Reconfigurable intelligent surface(RIS)-assisted communication systems have been extensively studied for providing high-precision location services. However, most studies have overlooked the impact ofcarrier frequency offset(CFO) andphase noise(PN) resulting from hardware impairments on localization. This paper presents a novel,alternating optimization(AO)-based algorithm to jointly estimate the CFO, PN, anduser equipment(UE) position inorthogonal frequency division multiplexing(OFDM) systems, where, provided the UE position, closed-form expressions for the CFO and PN are derived per iteration, significantly reducing the complexity and enhancing the stability of the algorithm. Another important aspect is a new RIS phase shift optimization algorithm developed to minimize the analytical lower bound of localization accuracy, hence benefiting localization. The semidefinite relaxation method and Schur complement are utilized to convexify this challenging non-convex optimization problem to a semidefinite program. Simulations demonstrate the effectiveness of the proposed algorithms, with the localization accuracy enhanced by two orders of magnitude. The localization accuracy of the proposed algorithm is close to the analytical lower bound, with a root mean square error of lower than 10−2m.

This paper proposes a movable antenna (MA) empowered scheme for wireless powered and backscattering mobile edge computing (WPB-MEC) system. In the considered system, the access point (AP) transmits signals to enable wireless power transfer (WPT) to wireless devices (WDs) and passive offloading from the WDs to the MEC server. Moreover, the WDs are capable of actively offloading their tasks to the MEC server based on the previously harvested energy. The MAs are deployed at the AP and MEC server to improve the efficiency of WPT and hybrid offloading by flexibly adjusting their positions within an available area. Under this setup, we formulate a sum computational bits (SCBs) maximization problem and propose a block coordinate descent based optimization framework with the successive convex approximation method and the genetic algorithm based particle swarm optimization algorithm to find its accurate solution. Numerical results verify that utilizing the MAs can improve up to 81.77% SCBs compared to the scheme with fixed position antennas.

The sixth-generation (6G) communication research is currently in the early stage, where ultra-reliable low-latency communication (URLLC) is still an important service as in the fifth-generation (5G). Since 6G networks are expected to provide even higher levels of massive connectivity, high spectrum efficiency, high reliability, and low latency than 5G communication, it would confront much more severe spectrum scarcity problems, which make the new radio in unlicensed spectrum (NR-U) technology attractive. However, how to achieve URLLC requirements in NR-U networks is extremely challenging due to interference and collisions among multiple radio access technologies (e.g., WiFi). Therefore, it is urgent to design efficient spectrum-sharing algorithms to support URLLC in emerging 6G networks. In this paper, we develop novel centralized deep reinforcement learning (CDRL) and federated DRL (FDRL) frameworks, respectively, to optimize the downlink URLLC transmission in NR-U and WiFi coexistence systems through dynamically adjusting energy detection (ED) thresholds. Our results show that both CDRL and FDRL approaches have improved the reliability of the NR-U system significantly, but the CDRL framework has sacrificed the reliability of the WiFi system. To guarantee the reliability of the WiFi system while improving the NR-U system, we take fairness into account by redesigning the reward of CDRL.

Grant-free non-orthogonal multiple access (GF-NOMA) is a potential technique to support massive Ultra-Reliable and Low-Latency Communication (mURLLC) service. However, the dynamic resource configuration in GF-NOMA systems is challenging due to random traffics and collisions, that are unknown at the base station (BS). Meanwhile, joint consideration of the latency and reliability requirements makes the resource configuration of GF-NOMA for mURLLC more complex. To address this problem, we develop a novel learning framework for signature-based GF-NOMA in mURLLC service taking into account the multiple access signature collision, the UE detection, as well as the data decoding procedures for the K-repetition GF and the Proactive GF schemes. The goal of our learning framework is to maximize the long-term average number of successfully served users (UEs) under the latency constraint. We first perform a real-time repetition value configuration based on a double deep Q-Network (DDQN) and then propose a Cooperative Multi-Agent learning technique based DQN (CMA-DQN) to optimize the configuration of both the repetition values and the contention-transmission unit (CTU) numbers. Our results show the superior performance of CMA-DQN over the conventional load estimation-based uplink resource configuration approach (LE-URC) in heavy traffic and demonstrate its capability in dynamically configuring in long term for mURLLC service. In addition, with our learning optimization, the Proactive scheme always outperforms the K-repetition scheme in terms of the number of successfully served UEs, especially under the high backlog traffic scenario.

