We study a unified information-theoretic framework for integrated sensing and communications (ISAC), applicable to both monostatic and bistatic sensing scenarios. Special attention is given to the case where the sensing receiver (Rx) is required to produce a “soft" estimate of the state sequence, with logarithmic loss serving as the performance metric. We derive lower and upper bounds on the capacity-distortion function, which delineates the fundamental tradeoff between communication rate and sensing distortion. These bounds coincide when the channel between the ISAC transmitter (Tx) and the communication Rx is degraded with respect to the channel between the ISAC Tx and the sensing Rx, or vice versa. Furthermore, we provide a complete characterization of the capacity-distortion function for an ISAC system that simultaneously transmits information over a binary-symmetric channel and senses additive Bernoulli states through another binary-symmetric channel. The Gaussian counterpart of this problem is also explored, which, together with a state-splitting trick, fully determines the capacity-distortion-power function under the squared error distortion measure.

A distributed lossy compression network with$L$encoders and a decoder is considered. Each encoder observes a source and sends a compressed version to the decoder. The decoder produces a joint reconstruction of target signals with the mean squared error distortion below a given threshold. It is assumed that the observed sources can be expressed as the sum of target signals and corruptive noises which are independently generated from two symmetric multivariate Gaussian distributions. The minimum compression rate of this network versus the distortion threshold is referred to as the rate-distortion function, for which an explicit lower bound is established by solving a minimization problem. Our lower bound matches the well-known Berger-Tung upper bound for some values of the distortion threshold. The asymptotic gap between the upper and lower bounds is characterized in the large$L$limit.

The applications of artificial intelligence (AI) and machine learning (ML) technologies in wireless communications have drawn significant attention recently. AI has demonstrated real success in speech understanding, image identification, and natural language processing domains, thus exhibiting its great potential in solving problems that cannot be easily modeled. AI techniques have become an enabler in wireless communications to fulfill the increasing and diverse requirements across a large range of application scenarios. In this paper, we elaborate on several typical wireless scenarios, such as channel modeling, channel decoding and signal detection, and channel coding design, in which AI plays an important role in wireless communications. Then, AI and information theory are discussed from the viewpoint of the information bottleneck. Finally, we discuss some ideas about how AI techniques can be deeply integrated with wireless communication systems.

In this paper, a localization-based polar construction method is proposed to directly find the set of synthetic channels for information bits given a code configuration. Taking advantage of the partial order of polar codes, only a small number of synthetic channels need to be ordered, which scales as O(N/ log23/2 N), resulting in a sublinear complexity to construct a polar code. Specifically, a practical method is put forward first to fast construct a group-based partial order diagram. A local area in the diagram with adaptive boundaries is then identified. By ordering the synthetic channels within the local area and combining selected ones with all the synthetic channels beyond the local area, the final set of synthetic channels for information bits are determined. Simulation results demonstrate how to adapt the boundary settings to different rate matching schemes and code configurations, and validate the effectiveness of the proposed method compared with the density evolution based methods.

Recently convolutional polar (cpolar) codes have been proposed. A tensor-network-based successive cancellation (SC) decoding was proposed for them under which cpolar codes were shown to outperform polar codes. In this paper we present the notion of m-bit-channels for cpolar codes and give the recursive construction of m-bit-channels for m=3. Then a log likelihood ratio(LLR)-based SC decoding of complexity order O(Nlog(N)) for cpolar codes is presented. We also present the numerical results for performance evaluation of cpolar codes under SC list (SCL) decoding. Our simulation results show that cpolar codes can achieve the performance of polar codes with a list size reduced by a factor of 4.