Novel Sum-of-Sinusoids Simulation Channel Modeling for 6G Multiple-Input Multiple-Output Vehicle-to-Everything Communications

In this paper,a statistical cluster-based simulation channel model with a finite number of sinusoids is proposed for depicting the multiple-input multiple-output(MIMO)communications in vehicleto-everything(V2X)environments.In the proposed sum-of-sinusoids(SoS)channel model,the waves that emerge from the transmitter undergo line-of-sight(LoS)and non-line-of-sight(NLoS)propagation to the receiver,which makes the model suitable for describing numerous V2X wireless communication scenarios for sixth-generation(6G).We derive expressions for the real and imaginary parts of the complex channel impulse response(CIR),which characterize the physical propagation characteristics of V2X wireless channels.The statistical properties of the real and imaginary parts of the complex CIRs,i.e.,autocorrelation functions(ACFs),Doppler power spectral densities(PSDs),cross-correlation functions(CCFs),and variances of ACFs and CCFs,are derived and discussed.Simulation results are generated and match those predicted by the underlying theory,demonstrating the accuracy of our derivation and analysis.The proposed framework and underlying theory arise as an efficient tool to investigate the statistical properties of 6G MIMO V2X communication systems.

A Novel Angle of Arrival Tracking Method in Large-Scale-Array Systems

In this paper,we investigate the problem of angle of arrival(AOA)tracking for the largescale array in terahertz(THz)communication,which has a large size and a narrow beam,highly demanding an accurate angle estimation.On the one hand,the system usually adopts a hybrid structure with limited radio-frequency(RF)chains,which increases the difficulty of angle estimation;on the other hand,the rapid mobility of users also brings new challenges to angle estimation.To address the above challenges,a two-stage tracking framework is proposed in this paper,which employs the random phase matrix and orthogonal long pilots in the first stage to reduce the complicated multi-user estimation to multiple single-user estimations,followed by using both wide and narrow beams in the second stage to serve high-speed and low-speed users.Furthermore,a generalized-approximated-message-passing(GAMP)method is proposed for facilitating a low-accuracy estimation of the angles,followed by adopting a modified expectation-maximization(EM)algorithm based phase estimation to unbiased estimate the instantaneous angle with the help of high-gain characteristics of the beams.The proposed structure can not only simplify the estimation complexity,but also improve the estimation accuracy due to its capability of transferring the non-linear problem of angle observation into a linear gaussian model.In addition,the Kalman tracking framework is employed for performing a continuous angle tracking.Numerical results show that the angle estimation based on the random phase matrix in the initial stage can obtain a high enough estimation accuracy,while the GAMP algorithm implemented in the second stage can quickly capture the angle range under the Rayleigh limit.The performance of the proposed EM-based tracking method is shown to outperform the traditional extended Kalman filter(EKF)method.

Distributed Throughput and Energy Efficient Resource Optimization When D2D and Massive MIMO Coexist

Fifth generation(5G)cellular networks intend to overcome the challenging demands posed by dynamic service quality requirements,which are not achieved by single network technology.The future cellular networks require efficient resource allocation and power control schemes that meet throughput and energy efficiency requirements when multiple technologies coexist and share network resources.In this paper,we optimize the throughput and energy efficiency(EE)performance for the coexistence of two technologies that have been identified for the future cellular networks,namely,massive multiple-input multiple-output(MIMO)and network-assisted device-to-device(D2D)communications.In such a hybrid network,the co/cross-tier interferences between cellular and D2D communications caused by spectrum sharing is a significant challenge.To this end,we formulate the average sum rate and EE optimization problem as mixed-integer non-linear programming(MINLP).We develop distributed resource allocation algorithms based on matching theory to alleviate interferences and optimize network performance.It is shown in this paper that the proposed algorithms converge to a stable matching and terminate after finite iterations.Matlab simulation results show that the proposed algorithms achieved more than 88%of the average transmission rate and 86%of the energy efficiency performance of the optimal matching with lower complexity.