-time traffic control is an ability to accurately predict when sensed vehicles will arrive at and pass through the intersection. Prior work in schedule-driven traffic control has assumed a static vehicle arrival model. However, this static predictive model ignores the fact that the queue count and the incurred delay should vary as different partial signal timing schedules (i.e., different possible futures) are explored during the online planning process. In this paper, we propose an alternative arrival time model that incorporates queueing dynamics into this forward search process for a signal timing schedule, to more accurately capture how the intersection’s queues vary over time. As each search state is generated, an incremental queueing delay is dynamically projected for each vehicle. The resulting total queueing delay is then considered in addition to the cumulative delay caused by signal operations. We demonstrate the potential of this approach through microscopic traffic simulation of a real-world road network, showing a 10-15% reduction in average wait times over the schedule-driven traffic signal control system in heavy traffic scenarios.

Recent work in decentralized, schedule-driven traffic control has demonstrated the ability to improve the efficiency of traffic flow in complex urban road networks. In this approach, a scheduling agent is associated with each intersection. Each agent senses the traffic approaching its intersection and in real-time constructs a schedule that minimizes the cumulative wait time of vehicles approaching the intersection over the current look-ahead horizon. In this paper, we propose a cooperative algorithm that utilizes both connected and autonomous vehicles (CAV) and schedule-driven traffic control to create better traffic flow in the city. The algorithm enables an intersection scheduling agent to adjust the arrival time of an approaching platoon through use of wireless communication to control the velocity of vehicles. The sequence of approaching platoons is thus shifted toward a new shape that has smaller cumulative delay. We demonstrate how this algorithm outperforms the original approach in a real-time traffic signal control problem.

We consider the problem of minimizing the the delay of jobs moving through a directed graph of service nodes. In this problem, each node may have several links and is constrained to serve one link at a time. As jobs move through the network, they can pass through a node only after they have been serviced by that node. The objective is to minimize the delay jobs incur sitting on queues waiting to be serviced. Two popular approaches to this problem are backpressure algorithm and schedule-driven control. In this paper, we present a hybrid approach of those two methods that incorporates the stability of queuing theory into the schedule-driven control. We then demonstrate how this hybrid method outperforms the other two in a real-time traffic signal control problem, where the nodes are traffic lights, the links are roads, and the jobs are vehicles. We show through simulations that, in scenarios with heavy congestion, the hybrid method results in 50% and 15% reductions in delay over schedule-driven control and backpressure respectively. A theoretical analysis also justifies our results.

The multicast/broadcast service (MBS) has emerged as a key technology for delivering multimedia contents in wireless networks.The development of orthogonal frequency division multiplexing access (OFDMA) and multiple-input multiple-output (MIMO) communications have provided a high-throughput air interface for delivering the multimedia contents in MBSs. To further improve the spectral efficiency for MIMO communications, the technique of precoding can be applied to tailor the signals to the current downlink channel quality. However, due to the broadcasting nature of MBSs, link adaptation and precoder selection for individual user is infeasible. A fixed precoding scheme where the transmitter simply uses identical precoding matrices is commonly adopted for MBS transmissions in existing systems. In this paper, we propose an effective adaptive precoder selection scheme with low complexity based on precoder training and user feedback. Simulation results show that the proposed scheme effectively improves the performance of received SINR measured at the mobile stations (MSs).

In this paper, an accurate performance analysis is proposed for type-II hybrid ARQ scheme. As opposed to the existing type-II hybrid ARQ analysis work in the literature, the analysis proposed in this paper takes the dependency of the successive decoding attempts into account. The proposed analysis is applicable for type-II hybrid ARQ schemes with known weight spectrum of the error-correcting codes used. The channel models considered include AWGN and Rayleigh fading. Numerical experiments show that the proposed analysis is accurate in capturing the performance of type-II hybrid ARQ systems at mid-to-high SNR region.