Internet of vehicles (IoV) has attracted much interest recently due to its ubiquitous message exchange and content sharing among smart vehicles with the development of the mobile communication and computation technologies. In this paper, we investigate the policy for jointly communication mode selection, resource block assignment and power control in device- to-device-enabled vehicle-to-vehicle (D2D-V2V)-based IoV comm- unication networks with the purpose of guaranteeing the strict latency and reliability requirements of V2V links while maximizing the sum capacity of vehicle-to-infrastructure (V2I) links. Considering the unknown environment's dynamics as well as the continuous-valued state and action space in IoV networks, we exploit a decentralized actor-critic reinforcement learning model with a new reward function to learn the policy by interacting with the environment. Moreover, we propose an efficient transfer actor-critic learning (ETAC) approach to effectively enhance the learning efficiency and improve the learning convergence speed, in order to support reliable and delay-sensitive vehicular services in IoV networks. Simulation results show that the proposed ETAC approach can effectively reduce the generated interference in IoV networks and ensure the latency and reliability requirements of V2V link, as well as achieve the fast convergence speed and high convergence stability, compared with other existing approaches.

With the advent of sixth generation (6G) Internet of Things (IoT), integrated terrestrial network (TN) and non-terrestrial network (NTN) will play a vital role in enabling new applications and services. However, realizing the potential of 6G integrated TN-NTN requires addressing key challenges like intelligent and optimized control mechanisms for resource management, interference cancellation, and handover management. This paper explores the potential of large language models (LLMs) in intelligent network control for 6G integrated TN-NTN. LLMs can learn complex relationships and patterns from large-scale data, and then be fine-tuned on small labeled datasets, significantly reducing training time and cost. This study examines the main obstacles in the integration of 6G IoT and TN-NTN systems, and further discusses how intelligent control may effectively address those issues. Our suggested approach utilizes LLMs to create efficient anaptive control algorithms that can effectively handle the diverse, ever-changing, and decentralized characteristics of 6G integrated TN-NTN.

Wireless mesh network (WMN) has recently emerged as a promising technology for next-generation wireless networking. In WMNs, many important applications, such as mobile TV and video/audio conferencing, require the support of multicast communication with quality-of-service (QoS) guarantee. In this paper, we address the QoS multicast routing issue in WMNs. Specifically, we propose a novel network graph preprocessing approach to enable traffic engineering and enhance the performance of QoS multicast routing algorithms. In this approach, we employ prioritized admission control scheme and develop a utility-constrained optimal priority gain policy. Extensive simulation results show that our approach can significantly improve the performance of QoS multicast routing in WMNs.

In this paper, we employ selective bit mapping to improve the performance of the LDPC coded QAM scheme for terrestrial DTV broadcasting system. The threshold of message-passing decoding can be considerably lowered by selectively mapping the binary components of LDPC codeword to the positions in the m-tuples to be mapped into 2mQAM symbols. In our approach, the mapping pattern is described by bit-mapping polynomials, based on which density evolution can be applied. The optimization algorithm is developed with two implementation concerns, using the Chinese DTMB standard as an example. Numerical results illustrate that our proposed approach can improve the decoding threshold by 0.05 dB to 0.499 dB depending on the code rate and the order of QAM modulation. Simulation results show that the actual BER improvement varies from 0.09 dB to 0.6 dB with different code-modulation combinations in both single-carrier and OFDM modes.

In the past few years, wireless mesh networks (WMNs) have drawn significant attention from academia and industry as a fast, easy, and inexpensive solution for broadband wireless access. In WMNs, it is important to support video communications in an efficient way. To address this issue, this paper studies the multipath routing for multiple description (MD) video delivery over IEEE 802.11 based WMN. Specifically, we first design a framework to transmit MD video over WMNs through multiple paths; we then investigate the technical challenges encountered. In our proposed framework, multipath routing relies on the maximally disjoint paths to achieve good traffic engineering performance. However, video applications usually have strict delay requirements, which make it difficult to find multiple qualified paths with the least joints. To overcome this problem, we develop an enhanced version of Guaranteed-Rate (GR) packet scheduling algorithm, namely virtual reserved rate GR (VRR-GR), to shorten the packet delay of video communications in multiservice network environment. Simulation study shows that our proposed approach can reduce the latency of video delivery and achieve desirable traffic engineering performance in multipath routing environment.

Min-sum decoding is a low-complexity alternative to the so-called belief propagation and consists in simplification of the nonlinear operation on the log likelihood ratios (LLRs) in the check nodes. The resulting suboptimality may be tempered via appropriate scaling of the LLRs, e.g., the fixed optimal scaling in the normalized min-sum algorithm, and variable scaling algorithms gradually appearing in the literature. However, up to now, none of the papers studied variable scaling both as per iteration and as per different check node degree, due to the prohibitive complexity of multioptimization over space of too many parameters. In this paper, we propose a generalized mutual information (GMI) of LLRs as the criterion to search for the scaling factors for different check node degrees in every iteration in a 1-D thus low-complexity manner. This approach is first analyzed via density evolution, and in addition can be extended to practical LLRs based formulas via Monte Carlo tools to cope with the mismatch issue. Bit error rate simulation results on two low-density parity-check codes show that our proposed GMI metrics have a noticeable gain over the variable scaling schemes that appeared in the literature.