Remote state estimation plays a critical role in internet of things (IoT) systems, where distributed sensors report dynamic processes to remote estimators for control and monitoring. Traditional freshness metrics, such as age of information (AoI), do not necessarily capture the information value degradation caused by random transmission latency and content dynamics. In this paper, we evaluate the value of information through the lens of Shannon entropy by adopting theuncertainty of information(UoI), which quantifies the receiver’s uncertainty in estimating the source state caused by stale information delivery. We study a remote estimation system where a binary Markov source is sampled and transmitted over a randomly delayed channel with a long-term sampling frequency constraint. This problem is formulated as a constrained partially observable semi-Markov decision process (C-POSMDP), solved through Dinkelbach’s transformation and Lagrangian relaxation combined with the relative value iteration (RVI) algorithm. To mitigate the complexity of RVI, we further develop a low-complexity index-based policy by approximating the Bellman equation under relatively high delay. Numerical results show that both proposed policies outperform zero-wait, uniform, and AoI-optimal baselines, with the sub-optimal policy achieving near-optimal performance in relatively high delay regimes.

Robust perception for safety-critical Internet of Things (IoT) applications in dense jamming environments demands advanced radar sensing capabilities with enhanced interference mitigation and array-beampattern optimization. When the target direction overlaps with the jamming region, both a narrower mainlobe and a deeper sidelobe are desirable but hard to meet simultaneously since they both consume array degrees of freedom. This paper investigates the application of Movable Antenna Array (MAA) in aiding radar beampattern synthesis under dense jamming scenarios. Using the extra spatial degrees of freedom provided by MAA, this work jointly optimizes both weighting vectors and antenna element positions to achieve superior beampattern without adding array element number. The integrated sidelobe level is chosen as the optimization objective with practical constraints, which leads to a highly non-convex optimization. To address this, we propose an Alternating Optimization-based Sequential Approximation (AOSA) algorithm. In each iteration, the weighting vector subproblem is solved through convex approximations of the primary nonconvex constraints, while the antenna position vector subproblem is tackled indirectly with a specifically derived proposition. Simulation results verify that the joint optimization framework effectively improves target separability and jamming rejection in complex electromagnetic environments, demonstrating its promising potential for advancing radar detection capabilities.

To address the challenges of reliable and efficient short-packet communication in the Internet of Things (IoT), especially under ultra-reliable low-latency communication (URLLC) constraints, this work optimizes the design of short polar codes tailored for low-power IoT devices. To improve the reliability of short polar codes under successive cancellation list (SCL) decoders, we introduce a novel heuristic optimization algorithm guided by a unified metric. This algorithm carefully balances the tradeoff between the number of minimum-weight codewords (a.k.aerror coefficient) and the reliability of selected information subchannels. Through a guided and deliberate disruption of the partial order property of polar codes, our algorithm reduces the error coefficient to enhance maximum likelihood (ML) decoding performance, while managing the impact on subchannel reliability. Numerical results demonstrate a consistent and significant improvement over the baseline RM-Polar and Gaussian Approximation (GA) constructions across various code parameters. Furthermore, our approach features low offline design complexity, achieving state-of-the-art or highly competitive performance against other advanced schemes, particularly at low code rates. This makes our method highly suitable for URLLC, as the resulting optimized codes can be deployed on existing 5G hardware with zero additional on-device decoding complexity, while the achievable coding gain directly translates into transmission energy savings.

Topologically associating domains (TADs) are fundamental 3D genome structures that facilitate key gene regulatory interactions. The boundaries of TADs are rich in functional elements critical for maintaining structural integrity, making their identification essential for understanding the relationship between genome organization and gene expression. However, existing algorithms for identifying TAD boundaries often rely on fixed boundary sizes and neglect the varying predictive power of different features. To address these limitations, we introduce multiTAD, an advanced attention-based deep learning model that leverages 12 epigenetic signals to accurately detect TAD boundaries of diverse sizes. multiTAD significantly outperforms mainstream approaches, revealing distinct boundary size preferences across different cell lines. Additionally, multiTAD demonstrates strong cross-cell line predictive capabilities, further highlighting its broad applicability in genomic research.

In this paper, we first introduce an efficient method for estimating weight distributions of polar codes and polarization-adjusted convolutional (PAC) codes. Based on a recursive algorithm of computing the weight enumerating functions of polar cosets, this method focuses on two key objectives: accurately determining the number of low-weight codewords and quickly approximating the distribution of high-weight codewords. Then we optimize the Reed Muller-Gaussian Approximation (RM-GA) rate profiling scheme with the help of the proposed method aiming at reducing the truncated union bound (TUB). Simulation results demonstrate that the proposed hybrid method maintains competitively low complexity while effectively achieving the objectives. The TUB-improved RM-GA rate profiling scheme for polar codes exhibits a performance improvement of nearly 1 dB at 10–4compared to GA and around 0.3 dB improvement compared to RM-GA. The proposed scheme for PAC codes also achieves an enhancement of approximately 0.52 dB at 10–5compared to the commonly used RM-GA scheme.

