The recent development of unmanned aerial vehicle (UAV) and mobile edge computing (MEC) technologies provides flexible and resilient computation services to mobile users out of the terrestrial ...computing service coverage. In this paper, we consider a UAV-enabled MEC platform that serves multiple mobile ground users with random movements and task arrivals. We aim to minimize the average weighted energy consumption of all users subject to the average UAV energy consumption and data queue stability constraints. We formulate the problem as a multi-stage stochastic optimization, and adopt Lyapunov optimization to convert it into per-slot deterministic problems with fewer optimizing variables. We design two reduced-complexity methods that solve the resource allocation and the UAV movement either in two sequential steps or jointly in one step. Both methods can guarantee to satisfy the average UAV energy and queue stability constraints, meanwhile achieving a tradeoff between the user energy consumption and the length of queue backlog. Simulation results show that the two methods significantly outperform the other benchmark methods including a learning-based method in reducing the energy consumption of ground users. In between, the proposed joint optimization method achieves better performance than the two-stage method at the cost of higher computational complexity.
In this paper, we consider the profit-maximizing demand response of an energy load in the real-time electricity market. In a real-time electricity market, the market clearing price is determined by ...the random deviation of actual power supply and demand from the predicted values in the day-ahead market. An energy load, which requires a total amount of energy over a certain period of time, has the flexibility of shifting its energy usage in time, and therefore is in perfect position to exploit the volatile real-time market price through demand response. We show that the profit-maximizing demand response strategy can be obtained by solving a finite-horizon Markov decision process (MDP) problem, which requires extremely high computational complexity due to continuous state and action spaces. To tackle the high computational complexity, we propose a dual approximate approach that transforms the MDP problem into a linear programing problem by exploiting the threshold structure of the optimal solution. Then, a row-generation-based solution algorithm is proposed to solve the problem efficiently. We demonstrate through extensive simulations that the proposed method significantly reduces the computational complexity of the optimal MDP problem (linear versus exponential complexity), while incurring marginal performance loss. More interestingly, the proposed demand response strategy hits a triple win. It not only maximizes the profit of the energy load, but also alleviates the supply-demand imbalance in the power grid, and even reduces the bills of other market participants. On average, the proposed quadratic approximation and improved row generation algorithm increases the energy load's profit by 55.9% and saves the bills of other utilities by 80.2% comparing with the benchmark algorithms.
Provided with mobile edge computing (MEC) services, wireless devices (WDs) no longer have to experience long latency in running their desired programs locally, but can pay to offload computation ...tasks to the edge server. Given its limited storage space, it is important for the edge server at the base station (BS) to determine which service programs to cache by meeting and guiding WDs' offloading decisions. In this article, we propose an MEC service pricing scheme to coordinate with the service caching decisions and control WDs' task offloading behavior in a cellular network. We propose a two-stage dynamic game of incomplete information to model and analyze the two-stage interaction between the BS and multiple associated WDs. Specifically, in Stage I, the BS determines the MEC service caching and announces the service program prices to the WDs, with the objective to maximize its expected profit under both storage and computation resource constraints. In Stage II, given the prices of different service programs, each WD selfishly decides its offloading decision to minimize individual service delay and cost, without knowing the other WDs' desired program types or local execution delays. Despite the lack of WD's information and the coupling of all the WDs' offloading decisions, we derive the optimal threshold-based offloading policy that can be easily adopted by the WDs in Stage II at the Bayesian equilibrium. In particular, a WD is more likely to offload when there are fewer WDs competing for the edge server's computation resource, or when it perceives a good channel condition or low MEC service price. Then, by predicting the WDs' offloading equilibrium, we jointly optimize the BS' pricing and service caching in Stage I via a low-complexity algorithm. In particular, we first study the differentiated pricing scheme and prove that the same price should be charged to the cached programs of the same workload. Motivated by this analysis, we further propose a low-complexity uniform pricing heuristics.
Leveraging recent advances on mobile edge computing (MEC), edge intelligence has emerged as a promising paradigm to support mobile artificial intelligence (AI) applications at the network edge. In ...this paper, we consider the AI service placement problem in a multi-user MEC system, where the access point (AP) places the most up-to-date AI program at user devices to enable local computing/task execution at the user side. To fully utilize the stringent wireless spectrum and edge computing resources, the AP sends the AI service program to a user only when enabling local computing at the user yields a better system performance. We formulate a mixed-integer non-linear programming (MINLP) problem to minimize the total computation time and energy consumption of all users by jointly optimizing the service placement (i.e., which users to receive the program) and resource allocation (on local CPU frequencies, uplink bandwidth, and edge CPU frequency). To tackle the MINLP problem, we derive analytical expressions to calculate the optimal resource allocation decisions with low complexity. This allows us to efficiently obtain the optimal service placement solution by search-based algorithms such as meta-heuristic or greedy search algorithms. To enhance the algorithm scalability in large-sized networks, we further propose an ADMM (alternating direction method of multipliers) based method to decompose the optimization problem into parallel tractable MINLP subproblems. The ADMM method eliminates the need of searching in a high-dimensional space for service placement decisions and thus has a low computational complexity that grows linearly with the number of users. Simulation results show that the proposed algorithms perform extremely close to the optimum and significantly outperform the other representative benchmark algorithms.
