Since broadcast communication is considered the highly appropriate technique for the dissemination of safety messages in vehicular ad hoc networks (VANETs), it is vital to design a high-efficient medium access control (MAC) protocol for vehicles accessing wireless channel. However, the characteristics of VANETs, such as high-velocity vehicles, highly dynamic topology and very unstable communication link, lower the performance of existing MAC protocols. In this paper, we propose a novel Capture-aware TDMA-based MAC (CT-MAC) protocol, which can better utilize the channel resource than the existing MAC protocols by setting the optimal frame length with taking capture effect into account. To obtain the realistically optimal frame length, the closed form expression of probability of capture effect is derived under Nakagami-m fading channel which is proper to model the small-scale fading channel in VANETs. Besides, a discrete Markov chain is introduced to analyze the impact of capture effect on channel utilization. The theoretical analyses and the simulation results show that the reliability of broadcast and the efficiency of channel utilization are significantly improved.

Dynamic framed slotted Aloha algorithm is one of popular passive radio frequency identification U+0028 RFID U+0029 tag anticollision algorithms. In the algorithm, a frame length requires dynamical adjustment to achieve higher identification efficiency. Generally, the adjustment of the frame length is not only related to the number of tags, but also to the occurrence probability of capture effect. Existing algorithms could estimate both the number of tags and the probability of capture effect. Under large-scale RFID tag identification, however, the number of tags would be much larger than an initial frame length. In this scenario, the existing algorithm U+02BC s estimation errors would substantially increase. In this paper, we propose a novel algorithm called capture-aware Bayesian estimate, which adopts Bayesian rules to accurately estimate the number and the probability simultaneously. From numerical results, the proposed algorithm adapts well to the large-scale RFID tag identification. It has lower estimation errors than the existing algorithms. Further, the identification efficiency from the proposed estimate is also higher than the existing algorithms.

How to estimate the number of passive radio frequency identification (RFID) tags and the occurrence probability of capture effect is very important for a dynamic frame length Aloha RFID system with capture effect. The estimation would relate to setting an optimal frame length, which makes tag identification achieve higher efficiency. Under large-scale tags identification environment, the number of tags may be much greater than an initial frame length. In this scenario, existing estimates do not work well. In this letter, we propose a novel estimation method for the large-scale tags identification. The proposed method could adjust the initial frame length matched to the number of tags from only the first several slots in the frame. The advantage of the proposed method is to work better even when the number of tags is much greater. Numerical results show that, the proposed method has lower estimation errors under the large-scale tag identification. After setting an optimal frame length from the estimated results of the proposed method, furthermore, we could obtain higher identification efficiency.

In this paper, we investigate the sum-rate maximization of cellular users (CUs) and device-to-device (D2D) pairs for the D2D communications underlaying cellular networks over the Nakagami-m fading channel. We consider a resource-abundant scenario where the number of CUs is more than that of D2D pairs, and discuss the joint optimization of signal to interference noise ratio (SINR) thresholds and CU-D2D pairing. We formulate the problem as a mixed integer non-linear programming (MINLP), which is NP-hard in general. To solve the problem, we propose a two-step approach. First, we add virtual D2D pairs to make the number of D2D pairs equal to that of CUs, and derive the near-optimal SINR threshold for each possible CU-D2D pairing by the Simulated Annealing (SA) algorithm. Second, the optimal CU-D2D pairing can be obtained by Hungarian (HG) algorithm. Simulation results show that: 1) the proposed SINR policy outperforms the other policies by about 11.3% –63.4%; 2) compared with several state-of-the-art resource allocation policies, HG allocation can achieve an average sum-rate improvement of 10.9% –72.8%.

Vehicular ad hoc networks (VANETs) have recently drawn a large amount of attention because of their enormous potential in road safety improvement and traffic management as well as infotainment service support. As the standard of medium access control (MAC) and physical (PHY) layers for VANETs, IEEE 802.11p has been proposed for more than a decade. Though performance analyses of IEEE 802.11p MAC have been performed, the existing analytical methods still need to be improved. In this paper, to assess the saturated throughput and the average packet delay of IEEE 802.11p MAC in VANETs, a two-dimensional (2-D) Markov model is introduced by considering the capture effect under Nakagami-m fading channel. Moreover, the closed-form expressions of successful transmission, collided transmission, saturated throughput, and average packet delay are carefully derived. Finally, the simulation results are demonstrated to verify the accuracy of the proposed analytical model, which also proves that this analytical model is more precise than the existing ones in terms of saturated throughput and average packet delay.

As a key technology of intelligent transportation systems (ITS), vehicular ad hoc networks (VANETs) have been promising to provide safety and infotainment for drivers and passengers. To support different applications about traffic safety, traffic efficiency, autonomous driving and entertainment, it is important to investigate how to effectively deliver content in VANETs. Since it takes resources such as bandwidth and power for base stations (BSs) or roadside units (RSUs) to deliver content, the optimal pricing strategy for BSs and the optimal caching incentive scheme for RSUs need to be studied. In this paper, a framework of content delivery is proposed first, where each moving vehicle can obtain small-volume content files from either the nearest BS or the nearest RSU according to the competition among them. Then, the profit models for both BSs and RSUs are established based on stochastic geometry and point processes theory. Next, a caching incentive scheme for RSUs based on Stackelberg game is proposed, where both competition sides (i.e., BSs and RSUs) can maximize their own profits. Besides, a backward introduction method is introduced to solve the Stackelberg equilibrium. Finally, the simulation results demonstrate that BSs can obtain their own optimal pricing strategy for maximizing the profit as well as RSUs can obtain the optimal caching scheme with the maximum profit during the content delivery.