Vehicles are increasingly getting connected with 5G cellular connectivity, and are expected to be connected to 6G in the future. One of the key features of 5G and of 6G will be use of millimeter wave (mmW) bands. In this paper, we consider vehicles equipped with mmW connectivity. The mmW band requires beam tracking and management to achieve high data rate. This can be particularly challenging for vehicle mobility. We study how perception from cameras in a vehicle can be used to improve beam management for mmW in vehicles. We propose a reinforcement learning-based beam management technique to combine 3D maps generated from perception with current 5G beam management. Our results show that the perception can be used to significantly improve the tail of throughput. Additionally, our results show that perception can also be used to significantly reduce power needed for beam management.

In this paper, we study and analyze cooperative cognitive radio networks with arbitrary number of secondary users (SUs). Each SU is considered a prospective relay for the primary user (PU) besides having its own data transmission demand. We consider a multi-packet transmission framework that allows multiple SUs to transmit simultaneously because of dirty-paper coding. We propose power allocation and scheduling policies that optimize the throughput for both PU and SU with minimum energy expenditure. The performance of the system is evaluated in terms of throughput and delay under different opportunistic relay selection policies. Toward this objective, we present a mathematical framework for deriving stability conditions for all queues in the system. Consequently, the throughput of both primary and secondary links is quantified. Furthermore, a moment generating function approach is employed to derive a closed-form expression for the average delay encountered by the PU packets. Results reveal that we achieve better performance in terms of throughput and delay at lower energy cost as compared with equal power allocation schemes proposed earlier in the literature. Extensive simulations are conducted to validate our theoretical findings.

In this paper, we study and analyze cooperative cognitive radio networks with multiple secondary users (SUs). Each SU is considered a prospective relay for the primary user (PU) besides having its own data demand. The proposed scheme leverages the spectral efficiency of the system via allowing two SUs to transmit simultaneously thanks to dirty-paper coding. We propose a power allocation policy that minimizes the average transmitted power at each SU. Moreover, we are concerned with enhancing the throughput of both primary and secondary links. Towards this objective, we investigate multiple opportunistic relay selection policies. We develop a mathematical framework for deriving stability conditions for the queues involved in the system using outage probabilities. Results reveal that we achieve better performance in terms of average power and throughput as compared to uniform power allocation schemes proposed earlier in literature. Extensive numerical simulations are conducted to validate our theoretical findings.

In this paper, a platform for measuring the impulse response of wireless multiple-input-multiple-output (MIMO) vehicle-to-vehicle (V2V) channels is presented. The platform is developed using Rice University Wireless Open-Access Research Platform (WARP) boards and utilized to perform an experimental study of V2V channels through conducting field measurements. A full characterization of MIMO-V2V channels is given via extracting the channel parameters from the acquired measurements. These parameters include the power-delay profile, the Doppler spectrum, and the MIMO transmit and receive correlation matrices of the channel. Numerical simulations are utilized to examine the effect of the channel parameters on the bit error rate (BER) performance of an orthogonal frequency division multiplexing (OFDM) based system.

In this paper, we study and analyze fundamental throughput-delay tradeoffs in cooperative multiple access for cognitive radio systems. We focus on the class of randomized cooperative policies, whereby the secondary user (SU) serves either the queue of its own data or the queue of the primary user (PU) relayed data with certain service probabilities. The proposed policy opens room for trading the PU delay for enhanced SU delay. Towards this objective, stability conditions for the queues involved in the system are derived. Furthermore, a moment generating function approach is employed to derive closed-form expressions for the average delay encountered by the packets of both users. Results reveal that cooperation expands the stable throughput region of the system and significantly reduces the delay at both users. Moreover, we quantify the gain obtained in terms of the SU delay under the proposed policy, over conventional relaying that gives strict priority to the relay queue.

In this paper, an iterative blind estimator for fractional carrier frequency offset (CFO) in orthogonal frequency division multiplexing (OFDM) systems is proposed. The estimator utilizes the null subcarriers transmitted at the edge of the spectrum and does not require any training. In addition, the proposed estimator does not require any prior knowledge of the frequency response of the channel. The problem is formulated using a state-space model, and an extended Kalman filter (EKF) is employed to estimate the CFO iteratively. Simulation results illustrate the enhanced ability of the proposed algorithm, relative to the existing approaches, to estimate and track the CFO even in the presence of high Doppler.