Despite significant advancements in point cloud analysis, reducing energy consumption and improving robustness remain understudied, largely due to the inherent limitations of Convolutional Neural Networks (CNNs). To address this, we take the cue from the primary visual cortex and propose a Dendritic-Connected Continuous-Coupled Neural Network (DC-CCNN), a novel Brain-Inspired Neural Network (BINN) architecture tailored for point cloud analysis. By leveraging the unique characteristics of point clouds, our design combines discrete and continuous encoding, replacing traditional Multilayer Perceptrons (MLPs) with more efficient and robust BINNs. Our approach substantially improves the performance of Brain-Inspired Neural Networks on point analysis tasks and maintaining performance comparable to state-of-the-art methods. Furthermore, DC-CCNN exhibits enhanced robustness against various point cloud deformations and corruptions. Our experimental results demonstrate that DC-CCNN achieves competitive performance on benchmark datasets, making it a promising alternative to traditional deep learning methods for point cloud analysis. With its high efficiency and robustness, DC-CCNN has the potential for widespread adoption in 3D computer vision, robotics, and autonomous systems.

Wireless-powered Internet of Things networks face critical challenges due to the limited energy of sensor nodes and the difficulty of maintaining data freshness. To address these issues, this paper proposes a multi–unmanned aerial vehicle (UAV)-assisted data collection framework to minimize the age of information (AoI). A hierarchical multiple decision strategy (HMDS) is developed to optimize UAV hovering time by dynamically selecting non-orthogonal multiple access or orthogonal frequency division multiple access for intra-cluster transmission according to energy constraints and channel conditions. In addition, a multi-collaborative trajectory optimization (MCTO) scheme is proposed to reduce UAV flight time through joint clustering, partitioning, and trajectory planning. Specifically, radius-adaptive K-means++ clustering, load-balanced spectral partitioning, and a hybrid K-nearest neighbors and elite ant strategy are employed. Simulation results show that the proposed HMDS and MCTO significantly reduce hovering and flight times, thereby effectively improving AoI performance.

Long-term video understanding in intelligent transportation systems (ITS) has advanced significantly with the integration of large language models (LLMs) and vision foundation models. However, existing LLM-based multimodal approaches are limited by context length and memory constraints, restricting their effectiveness to short video scenarios. To address these challenges, we propose AS-Memory, a novel framework that combines adaptive sparse memory with LLMs for efficient and scalable long-term video understanding. AS-Memory introduces a plug-and-play memory bank, a lightweight module designed to seamlessly integrate with existing multimodal LLMs. This memory bank stores and retrieves historical video content, enabling long-term analysis while mitigating context length and GPU memory limitations. To further enhance efficiency, we propose a sparse adaptive mechanism that dynamically compresses redundant features and retains critical information, ensuring effective management of streaming video data. Comprehensive evaluations on long-term video understanding benchmarks demonstrate that AS-Memory consistently outperforms state-of-the-art methods in terms of accuracy. The source code and trained models will be made available to the public.

Age of information (AoI) serves as a critical metric for evaluating information timeliness in health monitoring systems (HMS), particularly for real-time status updates. This paper investigates single-relay HMS with short-packet communications, proposing a two-hop transmission framework to analyze system’s average AoI under three distinct first-hop transmission protocols combined with classical automatic repeat request (CARQ) mechanism in the second hop. Expressions for average AoI are rigorously derived for each scheme, enabling a comprehensive evaluation of the joint impacts of coding length, signal-to-noise ratio, and destination node quantity on system performance. Numerical simulation results show that the use of non-ARQ for the first hop can minimize average AoI under better channel conditions, while CARQ is suitable for scenarios with high reliability requirements, and truncated ARQ shows better overall performance when dealing with the trade-off between information timeliness and reliability.

Age of information (AoI) in unmanned aerial vehicle (UAV)-assisted environmental monitoring of the Internet of Things (IoT) is an important indicator for characterizing system performance. This paper aims to optimize the UAV hovering points and trajectories to improve the system performance by using the radius-adaptive dynamic K-means algorithm (RAD-Kmeans), solving the coverage blindness of sensor nodes (SNs). We introduce the dual-pheromone reinforcement ant colony optimization algorithm (DPR-ACO) of the joint optimization strategy, combined with the whole path feedback and elite reinforcement to improve the search accuracy and the ability to search for excellence globally. Numerical simulation results demonstrate that the proposed algorithm achieves high stability in trajectory design than the traditional one, and significantly reduces the system AoI and flight length, while minimizes the UAV’s energy consumption.

