The integration of edge computing and satellite networks has emerged as a promising solution to support remote terrestrial computation with wide coverage and low latency. However, single-satellite computing leads to uneven resource utilization and degraded service quality. To address this, peer offloading is required to improve both service quality and resource efficiency. Asides from peer offloading, diverse service requests also call for an appropriate service deployment strategy, which should be jointly optimize with offloading decision. In this paper, taking into processing cost and service update cost, we formulate a long-term optimization for service deployment and peer offloading. To pursue long-term performance, the problem is first reformulated into a sequence of time-invariant problems. Since frequent service deployment adjustments incur overhead and may cause service interruption, we decompose the time-invariant problem into a service deployment subproblem and a peer offloading subproblem, optimized at different timescales. A hierarchical method iteratively solve the two subproblems. In particular, we propose an online distributed algorithm for small-timescale peer offloading. Each local peer offloading problem is transformed into a capacity-constrained minimum cost maximum flow problem, enabling a low-complexity solution via the successive shortest path algorithm. We provide theoretical analysis showing that the proposed algorithm asymptotically approaches the offline optimum at the expense of system congestion. Moreover, we show that the performance bound grows with the large-timescale interval. Simulations results validate the theoretical analysis and demonstrate the effectiveness of the propose algorithm in terms of processing cost and service update cost.

This paper aims to develop an effective uplink access scheduling strategy for massive Internet-of-Things (IoT) networks. To better reap the benefits of uplink resources, the BS has to adjust the uplink resources relies on the network states available at the BS. However, in massive IoT networks, the acquisition of network states, including traffic arrivals, channel conditions, and energy supply rate, are typically obtained through in-band feedback from devices. Therefore, the network states available at the BS are differently outdated across devices, as the staleness depends on the time elapsed since each device was last scheduled. This motivates us to develop a proactive scheduling scheme that enables the BS to schedule uplink access under differently outdated states information. To combat the performance loss caused by the outdated states information, we propose a novel primal-dual online learning framework. This framework leverages mini-batch gradient descent for dual updates and employs Online Convex Optimization for proactive primal updates, which effectively predicting current network states based on outdated knowledge. We evaluate the performance of the proposed proactive scheduling scheme against the offline optimum, which is optimized using prior knowledge of network states. The performance analysis shows that the proactive scheme asymptotically approaches to the offline optimum. Simulation results further validate the effectiveness of the proposed algorithm by comparing to other benchmarks.

Abstract Massive multiple‐input multiple‐output and non‐orthogonal multiple access (MIMO‐NOMA) with low‐resolution analog‐to‐digital converters (ADCs) have been widely considered for the next‐generation wireless communication systems. However, the performance of the system including power‐scaling law has not been well investigated for the practical low complexity receivers. Employing the additive quantization noise model, we derive asymptotic approximate expressions of the spectrum efficiency for the system with group successive interference cancellation (GSIC) receivers over Rician fading channels. Based on these approximations, we conduct a unified asymptotic analysis for the system with linear, SIC, and GSIC receivers. The analysis reveals the transmission power can be scaled by the number of antennas for the system with GSIC receivers and shows the effects of crucial parameters including the number of groups, resolution bits, and antennas on the performance. Given a quality of service, the minimum data transmission power is also calculated for each user and the corresponding approximate power allocation is derived. The asymptotic analysis and the accuracy of the power allocation approximation are then verified by simulation results. Numerical results also demonstrate that high spectrum efficiency and energy efficiency can be achieved by the system with medium‐resolution ADCs and low complexity maximum ratio combining‐GSIC receivers with a small number of groups.

We investigate joint power allocation and decoding order selection (PA-DOS) aimed at enhancing the outage performance of non-orthogonal multiple access (NOMA) systems. By considering the diverse target rates of users, new important properties of NOMA are revealed: When users’ target rates satisfy certain conditions, the channel state information (CSI) is not required by PA-DOS to minimize the system outage probability, and different users’ outage probabilities can be minimized simultaneously; When such conditions are not satisfied, the opposite situation occurs. Following these properties, two PA-DOS strategies are designed regarding distinct user priorities, which ensure the minimization of the system outage probability and the user outage probability of the high-priority user. Especially, these strategies do not require CSI or only require one-bit CSI feedback depending on users’ target rates. Analytical and numerical results are provided to demonstrate that the proposed strategies significantly outperform the existing strategies in terms of both system and user outage performance. Furthermore, the results show that although under some target rates the minimum user outage probabilities cannot be simultaneously achieved, they can be closely approached at the same time in the high-signal-to-noise-ratio regime by the proposed strategies.