Age of information (AoI) defines the freshness of status update in real-time systems, such as the Industrial Internet of Things (IIoT), and can be affected by delays and transmission error probability. To improve the reliability of data transmissions, the recent AoI works on physical layer considered applying practical coding schemes. Since polar codes can be strictly proved to achieve the channel capacity, this article makes an effort to comprehensively investigate and optimize the AoI performance in a polar-coded status update system. First, we propose a practical code-based status update system that takes full consideration of encoding, transmission, propagation, decoding, and feedback delays in AoI analysis. Then, we analyze and derive the average AoI of the proposed system with various transmission protocols. The simulation results of a polar-coded system validate the theoretical analysis and show that hybrid automatic repeat request (HARQ) achieves better AoI performance than non-HARQ. To optimize AoI in polar-coded status update system, we further improve the designs for HARQ with chase combining (HARQ-CC) and HARQ with incremental redundancy (HARQ-IR), respectively. The design signal-to-noise ratio (SNR), puncturing length of HARQ-CC are optimized by traversal, while the code lengths for each transmission and maximum transmission times of HARQ-IR are optimized by the greedy algorithm. Simulation results show that the proposed HARQ can achieve better average AoI performance than traditional HARQ.

Recently, the Space-Air-Ground-Sea Integrated Network (SAGSIN) attracts great attention due to its ability to provide high-speed communication services to aviation users (AUs) and marine users (MUs), with Low Earth Orbit (LEO) satellites play an essential role. However, the available space in LEO is nearly saturated and full of massive space junks, which, combined with the ultra-low latency requirements for future 6G, presents a significant challenge. To address this issue, we propose designing a Very Low Earth Orbit (VLEO)-based satellite network that efficiently serves AUs and MUs. We first create and analyze the heat maps based on actual collected data of Chinese aviation and marine communication traffic, and generate a benchmark observation point model with grid point method. Then we propose an implicit multi-objective continuous multi-variate optimization problem to achieve the maximum average coverage with minimum VLEO satellites. To solve this problem, we build a satellite constellation simulation system, using the idea of decomposition and polymerization combined with the elite strategic genetic algorithm (ESGA) of swarm intelligence optimization algorithm. Many simulation results are obtained, including the indication that the optimal VLEO constellation has the deployment features of large altitude and low inclination, and has better coverage performance for longitudinal distributed business. The design process in this work is highly migratory,

Energy efficiency and information freshness are two fundamentally critical performance metrics in real-time status update systems which can be measured by energy cost (EC) and age of information (AoI), respectively. This paper examines the AoI and EC performance of the hybrid automatic repeat request with incremental redundancy (HARQ-IR) scheme in code-based status update systems and presents unified results that can generally depict the average AoI and EC over block fading channels. First, we propose a practical code-based status update system that fully takes into account the impact of information processing and long-distance transmission in performance analysis. Then, we analyze and derive the average AoI/EC expressions for HARQ-IR scheme, which are unified results over block fading channels. The simulations of different transmission protocols validate our explicit results and show that there is a distance threshold on whether to retransmit the failed updates. Based on the simulation results, it appears that system AoI/EC demand will affect distance threshold values, which provide guidance for future designs of age-energy tradeoff transmission schemes.

In this paper, we offer an explicit, unified result that can generally depict the age performance of error-correcting techniques at the physical layer. We first propose a more realistic code-based status update system, wherein different types of delay elements, e.g., the coding delay, transmission delay, propagation delay, decoding delay and feedback delay are comprehensively considered. Under this system, we derive closed-form average Age of Information (AoI) expressions for reactive HARQ and proactive HARQ, respectively. On the basis of these explicit expressions, and utilizing the existing results for finite-length codes, we formulate an AoI minimization problem to investigate the age-optimal codeblock assignment strategy in the finite block-length (FBL) regime. Through case studies and analytical results, we provide comparative insights between reactive HARQ and proactive HARQ from the perspective of freshness of information. The numerical results and optimization solutions reveal that proactive HARQ draws its strength from both superior age performance and system robustness, thus enabling the potential to provide new system advancement for a freshness-critical status update system. The full paper version of this work is available on the arXiv at https://arxiv.org/abs/2204.01257.

The flip-successive cancellation (SCF) decoding algorithm is a decoding scheme to improve the performance of the SC decoding algorithm under short code length by flipping erroneous bits in initial SC decoding. The degraded performance of the SCF decoding algorithm is usually caused by the wrong locating of the first erroneous bit or additional erroneous bits. To address this issue, we propose a machine learning based multi-flips SC decoding scheme (ML-MSCF), which can improve the performance of the SCF decoding algorithm with multiple flips based on the long short-term memory (LSTM) network and reinforcement learning (RL). Specifically, we use a LSTM network to locate the first erroneous bit when initial SC decoding fails, then the outputs of the LSTM network are used as the action space of RL to identify additional erroneous bits in the followed procedure. Simulation results show that the proposed scheme can achieve performance improvement of 0.2-0.3dB over the stateof-art SCF decoding algorithm on both the bit error ratio (BER) and the frame error rate (FER) with less decoding latency.

Polar codes with short code length under successive cancellation (SC) decoding are inferior to other advanced codes of similar block length. Although more sophisticated algorithms, such as SC list (SCL) decoding and SC stack (SCS) decoding were introduced to address the problem, the complexity of these algorithms has also increased. In this paper, we first propose a novel construction of Polar codes, named index modulated Polar (IM-Polar) codes. This scheme conveys information not only by the information bits in non-frozen channels as conventional Polar codes, but also by the indices of channels, which are activated according to the incoming bit stream. Moreover, we give a specific implementation of IM-Polar codes under cyclic redundancy check (CRC) aided SCL (CA-SCL) decoding. In this implementation, repetition-assisted encoding is employed to improve the accuracy of index detection. It is shown via simulations that the proposed implementation of IM-Polar codes can provide gain of 0.2--0.3 dB over the classical CRC-aided Polar (CA-Polar) codes with code rate 0.357 and code length 128 at the bit error ratio (BER) of $10^{-4}$.