This paper concentrates on the synergetic effect of the hybrid multicast/unicast/device-to-device (D2D) transmission for downlink cell-free massive multiple-input multiple-output (MIMO) Internet-of-Things (IoT) systems. By leveraging on the acquired imperfect channel state information (CSI) both for multicast, unicast, D2D links, particularly, we first derive the closed-form solutions for the multicast, unicast, and D2D transmission links, respectively. After that, based on the practical power consumption model, the achievable sum energy efficiency (EE) analysis is conducted as well by exploiting the achievable sum rate. At last, extensive simulation results are provided to validate the performance of the proposed framework. The obtained results are given to provide promising preliminary insights on the potential of deploying multicast/unicast/D2D in the cell-free massive MIMO topology. It is revealed by the above analysis that some guiding rules of the practical deployment of future. It is noteworthy that when we select the 5, 6, and 8 bits, we can achieve the maximum sum EE, the tradeoff from the total power consumption to the sum rate, and the maximum sum rate, respectively.

As one of the most critical tasks currently, it is essential to excavate the status update performance. In this paper, we combine access control and pilot design to minimize average age of information (AoI) in cell-free (CF) massive multiple-input multiple-output (MIMO) system with grant-free (GF) random access, where the positions of both the access points (APs) and machine-type communication devices (MTCDs) are geographically distributed as independent Poisson point processes (PPPs). Based on the establish two-disk computation model, we frist formulate the access successful probability by leveraging the stochastic geometry for GF transmission. Then, the closed-form expression of the average AoI is approximately derived. The derived results enable us precisely quantify the impact of network parameters on the system-level performance. To minimize the average AoI, a joint access control and pilot design scheme is proposed. Finally, simulation results are provided to furnish invaluable insight into system performance and certificate the validity of the proposed algorithm.

Peak age of information (PAoI) is an important performance metric for mobile edge computing (MEC) systems to characterize the freshness of information. This paper addresses MEC systems with joint sensing, transmission and computation, where sensors sense and transmit computationally intensive packets to local and edge servers. Three computation scenarios are considered: (1) a local computation scenario in which all computation tasks are handled by the local server, (2) an edge computation scenario in which all computation tasks are handled by the edge server, and (3) a partial computation scenario in which some of the computation tasks are handled at the local server, and the rest is handled by the edge server. Considering the exponential distribution of transmission and computation times and using a first-come-first-served queuing policy, a closed expression for the average PAoI is derived. Numerical simulation results show that a reasonable packet sensing rate can reduce the PAoI of the system. In addition, the PAoI of the system can be reduced by adjusting the server computation rate and offloading ratio, so that the MEC system can achieve better performance.

The traditional large-scale and low-power Internet of Things systems cannot meet the requirement of freshness of information. In this paper, a frame-slotted Aloha (FSA) based synchronization strategy is proposed to update information for multi-node access, which makes the information at the receiver side as fresh as possible. Firstly, a multi-point access model of the system from the perspective of FSA is studied, and the corresponding data flow and framework analysis are presented. Secondly, the age of information (AoI) analysis of FSA is conducted, and the expression of the average AoI and the optimal frame size under frame synchronization are derived. Finally, numerical simulation results show that the proposed strategy achieves the lower AoI in FSA based multi-node wireless sensor networks.

This paper focuses on the timely delivery of status updates for a multi-relay assisted energy harvesting (EH) Internet of Things system with short packet communications (SPC), where an EH-powered source transmits status updates to a destination with the aid of multiple EH-powered relays. To improve the energy efficiency and information freshness, an earliest-$\ell $partial relay selection scheme is proposed, in which the earliest$\ell $relays that successfully receive the status update from the source will be selected to forward the status update to the destination, and the selection combining (SC) and maximal-ratio combining (MRC) schemes are used at the destination. Firstly, we investigate the update packet delivery schemes of source-relay (S-R) and relay-destination (R-D) links, and present the packet error probabilities under the SC and MRC schemes based on SPC theory. Secondly, after characterizing the full charge time durations of both the source and relay, we investigate the statistical description of the number of the packet transmission attempts from the source to relay as well as the one from the relay to destination by using order statistics. With the analysis of the evolution of the instantaneous age of information (AoI), the expression for average AoI is derived. Finally, we investigate the average AoI minimization, which is formulated as a coupled triplet problem of the packet lengths at the source and relays and the value of$\ell $. To overcome the coupling, we present the convexity approximation of the average AoI as well as the concise proof. Then, an efficient gradient-based two-step optimal search algorithm is proposed to solve the optimization problem, which searches the local optimal packet lengths for S-R and R-D links by iteratively updating the value of$\ell $until the global optimal objective parameters are found. Numerical analyses give the effect of the key parameters on the average AoI, and the comparison demonstrates that the proposed gradient-based method converges quickly than the conventional greedy search method.