To improve the capacity of cellular systems without additional expenses on licensed frequency bands, the 3rd Gen-eration Partnership Project (3GPP) has proposed New Radio Unlicensed (NR-U). It should be noted that each node in NR-U has to perform the Listen-Before- Talk (LBT) operation before transmission to avoid collisions by other unlicensed radio access technologies (e.g., WiFi). Thus, packets transmissions are prone to delay due to the LBT channel access mechanism. How to achieve Ultra-Reliable and Low-Latency Communications (URLLC) requirements in NR-U networks under the coexistence with WiFi networks is of importance and extremely challenging. In this paper, we develop a novel deep reinforcement learning (DRL) framework to optimize the downlink URLLC trans-mission in the NR-U and WiFi coexistence system through dynamically adjusting the energy detection (ED) thresholds. Our results have shown that the NR-U system reliability has been improved significantly via the DRL compared to that without learning approaches, but with the sacrifice of WiFi system reliability. To address this, we redesigned the reward to take fairness into account, which guarantees the WiFi system reliability while improvina the NR- U system reliability.

Realizing efficient, delay-bounded, and reliable communications for a massive number of user equipments (UEs) in massive Ultra-Reliable and Low-Latency Communications (mURLLC) is extremely challenging as it needs to simultaneously take into account the latency, reliability, and massive access requirements. To support these requirements, the third generation partnership project (3GPP) has introduced grant-free non-orthogonal multiple access (GF-NOMA) with multiple configured-grants (MCGs), where UE can choose any of these grants as soon as the data arrives. In this paper, we develop a novel learning framework for MCG-GF-NOMA systems. We first design the MCG-GF-NOMA model by characterizing each CG. We then formulate the MCG-GF-NOMA resources configuration problem taking into account three constraints. Finally, we propose a Cooperative Multi-Agent based Double Deep Q-Network (CMA-DDQN) algorithm to allocate the channel resources among MCGs to maximize the number of successful transmissions under the latency constraint. Our results show that the MCG-GF-NOMA framework can simultaneously improve the low latency and high reliability performances for mURLLC.

Mobile Internet is an integral part of daily life and the development of the world. The delay of mobile Internet directly affects the income of enterprises that provide Internet services and our living standards. According to research, DNS is one of the two most important factors affecting Internet latency. DNS relies on extensive caching for good performance. Additionally, each DNS zone provides caching to improve DNS response speed, but most caching is recursive. In terms of authoritative caching, except for the root cache, the caches of other tlds are less concerned. This paper analyzes the impact of domestically deployed .com and .net caches on resolution time, hoping to find out the impact of tld cache on DNS resolution speed. To this end, we have an online deployment at China Mobile Communications Group Henan Co., Ltd. A large number of experimental results show that the tld cache can greatly improve the DNS resolution speed and reduce the Internet delay.

Abstract Domain Name System (DNS) is a key infrastructure on the Internet. The Distribution Master (DM) system is used to transmit root zone data from Internet Assigned Numbers Authority (IANA) to the root server. DM is a centralized system that will introduce single points of failure and abuse of authority. To solve the problem of a single point of failure, we adopt the decentralization of blockchain in architecture, and propose a blockchain-based distributed DM architecture, which allows nodes to join and exit at any time. At the technical level, the 3R-PBFT (Replicated, Redundant Practical Byzantine Fault Tolerance) algorithm is proposed to reach a consensus, which increases the security of the system. We use flexible mechanisms to reduce the number of signatures and improve the performance of the system. A threshold signature algorithm is used to ensure the uniqueness of the signature key information, and the increase of DM nodes will not bring about an increase in the number of keys. The advantages of the system structure in stability and scalability were verified by experiments.

Massive Ultra-Reliable and Low-Latency Communications (mURLLC), which integrates URLLC with massive access, is emerging as a new and important service class in the next generation (6G) for time-sensitive traffics and has recently received tremendous research attention. However, realizing efficient, delay-bounded, and reliable communications for a massive number of user equipments (UEs) in mURLLC, is extremely challenging as it needs to simultaneously take into account the latency, reliability, and massive access requirements. To support these requirements, the third generation partnership project (3GPP) has introduced enhanced grant-free (GF) transmission in the uplink (UL), with multiple active configured-grants (CGs) for URLLC UEs. With multiple CGs (MCG) for UL, UE can choose any of these grants as soon as the data arrives. In addition, non-orthogonal multiple access (NOMA) has been proposed to synergize with GF transmission to mitigate the serious transmission delay and network congestion problems. In this paper, we develop a novel learning framework for MCG-GF-NOMA systems with bursty traffic. We first design the MCG-GF-NOMA model by characterizing each CG using the parameters: the number of contention-transmission units (CTUs), the starting slot of each CG within a subframe, and the number of repetitions of each CG. Based on the model, the latency and reliability performances are characterized. We then formulate the MCG-GF-NOMA resources configuration problem taking into account three constraints. Finally, we propose a Cooperative Multi-Agent based Double Deep Q-Network (CMA-DDQN) algorithm to balance the allocations of the channel resources among MCGs so as to maximize the number of successful transmissions under the latency constraint. Our results show that the MCG-GF-NOMA framework can simultaneously improve the low latency and high reliability performances in massive URLLC.

