Performance Analysis of Receivers using Sector Antennas for Broadcast Vehicular Communications

In this paper, we analyze a carrier-sense multiple access (CSMA) system with all-to-all broadcast data traffic to assess the performance gain obtained by using multiple sector antennas and a receiver setup that can decode multiple packets simultaneously when packets arrive in narrow angle of arrivals. In the broadcast mode of IEEE 802.11p based vehicle-to-vehicle communications, acknowledgment messages are absent and a fixed contention window is used in medium access. As a result, the probability of multiple vehicles simultaneously transmitting a packet increases with the number of vehicles. In the case of a simultaneous transmission, a receiver with omnidirectional antennas receives power from all the transmitting vehicles and the probability of successfully decoding a packet decreases. This problem can be alleviated by using sector antennas when the simultaneously transmitted packets arrive at a receiver in narrow angle of arrivals. We show through analysis and simulations that the packet success rate (PSR) can be improved significantly by using the sector antennas setup instead of an omnidirectional antenna. Numerical results show that a several fold increase in PSR can be achieved in a setup with four sector antennas compared with an omnidirectional antenna when the density of vehicles is large

Robust Analog Beamforming for Periodic Broadcast V2V Communication

We generalize an existing low-cost analog signal\ua0processing concept that takes advantage of the periodicity of\ua0vehicle-to-vehicle broadcast service to the transmitter side. In\ua0particular, we propose to process multiple antennas using either\ua0an analog beamforming network (ABN) of phase shifters, or\ua0an antenna switching network (ASN) that periodically alternates\ua0between the available antennas, to transmit periodic messages to\ua0receivers that have an analog combining network (ACN) of phase\ua0shifters, which has been proposed in earlier work. To guarantee\ua0robustness, we aim to minimize the burst error probability for the\ua0worst receiving vehicular user, in a scenario of bad propagation\ua0condition that is modeled by a single dominant path between\ua0the communicating vehicles. In absence of any form of channel\ua0knowledge, we analytically derive the optimal parameters of both\ua0ABN and ASN. The ABN beamforming vector is found to be\ua0optimal for all users and not only for the worst receiving user.\ua0Further, we demonstrate that Alamouti scheme for the special\ua0case of two transmit antennas yields similar performance to ABN\ua0and ASN. At last, we show that the derived parameters of the two\ua0proposed transmission strategies are also optimal when hybrid\ua0ACN-maximal ratio combining is used at the receiver

Sensitivity analysis of beamforming techniques for periodic broadcast V2V communication

In this work, we extend the results of two previously proposed transmit beamforming techniques for periodic,\ua0broadcast, vehicle-to-vehicle communication with a common fixed\ua0broadcast period, to the scenario where vehicular users (VUs)\ua0use different, and potentially varying broadcast periods. The two\ua0techniques, analog beamforming network (ABN) of phase shifters\ua0and antenna switching network (ASN), were previously developed\ua0in accordance with a multiple antenna receiver that employs\ua0an analog combining network (ACN) of phase shifters. To\ua0accommodate the use of multiple broadcast periods, we propose\ua0the design of phase shifter parameters of ABN-ACN and ASN-ACN systems using a design period Td. Then, we analytically\ua0derive sets of broadcast periods that sustain optimality, in the\ua0sense that the sum signal-to-noise ratio (SNR) of Kconsecutive\ua0packets for any receiving VU is maximized. Next, we provide\ua0guidelines on how to set Td to ensure a sufficient granularity\ua0of the sets of optimal broadcast periods. Finally, we investigate\ua0using numerical computations the effect of certain design choices\ua0on the sensitivity of ABN/ASN-ACN systems to small variations\ua0of the broadcast period

Radio resource management for V2V multihop communication considering adjacent channel interference

This paper investigates schemes for multihop scheduling and power control for vehicle-to-vehicle (V2V) multicast communication, taking into account the effects of both co-channel interference and adjacent channel interference, such that requirements on latency or age of information (AoI) are satisfied. Optimal performance can be achieved by formulating and solving mixed Boolean linear programming (MBLP) optimization problems for various performance metrics, including network throughput and connectivity. Fairness among network nodes (vehicles) is addressed by considering formulations that maximizes the worst-case network node performance. Solving the optimization problem comes at the cost of significant computational complexity for large networks and requires that (slow) channel state information is gathered at a central point. To address these issues, a clustering method is proposed to partition the optimization problem into a set of smaller problems, which reduces the overall computational complexity, and a decentralized algorithm that does not need channel state information is provided