To enhance the outage performance of downlink non-orthogonal multiple access (NOMA) systems, we investigate opportunistic user pair scheduling (UPS) as well as the joint power allocation and decoding order selection (PA-DOS) for the scheduled user pair. Unlike most existing literature where the UPS and PA-DOS schemes are solely driven by channel conditions, we consider the impact of both the channel conditions and users’ target rates in the scheme design, since these two factors jointly determine the occurrence of transmission outages. Specifically, a two-stage rate-aware scheme for joint UPS and PA-DOS is proposed, which is proved to achieve the minimum system outage probability and guarantee fair access opportunities among users. For performance evaluation, the exact system outage probability achieved by the proposed scheme is derived in closed form, based on which a high-signal-to-noise-ratio asymptotic result is further derived for meaningful insights. Our analytical results reveal a new finding: The diversity order achieved by our proposed scheme, which is also the maximum achievable diversity order of the considered NOMA system, varies with the users’ target rates. Simulations confirm our findings and demonstrate that the proposed scheme can achieve a significant outage performance gain over the existing benchmark schemes.

The issue of high energy consumption and low energy utilization in data center networks (DCNs) has always been the focus of attention of both academia and industry. One general solution is to select a subset of network devices that can meet the traffic transmission requirements, thereby turning off the remaining redundant devices. However, modeling the problem as integer linear programming introduces significant time overhead, while heuristic approaches often suffer from poor generalizability. In this paper, we propose GreenDCN.ai, a closed-loop control system, which utilizes In-band Network Telemetry to collect the network-wide device-internal state, and leverages a Deep Reinforcement Learning-based energy-saving algorithm to make rapid decisions to turn on or off network device ports in response to the real-time network state. The trained GreenDCN.ai can adaptively adjust its energy-saving strategy without human intervention when the DCN topology changes. Besides, based on the regularity of the DCN topology, we design two training complexity reduction methods to address the non-convergence issue under large-scale DCN topologies. Specifically, we split the large-scale DCN topology into sub-topologies for parallel training on each sub-topology without breaking the DCN topology connectivity. Evaluation on software P4 switches suggests that GreenDCN.ai can achieve stable convergence within 590 episodes, generate effective action decisions within$\boldsymbol{79}\upmu\mathrm{s}$, and save about 34% to 39% of the network energy consumption.

Abstract This paper proposes a novel multi‐phase coordinated direct and user‐assisted transmission (MP‐CDUAT) strategy for a non‐orthogonal multiple access system consisting of a base station (BS), K near users (NUs) and a far user (FU). In the first phase, the BS directly serves an opportunistically scheduled NU. For the rest phases, an NU scheduling scheme is developed to jointly select two NUs in each phase, where one receives desired message from the BS and the other one serves as a relay to help FU's receiving. To evaluate the performance of the proposed MP‐CDUAT strategy, the analytical expression of the ergodic capacity (EC) for both NU and FU is derived. With the derived results, the EC scaling of NU and FU are, respectively, derived in the high ρ regime, where the EC scaling of FU increases with the transmission phase, while the EC scaling of NU remains the same. Finally, the numerical and simulation results show that (a) our proposed strategy can improve the performance of FU without affecting the capacity scaling of served NUs; (b) the performance gain achieved by the proposed strategy is more prominent with the increasing number of the transmission phases.

Drawing on the self-determination theory, this study explored the effect of three kinds of low-carbon knowledge (LCB), including system knowledge (SK), action-related knowledge (AK), and effectiveness knowledge (EK), on college students’ low-carbon behavior (LCB) by focusing on the mediating role of low-carbon intrinsic motivation (LCIM) and the moderating role of climate change risk perception (CCRP). Using a sample of 2846 college students in China, this study found that SK has an inverted U-shaped relationship with LCB, but AK and EK positively affect LCB. Moreover, LCIM mediates the relationship between three kinds of LCK and LCB. The moderated path analysis also indicated that CCRP could strengthen the impact of LCB on LCIM. Finally, this study discusses the theoretical significance of these findings and provides some policy suggestions and practical implications for the government.

This paper proposes a new optimal time-switching relaying protocol for wireless-powered decode-and- forward relay networks. The relay has no embedded power supply, and it has to harvest energy from radio-frequency signals transmitted by the source. In specific, we focus on the time-switching mechanism where a fraction of block time is used for energy harvesting (EH) and the remaining is used for information transmission (IT). Equipped with a rechargeable battery, the relay can accumulate the harvested energy and choose a proper transmit power. For system throughput maximization, we propose an optimal time-switching relaying (TSR) protocol to optimally determine the EH time and the relay power, according to the channel state and the battery state. Interestingly, we prove that the optimal EH time is a discrete two-state variable, i.e., the whole block time is used for either IT or EH. With the proposed TSR relaying protocol, the analytical expression for the system throughput is derived. Simulations validate the analysis and demonstrate that a significant throughput improvement can be achieved.