Integrated sensing and communication (ISAC) has garnered significant attention in recent years. In this paper, we delve into the topic of sensing-assisted communication within ISAC systems. More specifically, a novel sensing-assisted channel estimation scheme is proposed for bistatic orthogonal-frequency-division-multiplexing (OFDM) ISAC systems. A framework of sensing-assisted channel estimator is first developed, integrating a tailored low-complexity sensing algorithm to facilitate real-time channel estimation and decoding. To address the potential sensing errors caused by low-complexity sensing algorithms, a sensing-assisted linear minimum mean square error (LMMSE) estimation algorithm is then developed. This algorithm incorporates tolerance factors designed to account for deviations between estimated and true channel parameters, enabling the construction of robust correlation matrices for LMMSE estimation. Additionally, we establish a systematic mechanism for determining these tolerance factors. A comprehensive analysis of the normalized mean square error (NMSE) performance and computational complexity is finally conducted, providing valuable insights into the selection of the estimator’s parameters. The effectiveness of our proposed scheme is validated by extensive simulations. Compared to existing methods, our proposed scheme demonstrates superior performance, particularly in high signal-to-noise ratio (SNR) regions or with large bandwidths, while maintaining low computational complexity.

Extended ambiguity performance (EAP), which includes all grating lobes and side peaks, indicates the maximum detectable region without undesired peaks for target parameter estimation and is critical to radar sensor design. Driven by EAP requirements of bi-static sensing, we propose design criteria for orthogonal frequency division multiplexing (OFDM) reference signal (RS) patterns. The design not only improves EAP in both time delay and Doppler shift domains under different types of sensing algorithms, but also reduces resource overhead for integrated communication and sensing. With minimal modifications of post-FFT processing for current RS patterns, guard interval is extended beyond conventional cyclic prefix (CP), while maintaining intersymbol-interference-(ISI)-free delay estimation. For standard-resolution sensing algorithms, a staggering offset of a linear slope that is relatively prime to the RS comb size is suggested. As for super-resolution sensing algorithms, necessary and sufficient conditions of comb RS staggering offsets, plus new patterns synthesized therefrom, are derived for the corresponding achievable EAP. Furthermore, we generalize the RS pattern design criterion for super-resolution sensing algorithms to irregular forms, which minimizes number of resource elements (REs) for associated algorithms to eliminate all side peaks. Starting from staggered comb pattern in current positioning RS, our generalized design eventually removes any regular form for ultimate flexibility. Overall, the proposed techniques are promising to extend the ISI- and ambiguity-free range of distance and speed estimates for radar sensing.

In this paper, we focus on solving the linear programming (LP) problem that arises in the decoding of low-density parity-check (LDPC) codes by means of the revised simplex method. In order to take advantage of the structure of the LP problem, we reformulate the dual LP and apply the idea of Dantzig-Wolfe (D-W) decomposition method to solve the problem. Each subproblem in the D-W decomposition method is an LP over a convex polyhedral cone. We define the convex polyhedral cone as local parity-check cone and discuss its special structures. Then, we enumerate its extreme rays and use these extreme rays to design an efficient method for the general LP decoding problem. The proposed method exhibits advantages in reducing both the storage and computational requirements.