The integration of radar sensing into communication networks for supporting various applications is an emerging area of interest in the field of integrated sensing and communication. In this paper, we propose a two-stage sensing-assisted channel extrapolation scheme for time-division duplex (TDD) massive MIMO-OFDM systems, utilizing radar-sensed scattering environment information (SEI) to enhance channel extrapolation performance. In Stage 1, user transmits uplink pilots (UPs) on a bandwidth part (BWP) that occupies only a fraction of the system bandwidth, for the base station (BS) to estimate the angle parameters through the proposed space-time multiple signal classification (ST-MUSIC) algorithm. However, it is difficult to achieve accurate channel extrapolation on the other BWPs because the delay resolution of a single BWP is limited. In Stage 2, the BS transmits full-band downlink pilots (DPs) aligned with the angles estimated in stage 1, and then performs joint scattering environment sensing and channel extrapolation based on the received UPs and reflected DPs. The EM-Turbo-CS algorithm is proposed for Stage 2 to exploit the SEI extracted from the full-band reflected DPs to dramatically improve the delay estimation accuracy, resulting in high-accuracy channel extrapolation. Meanwhile, a coarse estimation using the ST-MUSIC algorithm on the received signals helps reduce the complexity of the EM-Turbo-CS algorithm. Simulation results demonstrate that the proposed solution significantly improves the channel extrapolation performance.

This paper considers an integrated sensing and communication system, where some radar targets also serve as communication scatterers. A location domain channel modeling method is proposed based on the position of targets and scatterers in the scattering environment, and the resulting radar and communication channels exhibit a two-dimensional (2-D) joint burst sparsity. We propose a joint scattering environment sensing and channel estimation scheme to enhance the target/scatterer localization and channel estimation performance simultaneously, where a spatially non-stationary Markov random field (MRF) model is proposed to capture the 2-D joint burst sparsity. An expectation maximization (EM) based method is designed to solve the joint estimation problem, where the E-step obtains the Bayesian estimation of the radar and communication channels and the M-step automatically learns the dynamic position grid and prior parameters in the MRF. However, the existing sparse Bayesian inference methods used in the E-step involve a high-complexity matrix inverse per iteration. Moreover, due to the complicated non-stationary MRF prior, the complexity of M-step is exponentially large. To address these difficulties, we propose an inverse-free variational Bayesian inference algorithm for the E-step and a low-complexity method based on pseudo-likelihood approximation for the M-step. In the simulations, the proposed scheme can achieve a better performance than the state-of-the-art method while reducing the computational overhead significantly.

This paper considers an integrated sensing and communication system, where some radar targets also serve as communication scatterers. A location domain channel modeling method is proposed based on the position of targets and scatterers in the scattering environment, and the resulting radar and communication channels exhibit a partially common sparsity. By exploiting this, we propose a joint scattering environment sensing and channel estimation scheme to enhance the target/scatterer localization and channel estimation performance simultaneously. Specifically, the base station (BS) first transmits downlink pilots to sense the targets in the scattering environment. Then the user transmits uplink pilots to estimate the communication channel. Finally, joint scattering environment sensing and channel estimation are performed at the BS based on the reflected downlink pilot signal and received uplink pilot signal. A message passing based algorithm is designed by combining the turbo approach and the expectation maximization method. The advantages of our proposed scheme are verified in the simulations.

This paper studies optimal inventory rationing policies for a retailer of perishable products who sells through its own stores and third party Websites by an affiliate program. By posting on partners' Webpages, an affiliate program allows the retailer to attract more customers who otherwise would be missed. However, the retailer needs to pay out a commission for each sale originated from the Website operator that participates in the affiliate program. Thus, the net revenue of selling one unit of product to an online "referral" (online customer) is less profitable than that to a customer from a physical store. When the inventory at stores is running low, the retailer may further refer the online request to somebody else for fulfilling, which is equivalently to say that the retailer can reject online customer requests. Therefore, upon the arrival of any demand through the affiliate program, the retailer needs to decide whether or not to accept it; and if so, assign which of multiple outlets for the fulfillment. Based on a discrete-time dynamic programming model, the optimal admission policy of the retailer is analyzed in this paper, which is shown to be a two-dimensional threshold policy. The structural properties of the revenue function are analyzed, and numerical examples are given to show the revenue impact of optimal admission control.