5G New Radio (NR) is expected to support new ultra-reliable low-latency communication (URLLC) service targeting at supporting the small packets transmissions with very stringent latency and reliability requirements. Current Long Term Evolution (LTE) system has been designed based on grant-based (GB) (i.e., dynamic grant) random access, which can hardly support the URLLC requirements. Grant-free (GF) (i.e., configured grant) access is proposed as a feasible and promising technology to meet such requirements, especially for uplink transmissions, which effectively saves the time of requesting/waiting for a grant. While some basic GF access features have been proposed and standardized in NR Release-15, there is still much space to improve. Being proposed as 3GPP study items, three GF access schemes with Hybrid Automatic Repeat reQuest (HARQ) retransmissions including Reactive, K-repetition, and Proactive, are analyzed in this article. Specifically, we present a spatio-temporal analytical framework for the contention-based GF access analysis. Based on this framework, we define the latent access failure probability to characterize URLLC reliability and latency performances. We propose a tractable approach to derive and analyze the latent access failure probability of the typical UE under three GF HARQ schemes. Our results show that under shorter latency constraints, the Proactive scheme provides the lowest latent access failure probability, whereas, under longer latency constraints, the K-repetition scheme achieves the lowest latent access failure probability, which depends on K. If K is overestimated, the Proactive scheme provides lower latent access failure probability than the K-repetition scheme.

NarrowBand-Internet of Things (NB-IoT) is a new 3GPP radio access technology designed to provide better coverage for a massive number of low-throughput low-cost devices in delay-tolerant applications with low power consumption. To provide reliable connections with extended coverage, a repetition transmission scheme is introduced to NB-IoT during both Random Access CHannel (RACH) procedure and data transmission procedure. To avoid the difficulty in replacing the battery for IoT devices, the energy harvesting is considered as a promising solution to support energy sustainability in the NB-IoT network. In this work, we analyze RACH success probability in a self-powered NB-IoT network taking into account the repeated preamble transmissions and collisions, where each IoT device with data is active when its battery energy is sufficient to support the transmission. We model the temporal dynamics of the energy level as a birth-death process, derive the energy availability of each IoT device, and examine its dependence on the energy storage capacity and the repetition value. We show that in certain scenarios, the energy availability remains unchanged despite randomness in the energy harvesting. We also derive the exact expression for the RACH success probability of a randomly chosen IoT device under the derived energy availability, which is validated under different repetition values via simulations. We show that the repetition scheme can efficiently improve the RACH success probability in a light traffic scenario, but only slightly improves that performance with very inefficient channel resource utilization in a heavy traffic scenario.

NarrowBand-Internet of Things (NB-IoT) is a new 3GPP radio access technology designed to provide better coverage for Low Power Wide Area (LPWA) networks. To provide reliable connections with extended coverage, a repetition transmission scheme and up to three Coverage Enhancement (CE) groups are introduced into NB-IoT during both Random Access CHannel (RACH) procedure and data transmission procedure, where each CE group is configured with different repetition values and transmission resources. To characterize the RACH performance of the NB-IoT network with three CE groups, this paper develops a novel traffic-aware spatio-temporal model to analyze the RACH success probability, where both the preamble transmission outage and the collision events of each CE group jointly determine the traffic evolution and the RACH success probability. Based on this analytical model, we derive the analytical expression for the RACH success probability of a randomly chosen IoT device in each CE group over multiple time slots with different RACH schemes, including baseline, back-off (BO), access class barring (ACB), and hybrid ACB and BO schemes (ACB&BO). Our results have shown that the RACH success probabilities of the devices in three CE groups outperform that of a single CE group network but not for all the groups, which is affected by the choice of the categorizing parameters.This mathematical model and analytical framework can be applied to evaluate the performance of multiple group users of other networks with spatial separations.

