Energy efficiency has recently drawn significant attention in diverse applications, such as vehicle-to-everything (V2X) communications. This paper investigates a joint energy efficiency and sum-rate maximization problem in autonomous resource selection in the 5G new radio (NR) vehicular communication while catering for reliability and latency requirements. An autonomous resource allocation problem is formulated as a ratio of sum-rate to energy consumption, where the objective is to maximize the total energy efficiency of power-saving users subject to the reliability and latency requirements. The energy efficiency problem is a mixed-integer programming optimization problem that is computationally complex when the number of users increases. Therefore, a heuristic algorithm, density of traffic-based resource allocation (DeTRA), is proposed to solve the problem. The traffic density per traffic type is considered a metric to split the radio resources and trigger an appropriate resource selection strategy. The simulation results show that the proposed algorithm outperforms the existing schemes in terms of energy efficiency.

Capacity, reliability and latency are key requirements of many applications, e.g. vehicle to vehicle (V2V) in future wireless networks. Device to device (D2D) communication is envisioned as a possible enabler to fulfill the requirements for such applications. The hierarchical radio resource allocation, i.e. sub-granting scheme, is seen as a solution to overcome the incurred inefficient radio resource utilization in overlay D2D communications. In this paper, we investigate the sub-granting radio resource assignment problem for device to infrastructure (D2I) users, i.e. beneficiary users. We first mathematically formulate the resource assignment problem via the cell throughput maximization subject to reliability and latency requirements for D2D communication. We then propose a heuristic dedicated sub-granting radio resource (DSGRR) algorithm to solve the assignment problem. Finally, we present simulation results that show promising improvements for the proposed scheme compared to some other existing approaches.

In this paper, the optimal jamming of wireless localization systems is investigated. Two optimal power allocation schemes are proposed for jammer nodes in the presence of total and peak power constraints. In the first scheme, power is allocated to jammer nodes in order to maximize the average Cramér-Rao lower bound (CRLB) of target nodes, whereas in the second scheme, the power allocation is performed for the aim of maximizing the minimum CRLB of target nodes. Both the schemes are formulated as linear programs, and a closed-form solution is obtained for the first scheme. For the second scheme, under certain conditions, the property of full total power utilization is specified, and a closed-form solution is obtained when the total power is lower than a specific threshold. In addition, it is shown that non-zero power is allocated to at most NTjammer nodes according to the second scheme in the absence of peak power constraints, where NTis the number of target nodes. In the presence of parameter uncertainty, robust versions of the power allocation schemes are proposed. Simulation results are presented to investigate the performance of the proposed schemes and to illustrate the theoretical results.

We investigate the range estimate between two wireless nodes without time stamps exchanging. Considering practical aspects of oscillator clocks, we propose a new model for ranging in which the measurement errors include the sum of two distributions, namely, uniform and Gaussian. We then derive an approximate maximum likelihood estimator (AMLE), which poses a difficult global optimization problem. To avoid the difficulty in solving the complex AMLE, we propose a simple estimator based on the method of moments. Numerical results show a promising performance for the proposed technique.

Clock synchronization is a crucial issue for mobile ad hoc networks due to the dynamic and distributed nature of these networks. In this paper, employing affine models for local clocks, a random broadcast based distributed consensus clock synchronization algorithm is proposed. In the absence of transmission delays, we theoretically prove the convergence of the proposed scheme, which is further illustrated by numerical results. In addition, it is concluded from simulations that the proposed scheme is scalable and robust to transmission delays as well as different accuracy requirements.

We deal with the positioning problem based on two-way time-of-arrival (TW-TOA) measurements in asynchronous wireless sensor networks. The optimal estimator for this problem poses a difficult global optimization problem. To avoid the drawbacks in solving the optimal estimator, we use approximations and derive linear models, which facilitate efficient solutions. In particular, we employ the least squares method and solve a general trust region subproblem to find a coarse estimate. To further refine the estimate, we linearize the measurements and obtain a linear model which can be solved using regularized least squares. Simulation results illustrate that the proposed approaches asymptotically attain the Cramér-Rao lower bound.

