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Abstract-Physical

As the target radar signature (TRS) is target-radar orientation sensitive and time varying in wideband cognitive radar (WCR), the TRS information of WCR should be recursively updated by the receiver on the fly. Therefore, in WCR waveform design for target detection, TRS estimation ability should also be considered to provide the prior knowledge for the optimization of the next waveform. To address this problem, a WCR waveform design method is proposed for target detection by maximizing the average signal to clutter plus noise ratio of the received echo on the premise of ensuring the TRS estimation precision. By constraining the estimation performance, a convex cost function is established and the optimal solution can be obtained by the existing convex programming algorithm. Furthermore, for the convenience of waveform optimization in real time, a fast hierarchical scheme is also proposed with comparative performance. Numerical results show that, the proposed methods are able to improve the target detection performance under given estimation performance constraint. Manuscript

In this paper, a semantic communication framework is proposed for textual data transmission. In the studied model, a base station (BS) extracts the semantic information from textual data, and transmits it to each user. The semantic information is modeled by a knowledge graph (KG) that consists of a set of semantic triples. After receiving the semantic information, each user recovers the original text using a graph-to-text generation model. To measure the performance of the considered semantic communication framework, a metric of semantic similarity (MSS) that jointly captures the semantic accuracy and completeness of the recovered text is proposed. Due to wireless resource limitations, the BS may not be able to transmit the entire semantic information to each user and satisfy the transmission delay constraint. Hence, the BS must select an appropriate resource block for each user as well as determine and transmit part of the semantic information to the users. As such, we formulate an optimization problem whose goal is to maximize the total MSS by jointly optimizing the resource allocation policy and determining the partial semantic information to be transmitted. To solve this problem, a proximal-policy-optimization-based reinforcement learning (RL) algorithm integrated with an attention network is proposed. The proposed algorithm can evaluate the importance of each triple in the semantic information using an attention network and then, build a relationship between the importance distribution of the triples in the semantic information and the total MSS. Compared to traditional RL algorithms, the proposed algorithm can dynamically adjust its learning rate thus ensuring convergence to a locally optimal solution. Simulation results show that the proposed framework can reduce by 41.3% data that the BS needs to transmit and improve by two-fold the total MSS compared to a standard communication network Y. Wang and T. Luo are with the