NarrowBand-Internet of Things (NB-IoT) is a new 3GPP radio access technology designed to provide better coverage for Low Power Wide Area (LPWA) networks. To provide reliable connections with extended coverage, a repetition transmission scheme and up to three Coverage Enhancement (CE) groups are introduced into NB-IoT during both Random Access CHannel (RACH) procedure and data transmission procedure, where each CE group is configured with different repetition values. Rather than our previous work only modeled RACH success probability in NB-IoT networks with a single CE group, this paper develops a novel model to analyze the RACH success probabilities in NB-IoT networks with three CE groups, which allow flexible RACH configuration for each CE group. Based on this analytical model, we derive the expression for the RACH success probability of a randomly chosen IoT device in each CE group. The analytical results can also be extended to analyze multiple group users of other networks with spatial separations.

To provide reliable connections with extended coverage in NarrowBand-Internet of Things (NB-IoT), a repetition transmission scheme is introduced during both Random Access CHannel (RACH) procedure and data transmission procedure. To avoid the difficulty in replacing the battery for IoT devices, energy harvesting from natural resources is considered to be a promising solution to support energy sustainability of NB-IoT network. In this work, we analyze RACH in the self-powered NB-IoT network taking into account the repeated preamble transmission and collision using stochastic geometry. We model the temporal dynamics of the energy level as a birth-death process, and we derive the energy availability of each IoT device and examine its dependence on the energy storage capacity, the cutoff value, and the repetition value. We also derive the exact expression for the RACH success probability of NB-IoT network under time correlated interference and the energy availability, which is validated under different repetition values via practical packet evolution simulations.

This paper investigates the problem of link scheduling in a single-hop energy harvesting wireless body area network (EH-WBANs) where sensor devices's energy harvesting rates and data rates are spatially heterogeneous and temporally variant. To maximize the channel utilization with the lifetime operation, an adaptive TDMA-based protocol (AT-MAC) is proposed, which is suitable for communication in an EH-WBAN for remote monitoring of physiological signals. In this protocol, a duty cycle can be dynamically adjusted to maintain the harvested energy amount, which is always greater than power consumption. Also, a novel time-slot allocation algorithm is designed to automatically adjust duty cycle with various data traffic and harvesting rates. This algorithm is decomposed into two sub-processes: predistribution of time-slot and redistribution of time-slot. The former process takes the information of energy harvesting rates into account, regardless of the rates varying in a nondeterministic manner and among various sensor nodes. For the latter process, the number of distributed time slots will be further adjusted to cope with data traffic of spatially heterogeneousness. Simulation results demonstrate the proposed protocal is a promising candidate for realizing the lifetime operation in EH-WBANs.

In Energy-Harvesting Wireless Body Area Networks (EH-WBANs), how to realize a power management strategy to enable lifetime operation is one of the most concern problems. To solve this problem, a feasible solution should be used to manage the harvested energy in sensor nodes in an EH-WBAN. So far, there are extensive studies about this issue for EH-WBANs. Unfortunately, the significant character of energy harvesting rates is ignored in existing solutions, which has immediate impact on the effectiveness and efficiency of energy harvesting in EH-WBANs. To handle this critical challenge, this paper propose a harvesting-rate oriented self-adaptive algorithm for enabling lifetime operation in EH-WBANs, in which a duty cycle can be dynamically adjusted to maintain the harvested energy amount always greater than the power consumption. In this study, the widely cited TDMA-based frame structure is chosen as the EH-WBAN architecture as well. To exploit the full potentials of the TDMA-based frame structure for energy harvesting, a self-adaptive mechanism is devised to adjust the on-to-off ratio within a duty cycle automatically in the proposal, in which the information of energy harvesting rates can be taken into account, regardless of the rates how varying in a nondeterministic manner and among various sensor nodes in EH-WBANs. To evaluate this algorithm, a demo is configured, which test results demonstrate the proposal is a promising candidate for realizing the lifetime operation in EH-WBANs.

This paper presents an improved routing algorithm based on the social link awareness. In this algorithm, multiple social features of the nodes' behaviors are utilized to quantify the nodes pairs' social links. The social links of the nodes pairs are computed based on their encounter history. These social links can be used to construct the friendship communities of the nodes. The intra-community and inter-community forwarding mechanisms are implemented to raise the successful delivery ratio with low overhead and decrease the transmission delay. Simulation results show that the proposed algorithm shortens the routing delay and the overhead, and increases the successful delivery ratio, thereby improving the routing efficiency.