This work aims to estimate multiple node positions in the presence of unknown turn-around times within the context of cooperative sensor network localization. In the adopted scheme, each target can communicate with a set of anchors (probably not in sufficient numbers) and a set of other tar- gets. Two-Way Times-of-Arrival between them are measured, which includes unknown processing delays at both channel endpoints. Since finding the Maximum Likelihood Estimates (MLE) of the positions and turn-around times given those measurements poses a difficult nonconvex optimization prob- lem, it is approximated by a Nonlinear Least Squares prob- lem. Then, the positions and turn-around times of multiple targets are estimated jointly by solving an Euclidean Distance Matrix completion problem. Simulations show that the local- ization accuracy of the proposed method is good, providing an initial point that subsequently enables MLE to attain the Cramér-Rao Lower Bound for all considered scenarios.

This paper studies the positioning problem of a single target node based on time-difference-of-arrival (TDOA) measurements in the presence of clock imperfections. Employing an affine model for the behaviour of a local clock, it is observed that TDOA based approaches suffer from a parameter of the model, called the clock skew. Modeling the clock skew as a nuisance parameter, this paper investigates joint clock skew and position estimation. The maximum likelihood estimator (MLE) is derived for this problem, which is highly nonconvex and difficult to solve. To avoid the difficulty in solving the MLE, we employ suitable approximations and relaxations and propose two suboptimal estimators based on semidefinite programming and linear estimation. To further improve the estimation accuracy, we also propose a refining step. In addition, the Cramér-Rao lower bound (CRLB) is derived for this problem as a benchmark. Simulation results show that the proposed suboptimal estimators can attain the CRLB for sufficiently high signal-to-noise ratios.

Received signal strength (RSS)-based single source localization when there is not a prior knowledge about the transmit power of the source is investigated. Because of nonconvex behavior of maximum likelihood (ML) estimator, convoluted computations are required to achieve its global minimum. Therefore, we propose a novel semidefinite programming (SDP) approach by approximating ML problem to a convex optimization problem which can be solved very efficiently. Computer simulations show that our proposed SDP has a remarkable performance very close to ML estimator. Linearizing RSS model, we also derive the partly novel least squares (LS) and weighted total least squares (WTLS) algorithms for this problem. Simulations illustrate that WTLS improves the performance of LS considerably.

The problem of positioning an unknown target is studied for a cooperative wireless sensor network using hybrid two-way time-of-arrival and time-difference-of-arrival measurements. A maximum likelihood estimator (MLE) can be employed to solve the problem. Due to the non-linear nature of the cost function in the MLE, a numerical method, e.g., an iterative search algorithm with a good initial point, should be taken to accurately estimate the target. To avoid drawbacks in a numerical method, we instead linearize the measurements and obtain a new two-step estimator that has a closed-form solution in each step. Simulation results confirm that the proposed linear estimator can attain Cramer-Rao lower bound for sufficiently high SNR.

The problem of positioning a target node is studied for wireless sensor networks with cooperative active and passive sensors. Two-way time-of-arrival and time-difference-of-arrival measurements made by both active and passive nodes are used to estimate the position of the target node. A maximum likelihood estimator (MLE) can be employed to solve the problem. Due to the nonlinear nature of the cost function in the MLE, an iterative search might converge to local minima which often results in large estimation errors. To avoid this drawback, we instead formulate the problem of positioning as finding the intersection of a number of convex sets derived from measurements. To obtain this intersection, we apply the projection onto convex sets approach, which is robust and can be implemented in a distributed manner. Simulations are performed to compare the performance of the MLE and the proposed method.

We deal with positioning of node in wireless sensor network (WSN) using received signal strength (RSS) when there is no priori knowledge about path-loss exponent and transmission power. Since the RSS decreases on the average with distance, it carries some information about the distance to an unknown node. By ordering the RSS's, we conclude that there are some convex sets where the position of the unknown node can be found in the intersection of them. We introduce a plane projection onto convex sets (PPOCS) approach to solve the positioning problem. Simulation results show good performance for the new methods compared to other reduced complexity algorithms.

The local oscillator phase noise can severely affect the performance of OFDM systems. In this paper a new method is presented for compensation of this phase noise. The method is based on the fact that the scattered pilots, which are used for channel estimation, are subject to the same phase error as the OFDM symbols. Least square approach is used to exploit the phase noise information that exists in the scattered pilots and compensate the phase noise on the OFDM symbols. Simulation results show that the method is very effective, especially in high SNR.