A design methodology is proposed to optimize the minimum weight and error coefficient of polarization-adjusted convolutional (PAC) codes, enhancing their maximum likelihood (ML) performance based on a theoretical analysis of code asymptotic behavior. Employing an adapted multilevel list search algorithm to identify minimum-weight codewords, the methodology comprises three deterministic optimization algorithms. First, an iterative rate-profiling optimization algorithm substantially reduces the number of minimum-weight codewords through efficient pairwise exchanges of information and frozen indices. Second, a tree search optimization algorithm progressively extends the convolutional impulse response, exploring superior solutions within a theoretically constrained search space. Third, a joint optimization algorithm synthesizes the two algorithms, alternately refining the rate-profiling and convolutional pre-transform. Complexity analysis underscores the computational efficiency of these algorithms for short PAC codes, while optimization results confirm the strong capability of the proposed methodology in improving the minimum weight and error coefficient. With moderate-to-large list decoding for code lengths of 64 to 256, the proposed PAC codes consistently outperform state-of-the-art polar code variants, attaining or approaching the random coding union (RCU) bound. Additionally, the proposed PAC codes demonstrate the capability to exceed the normal approximation (NA) bound at low-to-moderate code rates.

From pedestrians to Kuramoto oscillators, interactions between agents govern how dynamical systems evolve in space and time. Discovering how these agents relate to each other has the potential to improve our understanding of the often complex dynamics that underlie these systems. Recent works learn to categorize relationships between agents based on observations of their physical behavior. These approaches model relationship categories as outcomes of a categorical distribution which is limiting and contrary to real-world systems, where relationship categories often intermingle and interact. In this work, we introduce a level of abstraction between the observable behavior of agents and the latent categories that determine their behavior. To do this, we learn a mapping from agent observations to agent preferences for a set of latent categories. The learned preferences and inter-agent proximity are integrated in a nonlinear opinion dynamics model, which allows us to naturally identify mutually exclusive categories, predict an agent’s evolution in time, and control an agent’s behavior. Through extensive experiments, we demonstrate the utility of our model for learning interpretable categories, and the efficacy of our model for long-horizon trajectory prediction.

Low Earth Orbit (LEO) satellite networks have become a promising solution to support Internet of Things (IoT) services, especially in remote or infrastructure-limited areas. In LEO-enabled IoT scenarios, timely data delivery is essential for situational awareness and real-time decision-making. To measure the freshness of the data, Age of Information (AoI) has been widely adopted as a key performance metric, particularly in applications with frequent status updates. However, minimizing AoI in LEO systems is challenging due to limited onboard resources and dynamic traffic demands. This paper focuses on the joint optimization of beam hopping (BH) scheduling and resource block (RB) allocation to reduce the average AoI across the network. To handle the complex problem, we propose a hierarchical deep reinforcement learning (DRL) framework. At the high level, a centralized satellite agent leverages the Proximal Policy Optimization (PPO) algorithm to determine the beam illumination pattern over the service area. At the low level, a decentralized multi-agent PPO framework is employed, where each beam-level agent independently allocates resource block (RB) to users, aiming to improve data freshness and transmission efficiency. Simulation results show that the proposed method outperforms three benchmark strategies in reducing average AoI.

A novel rate-profiling design is proposed for polarization-adjusted convolutional (PAC) codes under list decoding, adopting a path probability perspective. Inspired by the concepts of Hamming distance and Hamming weight, the concepts of path probability distance (PPD) and path probability weight (PPW) are innovatively introduced to measure the disparity between the erroneous paths and the correct path for PAC codes under probabilistic decoding. An in-depth analysis of the recursion of node log-likelihood ratios (LLRs) in the factor graph is conducted, elucidating the derivation of a lower bound on the PPW as a consequential outcome. Utilizing the concept of the PPW, a rate-profiling design algorithm is proposed to establish the reliability ranking for the bit-channels of PAC codes. Simulation results demonstrate that PAC codes employing the proposed rate-profiling exhibit advantages over alternative rate-profiling and other state-of-the-art polar coding schemes across various block lengths and code rates under short decoding list sizes.

Hybrid automatic repeat request (HARQ) is a potential technique for communication systems in non-terrestrial networks (NTN) to ensure reliability. However, the long round-trip time (RTT) within NTN diminishes the information timeliness when applying HARQ. Moreover, the absence of perfect channel state information (CSI) poses challenges to the precise design of HARQ strategies. In this paper, we introduce a novel coded HARQ scheme for NTN, in which the age of coded information (C-AoI) is proposed as a metric of information timeliness to guide the search for the optimal coded HARQ strategy. The rate-compatible inherited bits codeword construction (RCIC) is introduced for HARQ process to enhance the transmission efficiency. By applying Markov decision process (MDP) and Q-learning algorithm in the optimization, the obtained coded HARQ scheme significantly outperforms the existing schemes in terms of information timeliness and number of transmissions. The throughput of satellite-terrestrial links is also improved.

