This paper introduces an innovative misbehavior detection (MBD) algorithm designed to identify false sudden stop information within Cooperative Intelligent Transportation Systems (C-ITS) using Collective Perception Messages (CPMs). The algorithm integrates a vehicle perception model and CPM data from neighboring vehicles. Through comprehensive simulations conducted with Omnet++, SUMO, and Veins, we explore the algorithm’s performance across various perception ranges. Additionally, we investigate the impact of potential attacks on the system, analyzing changes in average speed and average delay in travel time. Our results demonstrate the algorithm’s high efficacy in detecting false stop information and offer valuable insights into the relationship between perception range and detection performance. The proposed approach contributes to enhancing the security and safety of C-ITS.

This paper presents an analytical model for the average Packet Error Rate (PER) of Bluetooth in Basic Rate (BR) mode. The effects of the physical layer and the MAC layer are taken into account. A Nakagami-m block fading is considered to take into account the effects of wireless channel fading. A Gaussian Frequency Shift Keying (GFSK) modulation scheme is used. Also, Forward Error Control (FEC) coding effects are taken into account. The interference between the different piconets is captured by a simple Medium Access Control (MAC) layer collision model. An approximation of instantaneous PER is obtained as a first result. Then, a closed-form expression is derived for the PER at the physical layer of Bluetooth. Finally, the overall average PER at the physical and MAC layer of Bluetooth is computed. Extensive simulations are performed to show the accuracy of the obtained results.

We propose a simulation model of Bluetooth (BT) passive scanning for vehicular traffic monitoring. The model is designed to simulate packet detection in the physical and Medium Access Control (MAC) layers for fixed BT sensors. At the physical layer, we consider the radio propagation effects of path loss, small- and large-scale fading. At the MAC layer, we model channel hopping and packet collisions. Associated to a vehicular traffic simulator, our proposal provides a cost-efficient way to generate large-scale BT traffic measurement datasets including both sensor and ground-truth data, avoiding deployment costs. We validate the model by comparing the simulation output to experimental data and we show a simulation use-case of vehicular traffic monitoring in a complex urban context.

We propose a distributed learning algorithm for the resource allocation problem in Device-to-Device (D2D) wireless networks that takes into account the throughput estimation noise. We first formulate a stochastic optimization problem with the objective of maximizing the generalized alpha-fair function of the network. In order to solve it distributively, we then define and use the framework of noisy-potential games. In this context, we propose a Binary Log-linear Learning Algorithm (BLLA), which is distributed across cells and converges to a Nash equilibrium of the resource allocation game. This equilibrium is also an optimal for the resource allocation optimization problem. A key enabler for the analysis of the convergence are the proposed rules for computation of resistance of trees of perturbed Markov chains. The convergence of BLLA is proved for bounded and unbounded noise, with fixed and decreasing temperature parameter. A sufficient number of estimation samples is also provided that guarantees the convergence to an optimal state in a single cell scenario and close to an optimal state in a multi-cell scenario. We assess the performance of BLLA by extensive simulations by considering both bounded and unbounded noise cases and show that BLLA achieves higher sum data rate compared to the state-of-the-art.

Mission-critical communications conveyed over Professional Mobile Radio (PMR) are characterized by a high level of reliability, an improved coverage and group call communications. Hybrid Automatic Repeat on re-Quest (HARQ) schemes are commonly used to provide a reliable communication over multipath noisy wireless channels. They, however, generate an excessive control signaling overhead on the uplink when downlink multicast is considered and groups include many members. In this paper, we propose a simple repetition scheme without request as an alternative to HARQ for group communications. When transport blocks are retransmitted several times, a tradeoff arises between coverage and capacity on the one hand, coverage and delay on the other hand. To evaluate the performance of our scheme, we use a link layer abstraction based on the Mean Instantaneous Capacity (MIC) together with BLER vs. SNR curves in AWGN. We carefully design our repetition scheme by considering the channel characteristics and the delay constraint imposed by video codecs. We show that up to 11 dB gain in SNR is achieved when compared to a scheme without repetition. System level simulations show that cell radius can be multiplied by three in a Multicast/Broadcast Single Frequency Network (MBSFN).

We present a novel approach for distributed load balancing in heterogeneous networks that use cell range expansion (CRE) for user association and almost blank subframe (ABS) for interference management. First, we formulate the problem as a minimization of an α-fairness objective function with load and outage constraints. Depending on α, different objectives in terms of network performance or fairness can be achieved. Next, we model the interactions among the base stations for load balancing as a near-potential game, in which the potential function is the α-fairness function. The optimal pure Nash equilibrium (PNE) of the game is found by using distributed learning algorithms. We propose log-linear and binary log-linear learning algorithms for complete and partial information settings, respectively. We give a detailed proof of convergence of learning algorithms for a near-potential game. We provide sufficient conditions under which the learning algorithms converge to the optimal PNE. By running extensive simulations, we show that the proposed algorithms converge within few hundreds of iterations. The convergence speed in the case of partial information setting is comparable to that of the complete information setting. Finally, we show that outage can be controlled and a better load balancing can be achieved by introducing ABS.

We present a novel approach for distributive load balancing in heterogeneous networks that use cell range expansion (CRE) for user association. First, we formulate the problem as a minimisation of an α-fairness objective function. Depending on α, different objectives in terms of network performance or fairness can be achieved. Next, we model the interactions among the base stations for load balancing as a potential game, in which the potential function is the α-fairness function. The optimal Nash equilibrium of the game is found by using distributed learning algorithms. We use log-linear and binary log-linear learning algorithms for complete and partial information settings, respectively. By running extensive simulations, we show that the proposed algorithms converge within a few tens of iterations. The convergence speed in the case of partial information setting is comparable to that of the complete information setting. We also show that the best response algorithm does not necessarily converge to the optimal Nash equilibrium.

Accurately characterizing the time-varying interference caused to the primary users is essential in ensuring a successful deployment of cognitive radios (CR). We show that the aggregate interference at the primary receiver (PU-Rx) from multiple, randomly located cognitive users (CUs) is well modeled as a shifted lognormal random process, which is more accurate than the lognormal and the Gaussian process models considered in the literature, even for a relatively dense deployment of CUs. It also compares favorably with the asymptotically exact stable and symmetric truncated stable distribution models, except at high CU densities. Our model accounts for the effect of imperfect spectrum sensing, which depends on path-loss, shadowing, and small-scale fading of the link from the primary transmitter to the CU; the interweave and underlay modes of CR operation, which determine the transmit powers of the CUs; and time-correlated shadowing and fading of the links from the CUs to the PU-Rx. It leads to expressions for the probability distribution function, level crossing rate, and average exceedance duration. The impact of cooperative spectrum sensing is also characterized. We validate the model by applying it to redesign the primary exclusive zone to account for the time-varying nature of interference.

Accurately characterizing the time-varying nature of the aggregate interference from secondary users (SUs) is essential in ensuring a successful deployment of a cognitive radio (CR) network. This requires characterizing the probability distribution and the time-varying nature of the aggregate interference from multiple SUs. We show that it is well modeled as a shifted lognormal random process, and is more accurate than the lognormal, Gaussian, and symmetric truncated stable models considered in the literature even for a relatively dense deployment of SUs. Our model accounts for the effect of imperfect spectrum sensing, which depends on path loss, shadowing, and fading of the link from primary transmitter to the SU, and the randomness in the number of SUs and their locations. It also allows for both interweave and underlay modes of CR operation. We also demonstrate the relevance of the proposed analytically tractable model in the design of the primary exclusive zone.