Ultrasonic intra-body communication (IBC) is an emerging technique to enable revolutionary healthcare applications. This paper proposes an ultrasonic index modulation with spread spectrum (UsIM-SS) scheme for IBCs. The information bits consist of modulation bits and index bits. The core idea of UsIM-SS is that the modulation bits are spread by spreading codes and the index bits are leveraged to select the time-hoping sequence to transmit the spreading bits. We also find that the existing ultrasonic wideband (UsWB) and UsIM can be regarded as special cases of the proposed UsIM-SS. Furthermore, the soft decision-based maximum likelihood (S-ML) receiver and hard decision-based ML (H-ML) receiver are proposed for UsIM-SS, providing a trade-off between bit-error rate (BER) and complexity. Theoretical spectrum efficiency (SE) and BER expressions for UsIM-SS using S-ML and H-ML are also derived and verified by extensive Monte Carlo simulations. Both theoretical and simulation results show that UsIM-SS can achieve lower BER and higher SE than existing UsWB and Us 1M techniques.

In this paper, we study the average age of information (AoI) in energy harvesting wireless sensor networks with preemptive transmission. The downlink control signal is transmitted to sensors for allocating wireless resource with ARQ, and two sensors synchronously harvest energy and send the sampled common data to a base station (destination) with truncated HARQ-MRC. Firstly, the energy state transition diagram is characterized by a Markov chain and the construction method of energy transition matrix is introduced. Then, the expression of average AoI is derived for the preemptive transmission. Lastly, numerical results are provided to evaluate the energy and AoI performance of dual-sensor status update systems.

In this paper, we present a new construction of the globally-coupled LDPC (GC-LDPC) codes, referred to as implicit GC-LDPC codes, where the additional global parity-check bits are transmitted using the free-ride coding. The presented GC-LDPC codes have the same rates as the component LDPC codes, avoiding the rate reduction caused by the global parity checks for the conventional GC-LDPC codes. Moreover, the encoders of the component LDPC codes are reusable in the presented GC-LDPC codes, a distinguished feature as compared with the conventional GC-LDPC codes. The simulation results show that the proposed implicit GC-LDPC codes can improve the performance of the component LDPC codes, yielding extra coding gain of up to 0.7 dB (with (3,6)-regular LDPC codes as the component codes) and 0.5 dB (with IEEE 802.16e LDPC codes as the component codes).

This paper focuses on the real-time status update in a multiaccess vehicular network, in which multiple vehicles transmit not only the payload data (e.g., monitoring data) but also the extra data (e.g., driving intention) to a road side unit for scheduling vehicles to improve traffic efficiency and safety. A free-ride code is implemented, where the extra data is encoded by a random but fixed generator matrix and is delivered by superposition on the low-density parity-check (LDPC) coded payload data, consuming neither extra bandwidth nor extra transmit power. Considering the time slotted ALOHA random access protocol, we derive the closed-form expression for average age of information (AoI), and evaluate the AoI for both payload data and extra data. Numerical simulations demonstrate that free-ride codes can not only transmit extra data without extra transmit power, but also reduce the average AoI of extra data without affecting the average AoI of payload data.

This paper focuses on the age and energy tradeoff of a generalized m-th best relay selection scheme in a dual-hop status update system, where the destination associates to the relay with the m-th smallest number of receptions. By considering the short blocklength packet and retransmission, the expressions of the average age of information (AoI) and the average energy cost (EC) are derived and analyzed. Then, the weighted sum of average AoI and average EC is introduced and is minimized to tradeoff the average AoI and average EC. Numerical results show that for the generalized m-th best relay selection scheme, the optimal block length can be found to achieve the age and energy tradeoff. In addition, the best relay is not always the most suitable one to update the status especially when the weight coefficient of EC is much larger than that of AoI.

Real-time and energy-efficient transmissions are two critical demands for status update systems, however it is difficult to meet these requirements simultaneously. This paper focuses on the tradeoff between the average age of information (AoI) and the average energy cost (EC) for two-hop decode-and-forward (DF) relaying networks with short packet transmissions. Both the partial relay selection (PRS) and max-min relay selection (MMRS) schemes are considered. The expressions for the average AoI and the average EC of two-hop relaying networks are derived, and the age-energy tradeoff is also achieved by minimizing the weighted sum of them. Numerical simulations show that both PRS and MMRS schemes have their own advantages to minimize the age and energy cost under different channel conditions, where MMRS is a fairness scheme that takes the channel conditions of two hops into account. In addition, an optimal packet length can always be found to tradeoff the average AoI and average EC in two-hop relaying networks.

Age of information (AoI) and energy efficiency (EE), as two important performance metrics in status update systems, usually cannot be simultaneously optimized. This paper investigates the age and energy tradeoff for multicast networks with retransmissions, where each sensed status update is encoded as a short blocklength packet and is broadcasted to multiple destinations via independent and identically distributed error-prone channels. By considering the stopping threshold, the average AoI and EE expressions for stopping at earliest-l, stopping at preselected-l, and wait-for-all schemes are derived as a function of the packet length. On this basis, the average AoI-EE ratio is introduced and is minimized to trade off age and energy by optimizing the packet length. Numerical results indicate that among the three schemes, earliest-lscheme attains the minimum AoI when the packet length is small, and preselected-lscheme attains the maximum EE when the packet length is large. An optimal packet length can always be found for each scheme to minimize age-energy ratio. Moreover, the selection of stopping thresholdlwill also impact the age and energy performance.