With the advantage of robust resilience, large capacity, and strong adaptability, space-air-ground integrated network (SAGIN) can simultaneously support various task scenarios involving heterogeneous networks and diverse task demands. In this integrated network, the constraints from limited resources and dynamic environment pose challenges in fulfilling concurrent demands, and improper task scheduling strategy can lead to network resource wastage and task dissatisfaction. In this paper, we propose an adaptive solution for multiscenario joint scheduling in SAGIN. We construct a comprehensive task scheduling frame-work and propose the task relevance matrix for in-depth analysis. To achieve the goal of improving network resource utilization and task satisfaction, we formulate the joint optimization problem as a cooperative Markov game and propose a novel multi-scenario task scheduling algorithm based on heterogeneous-agent proximal policy optimization (HAPPO). Simulation results show that the proposed algorithm can achieve better performance by effectively improving resource utilization and reducing task delay, compared with two state-of-the-art multi-agent reinforcement learning algorithms and the random baseline.

Non-geostationary orbit (NGSO) satellites, combined with beam hopping (BH) technology, play a vital role in wide-area communications, sensing, and positioning. However, the uneven distribution of ground users and the high mobility of NGSO satellites pose more significant challenges to multi-beam scheduling. This paper proposes a multi-NGSO BH scheduling framework considering the traffic demand of ground users and inter-satellite interference. We decompose the complex optimization problem into two sub-problems. First, we reallocate the satellite service cells to achieve a balanced distribution of satellite service coverage. Then, we propose a multi-NGSO BH algorithm based on soft actor-critic (SAC) to achieve real-time and traffic-driven beam scheduling. Simulation results show that the proposed algorithm outperforms other benchmarks in throughput and service failure rate (SFR).

Space-air-ground integrated network (SAGIN) can support a wide variety of task scenarios as a next-generation network. In this dynamic network, tasks with different characteristics and demands are usually simultaneously generated, which requires an efficient task scheduling method to meet diverse task demands, reduce system cost, and improve resource utilization. In this paper, we propose a scalable task scheduling and resource allocation solution suitable for various task scenarios in SAGIN. Specifically, we construct a generic task scheduling framework that considers diverse task types and demands in SAGIN. With the objective of minimizing the total system cost, we propose a novel joint task scheduling and resource allocation algorithm based on deep reinforcement learning to obtain the optimal solution. Extensive simulations are conducted in a civil aviation scenario to evaluate the performance of the proposed algorithm. Numerical results demonstrate that our proposed algorithm can efficiently reduce system cost and task drop rate compared with three benchmark schemes, and the improvement of resource allocation to task scheduling is verified through analysis.

In this paper, we focus on the design of low complexity antenna selection algorithm of a millimeter-wave (mmWave) multiple-input multiple-output nonorthogonal multiple access (MIMO-NOMA) for space information network (SIN). First, the angle-domain sparse geometric based mmWave channel model is utilized in the mmWave downlink system. By grouping the NOMA users according to the distance and path loss, the system performance is related to the instantaneous channel gain of users. Hence, we propose a continuous maximum antenna selection (CM-AS) algorithm, which can approach the maximum sum-rate of the high complexity exhaustive search algorithm. Based on this CM-AS algorithm, we propose two algorithms to improve the user fairness, including discrete maximum AS (DM-AS) and ratio maximization AS (RM-AS) algorithms. Simulations are conducted to confirm the performance of the proposed algorithms in sum-rate and user fairness, and shown that the DM-AS and RM-AS algorithms are reasonable compromise using in practice based on the sum-rate and fairness.

The soft-decision decoding of polar codes is a trend that will be extensively applied in modern complex communication systems. However, the existing soft-decision decoding of polar codes is not satisfied due to the poor performance and high complexity. In this paper, a novel adjustable list decoding and its soft-decision type are proposed. Some bounds are given to depict the features of the decoding list with a correct path, which provides a guide to adjust the decoding list. The proposed adjustable list decoding scheme can achieve an equivalent performance to conventional SCL with significant lower complexity. Moreover, the soft adjustable list decoding can also outperform than the conventional soft-decision decoding schemes in concatenated structures.

