Multi-Array 5G V2V Relative Positioning: Performance Bounds

We study the performance bounds of vehicle-to-vehicle (V2V) relative positioning for vehicles with multiple antenna arrays. The Cram\'{e}r-Rao bound for the estimation of the relative position and the orientation of the Tx vehicle is derived, when angle of arrival (AOA) measurements with or without time-difference of arrival (TDOA) measurements are used. In addition, geometrically intuitive expressions for the corresponding Fisher information are provided. The derived bounds are numerically evaluated for different carrier frequencies, bandwidths and array configurations under different V2V scenarios, i.e. overtaking and platooning. The significance of the AOA and TDOA measurements for position estimation is investigated. The achievable positioning accuracy is then compared with the present requirements of the 3rd Generation Partnership Project (3GPP) 5G New Radio (NR) vehicle-to-everything (V2X) standardization

Non-uniform array design for robust LoS MIMO via convex optimization

The array design problem of multiple-input multiple-output (MIMO) systems in a line-of-sight (LoS) transmit environment is examined. As uniform array configurations at the transmitter (Tx) and receiver (Rx) are optimal at specific transmit distances only, they lead to reduced spectral efficiency over a range of transmit distances. To that end, the joint design of nonuniform Tx and Rx arrays towards maximizing the minimum capacity of a LoS MIMO system across a range of transmit distances is investigated in this paper. By introducing convex relaxation, the joint Tx and Rx array design is cast as a convex optimization problem, which is solved in a iterative manner. In addition, we also implement a local search to obtain a refined solution that achieves an improved performance. It is shown that the non-uniform configurations designed with our proposed approach outperform uniform and non-uniform array designs of the literature in terms of capacity and/or complexity.Comment: 6 pages, 5 figures, submitted for publication at IEEE PIMRC202

Power Allocation and Parameter Estimation for Multipath-based 5G Positioning

We consider a single-anchor multiple-input multiple-output orthogonal frequency-division multiplexing system with imperfectly synchronized transmitter (Tx) and receiver (Rx) clocks, where the Rx estimates its position based on the received reference signals. The Tx, having (imperfect) prior knowledge about the Rx location and the surrounding geometry, transmits reference signals based on a set of fixed beams. We develop strategies for the power allocation among the beams aiming to minimize the expected Cram\ue9r-Rao lower bound for Rx positioning. Additional constraints on the design are included to make the optimized power allocation robust to uncertainty on the line-of-sight (LOS) path direction. Furthermore, the effect of clock asynchronism on the proposed allocation strategies is studied. Our evaluation results show that, for non-negligible synchronization error, it is optimal to allocate a large fraction of the available power for the illumination of the non-LOS (NLOS) paths, which help resolve the clock offset. In addition, the complexity reduction achieved by our proposed suboptimal approach incurs only a small performance degradation. We also propose an off-grid compressed sensing-based position estimation algorithm, which exploits the information on the clock offset provided by NLOS paths, and show that it is asymptotically efficient

5G Downlink Multi-Beam Signal Design for LOS Positioning

In this work, we study optimal transmit strategies for minimizing the positioning error bound in a line-of-sight scenario, under different levels of prior knowledge of the channel parameters. For the case of perfect prior knowledge, we prove that two beams are optimal, and determine their beam directions and optimal power allocation. For the imperfect prior knowledge case, we compute the optimal power allocation among the beams of a codebook for two different robustness-related objectives, namely average or maximum squared position error bound minimization. Our numerical results show that our low-complexity approach can outperform existing methods that entail higher signaling and computational overhead.Comment: accepted for publication at IEEE GLOBECOM 201

Power Allocation and Parameter Estimation for Multipath-based 5G Positioning

We consider a single-anchor multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) system with imperfectly synchronized transmitter (Tx) and receiver (Rx) clocks, where the Rx estimates its position based on the received reference signals. The Tx, having (imperfect) prior knowledge about the Rx location and the surrounding geometry, transmits the reference signals based on a set of fixed beams. In this work, we develop strategies for the power allocation among the beams aiming to minimize the expected Cram\'er-Rao lower bound (CRLB) for Rx positioning. Additional constraints on the design are included to ensure that the line-of-sight (LOS) path is detected with high probability. Furthermore, the effect of clock asynchronism on the resulting allocation strategies is also studied. We also propose a gridless compressed sensing-based position estimation algorithm, which exploits the information on the clock offset provided by non-line-of-sight paths, and show that it is asymptotically efficient.Comment: 30 pages, 6 figures, submitted to IEEE Transactions on Wireless Communication