Design of false data injection attack on distributed process estimation

Herein, design of false data injection attack on a distributed cyber-physical system is considered. A stochastic process with linear dynamics and Gaussian noise is measured by multiple agent nodes, each equipped with multiple sensors. The agent nodes form a multi-hop network among themselves. Each agent node computes an estimate of the process by using its sensor observation and messages obtained from neighboring nodes, via Kalman-consensus filtering. An external attacker, capable of arbitrarily manipulating the sensor observations of some or all agent nodes, injects errors into those sensor observations. The goal of the attacker is to steer the estimates at the agent nodes as close as possible to a pre-specified value, while respecting a constraint on the attack detection probability. To this end, a constrained optimization problem is formulated to find the optimal parameter values of a certain class of linear attacks. The parameters of linear attack are learnt on-line via a combination of stochastic approximation based update of a Lagrange multiplier, and an optimization technique involving either the Karush-Kuhn-Tucker (KKT) conditions or online stochastic gradient descent. The problem turns out to be convex for some special cases. Desired convergence of the proposed algorithms are proved by exploiting the convexity and properties of stochastic approximation algorithms. Finally, numerical results demonstrate the efficacy of the attack

Adjacent Channel Interference Aware Joint Scheduling and Power Control for V2V Broadcast Communication

IEEE This paper proposes scheduling and power control schemes to mitigate the impact of both co-channel interference (CCI) and adjacent channel interference (ACI) on direct vehicle-to-vehicle broadcast communication. The objective is to maximize the number of vehicles that can communicate with the prescribed requirement on latency and reliability. The joint scheduling and power control problem is formulated as a mixed Boolean linear programming (MBLP) problem. A column generation method is proposed to reduce the computational complexity of the joint problem. From the joint problem, we formulate a scheduling-alone problem (given a power allocation) as a Boolean linear programming (BLP) problem and a power control-alone problem (given a schedule) as an MBLP problem. The scheduling problem is numerically sensitive due to the high dynamic range of channel values and adjacent channel interference ratio (ACIR) values. Therefore, a novel sensitivity reduction technique, which can compute a numerically stable optimal solution at the price of increased computational complexity, is proposed. Numerical results show that ACI, just as CCI, is a serious problem in direct vehicle-to-vehicle (V2V) communication due to near-far situations and hence should not be ignored, and its impact can be reduced by proper scheduling and power control

Robust Connectivity With Multiple Directional Antennas for Vehicular Communications

For critical vehicular communication services, such as traffic safety and traffic efficiency, it is advisable to design systems with robustness as the main criteria, possibly at the price of reduced peak performance and efficiency. We describe a simple, low-cost method for combining the output of L directional (i.e., not omnidirectional) antennas to the input of a single-port receiver with the aim to guarantee robustness, i.e., to minimize the probability that K consecutive packets arriving from the worst-case angle-of-arrival are decoded incorrectly. To minimize complexity, the combining network does not estimate or use channel state information. The combining network consists of L-1 analog phase shifters whose phases are affine functions of time. For a general Lłe K and when the packet error probability decays exponentially with the received SNR, the optimum slopes of the affine functions can be computed by solving an optimization problem that depends on the antenna far-field functions. We provide analytical solutions for the special case of L=2 and 3 antennas, which turns out to be independent of the antenna far-field functions and placement on a vehicle. In an experimental setup consisting of two monopole antennas mounted on the roof of a Volvo XC90, the proposed combining method is shown to give significant performance gains, compared to using any one of the antennas

Secure Estimation in V2X Networks with Injection and Packet Drop Attacks

Vehicle-to-anything (V2X) communications are essential for facilitating cooperative intelligent transport system (C-ITS) components such as traffic safety and traffic efficiency applications. Integral to proper functioning of C-ITS systems is sensing and telemetery. To this end, this paper examines how to ensure security in sensing systems for V2X networks. In particular, secure remote estimation of a Gauss-Markov process based on measurements done by a set of vehicles is considered. The measurements are collected by the individual vehicles and are communicated via wireless links to the central fusion center. The system is attacked by malicious or compromised vehicles with the goal of increasing the estimation error. The attack is achieved by two mechanisms: false data injection (FDI) and garbage packet injection. This paper extends a previously proposed adaptive filtering algorithm for tackling FDI to accommodate both FDI and garbage packet injection, by filtering out malicious observations and thus enabling secure estimates. The efficacy of the proposed filter is demonstrated numerically