Sparse code multiple access (SCMA) is one of the key techniques to address the high spectral efficiency and massive connectivity requirements for the fifth generation (5G) wireless systems. Moreover, polar codes are selected as the candidate scheme of control codes in enhanced mobile broadband (eMBB). Note that the joint design of channel coding and SCMA scheme can significantly improve the system overall performances, which essentially shows the potential for 5G massive machine type communications (mMTC). Thus, in this paper, we proposed a polarization weight (PW)-based polar coded SCMA (PC SCMA) system to satisfy the requirements of low complexity implementation and high reliability under a wide range of code length and rate. Our design of PW-based PC SCMA system is mainly including the following three aspects: 1) deploy the polarization weight (PW) algorithm to construct polar code with lower complexity; 2) employ the bit-reverse shortening (BRS) algorithm to achieve rate matching in the encoding part; 3) adopt the cyclic redundancy check (CRC) to set up an early stopping criterion in the decoding part. Simulation results show that the proposed PW-based PC SCMA system can outperform the existing PC SCMA system over AWGN and fading channels.

The long-haul communication systems can offer ultra high-speed data transfer rates but suffer from burst errors. The high-rate and high-performance staircase codes provide an efficient way for long-haul transmission. The staircase coding scheme is a concatenation structure, which provides the opportunity to improve the performance of high-rate polar codes. At the same time, the polar codes make the staircase structure more reliable. Thus, a high-rate polar-staircase coding scheme is proposed, where the systematic polar codes are applied as the component codes. The soft cancellation decoding of the systematic polar codes is proposed as a basic ingredient. The encoding of the polar-staircase codes is designed with the help of density evolution, where the unreliable parts of the polar codes are enhanced. The corresponding decoding is proposed with low complexity, and is also optimized for burst error channels. With the well designed encoding and decoding algorithms, the polar-staircase codes perform well on both AWGN channels and burst error channels.

The integration of high throughput satellite into Internet of Things (IoT) is regarded as an effective strategy to provide ubiquitous broadband access in a seamless, cost-efficient manner. Meanwhile, due to the demand of machine-to-machine (M2M) high throughput services, millimeter-wave (mmWave) IoT networks arouses huge interest. In this paper, we investigate the performance of an amplify-and-forward (AF) mmWave hybrid satellite-terrestrial relay networks (HSTRN) for IoT broadband communications, where we assume source-relay link undergos Shadowed-Rician fading and the relay-destination link undergos Rayleigh fading. Considering rain attenuation is the main factor at mmWave bands, we utilize the multidimensional rain attenuation model to analyze the effect of rain attenuation on system performance. Then we derive the closed-form expression of outage probability and tight approximation of ergodic capacity. Finally, numerical and simulation results are provided to validate our analytical results and show the effect of rain attenuation on the system performance.

Machine-to-Machine (M2M) communications are expected to support extremely harsh requirements on both latency and reliability, which is characterized by the ultra-reliable, low-latency coding (uRLLC) technology in physical layer. In this paper, motivated by the recent development on the finite-blocklength information theory, we propose an ultra-reliable short blocklength analog fountain code (AFC) for M2M communications. First, we use the extrinsic information transfer (EXIT) chart to analyze the AFC compressive sensing belief propagation (CS-BP) decoding algorithm, by tracking the mutual information of AFC CS-BP decoding process, which related to the channel dispersion for the short blocklength AFC. Then, based on the EXIT chart analysis, we propose a Weight-set optimization progressive edge-growth (WO-PEG) encoding algorithm for the short blocklength AFC. Simulation results show that the proposed WO-PEG AFC scheme can effectively improve block error rate (BLER) in the short blocklength regime.

In this paper, an improved random puncturing pattern of polar codes is proposed, where only the frozen bits can be selected to puncture. Compared to the existing random puncturing schemes, our improved random puncturing scheme can achieve 0.2-1dB decoding performance improvement. Then, an optimized rate-compatible hybrid automatic repeat request (HARQ) transmission scheme is proposed based on parallel concatenated punctured (PCP) polar codes. By analyzing the overhead of the previous successful decoded coding block in our rate-compatible HARQ scheme, two methods of determining the optimal initial code-rate of each new PCP polar coding block are proposed over a time-varying channel. Simulation results show that the average number of retransmissions is about 1.5 times in our proposed rate-compatible HARQ schemes with a 2-level PCP polar encoding construct, which reduces half of the average number of retransmissions than the existing rate-compatible polar coding scheme.

To enhance the performance in practical communications, a novel construction of polar code is designed for a rational polar and space-time block coding (Polar-STBC) system. The Polar-STBC system can be equivalent to a single transmission channel for each polar code bit in Rayleigh fading MIMO channels, and the equivalent channel can be regarded as a fading channel, of which the gain coefficient and additive noise are studied. Moreover, the distribution of the additive noise is also derived. Finally, we show that the bit error rate performance of our Polar-STBC system in 2 × 2, 4 ×⌉ 2 and 4 × 4 MIMOs.