Age of information (AoI) and energy efficiency (EE) are two critical metrics for real-time status update systems. However, these two metrics may not be optimized simultaneously. This paper focuses on the tradeoff between the average AoI and EE of short packets based transmissions in a multicast network with automatic repeat request. The fixed redundancy coding scheme is employed, and the encoded packet is broadcasted to destinations over additive white Gaussian noise channels. The expressions of average AoI and EE are derived and analyzed. In particular, the average AoI-EE ratio is proposed, which is minimized to achieve a tradeoff between average AoI and EE. The numerical results show that there exists an optimal packet length to achieve a compromise between average AoI and EE.

Age of Information (AoI) is a fundamentally important metric to characterize the freshness of information in real-time Internet-of-Things (IoT) monitoring systems. Another important metric is the energy cost for information sensing and transmission. In this article, we investigate the average AoI and energy cost for low-density parity-check coded status update with and without automatic repeat request (ARQ), where the fixed redundancy scheme is employed. The non-ARQ, classical ARQ, truncated ARQ, and truncated hybrid ARQ with chase combining (HARQ-CC) schemes are analyzed and compared. By using the renewal processes theory, the expressions for the average AoI as well as the average energy cost of each considered scheme are derived. Both the lower bound of age and the upper bound of energy are provided. It is shown through simulation results that the average AoI and energy cost are mainly influenced by network parameters in the low signal-to-noise ratio (SNR) region. With short code, the smaller average AoI can be obtained at the cost of more energy consumption. Compared with other schemes, the truncated HARQ-CC achieves the best average AoI and the moderate average energy cost, which is a compromise between the age and energy.

Age of information (AoI) is an important metric in real-time status update communication system to assess the freshness of information. Different from previous works, this paper focuses on the average AoI over additive white Gaussian noise (AWGN) channels. A fixed redundancy (FR) coding scheme is considered, which encodes each k-bits update as an n-bits packet by a low-density parity-check (LDPC) code. By using the renewal-reward theory, a closed-form expression of the average AoI under the FR scheme is derived. Simulation results show that the average AoI relies on Eb/N0, especially in the low Eb/N0region. For different Eb/N0, an optimal code length always exists to minimize the average AoI. In addition, the same average AoI can be achieved with different code lengths in the high Eb/N0region. Hence, in the high Eb/N0region, short codes are preferred especially when the transmission delay is taken into account.

This paper explores the potential of physical layer security for K- tier heterogeneous networks (HetNets) with nonbest user association (UA) scheme. By modeling the spatial positions of network elements in each tier as homogeneous Poisson point processes (PPPs), we first present the non-best UA probability of a typical user equipment associating with the m-th average biased received power (ABRP) with passive eavesdroppers, then the expression of total secrecy probability for the K-tier HetNets is derived with stochastic geometry. Both the analytical and numerical results show that the implementation of the non-best UA scheme can significantly improve the secrecy probability, which indicates that non-best UA could be a promising solution for safeguarding K-tier HetNets. Moreover, we show that the secrecy probability with non-best UA scheme is not always decreasing over the transmission power of the base stations, which is quite different from the best ones.

This paper focuses on a multi‐user full‐duplex massive multiple‐input multiple‐output amplify‐and‐forward relaying system with low‐resolution analogue‐to‐digital convertors (ADCs). By modelling the loop‐interference and quantisation noise as additive noise, we perform the analyses of the achievable spectral efficiency (SE) by considering the two canonical processing schemes, maximum ratio combining/maximal ratio transmission (MRC/MRT) and zero‐forcing receive/zero‐forcing transmission (ZFR/ZFT), respectively. Specially, we first obtain the tractable closed‐form expressions of the achievable SE. Then, we present the asymptotic analyses under three different power‐scaling scenarios. Comparison analysis shows that the low‐resolution quantisers impose more performance loss on ZFR/ZFT schemes than MRC/MRT ones. At the same time, it is found that with different power‐scaling schemes, the systems have different capabilities to restrict the effect of loop interference and low‐resolution ADCs. Specially, the impact of both the loop‐interference and quantisers on achievable SE can be restricted effectively only under the power‐scaling scenario where the relay's transmission power is scaled down with the number of transmit antennas and the sources’ transmission power is fixed. For the power‐scaling case where the transmission powers of both relay and sources are scaled down simultaneously with the number of antennas, only the effect of the loop interference can be restricted, but the one of low‐resolution quantisers remains.