With the increasing adoption of plug-in electric vehicles (PEVs), it is critical to develop efficient charging coordination mechanisms that minimize the cost and impact of PEV integration to the ...power grid. In this paper, we consider the optimal PEV charging scheduling, where the noncausal information about future PEV arrivals is not known in advance, but its statistical information can be estimated. This leads to an "online" charging scheduling problem that is naturally formulated as a finite-horizon dynamic programming with continuous state space and action space. To avoid the prohibitively high complexity of solving such a dynamic programming problem, we provide a model predictive control (MPC)based algorithm with computational complexity O(T 3 ), where T is the total number of time stages. We rigorously analyze the performance gap between the near-optimal solution of the MPC-based approach and the optimal solution for any distributions of exogenous random variables. Furthermore, our rigorous analysis shows that when the random process describing the arrival of charging demands is first-order periodic, the complexity of the proposed algorithm can be reduced to O(1), which is independent ofT. Extensive simulations show that the proposed online algorithm performs very closely to the optimal online algorithm. The performance gap is smaller than 0.4% in most cases.
In practical massive MIMO systems, a substantial portion of system resources are consumed to acquire channel state information (CSI), leading to a drastically lower system capacity compared with the ...ideal case, where perfect CSI is available. In this paper, we show that the overhead for CSI acquisition can be largely compensated by the potential gain due to the sparsity of the massive MIMO channel in a certain transformed domain. To this end, we propose a novel blind detection scheme that simultaneously estimates the channel and data by factorizing the received signal matrix. We show that by exploiting the channel sparsity, our proposed scheme can achieve a degree of freedom (DoF) very close to the ideal case, provided that the channel is sufficiently sparse. Specifically, the achievable DoF has a fractional gap of only 1/T from the ideal DoF, where T is the channel coherence time. This is a remarkable advance for understanding the performance limit of the massive MIMO system. We further show that the performance advantage of our proposed scheme in the asymptotic SNR regime carries over to the practical SNR regime. Numerical results demonstrate that our proposed scheme significantly outperforms its counterpart schemes in the practical SNR regime under various system configurations.
Neuropathic pain has been reported as a type of chronic pain due to the primary dysfunction of the somatosensory nervous system. It is the most serious types of chronic pain, which can lead to a ...significant public health burden. But, the understanding of the cellular and molecular pathogenesis of neuropathic pain is barely complete. Long noncoding RNAs (lncRNAs) have recently been regarded as modulators of neuronal functions. Growing studies have indicated lncRNAs can exert crucial roles in the development of neuropathic pain. Therefore, our present study focused on the potential role of the lncRNA Colorectal Neoplasia Differentially Expressed (CRNDE) in neuropathic pain progression. Firstly, a chronic constrictive injury (CCI) rat model was built. CRNDE was obviously increased in CCI rats. Interestingly, overexpression of CRNDE enhanced neuropathic pain behaviors. Neuroinflammation was induced by CRNDE and as demonstrated, interleukin‐10 (IL‐10), IL‐1, IL‐6, and tumor necrosis factor‐α (TNF‐α) protein levels in CCI rats were activated by LV‐CRNDE. For another, miR‐136 was obviously reduced in CCI rats. Previously, it is indicated that miR‐136 participates in the spinal cord injury via an inflammation in a rat model. Here, firstly, we verified miR‐136 could serve as CRNDE target. Loss of miR‐136 triggered neuropathic pain remarkably via the neuroinflammation activation. Additionally, IL6R was indicated as a target of miR‐136 and miR‐136 regulated its expression. Subsequently, we confirmed that CRNDE could induce interleukin 6 receptor (IL6R) expression positively. Overall, it was implied that CRNDE promoted neuropathic pain progression via modulating miR‐136/IL6R axis in CCI rat models.
In our investigation, we found CRNDE was elevated while miR‐136 was reduced in CCI rats. CRNDE contributed to neuropathic pain via activating the neuroinflammation. As exhibited, interleukin‐10 (IL‐10), IL‐1, IL‐6, and tumor necrosis factor‐α (TNF‐α) protein expression was induced by the increase of CRNDE. Meanwhile, CRNDE could serve as a sponge of miR‐136.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Federated learning (FL) has recently emerged as a promising technology to enable artificial intelligence (AI) at the network edge, where distributed mobile devices collaboratively train a shared AI ...model under the coordination of an edge server. To significantly improve the communication efficiency of FL, over-the-air computation allows a large number of mobile devices to concurrently upload their local models by exploiting the superposition property of wireless multi-access channels. Due to wireless channel fading, the model aggregation error at the edge server is dominated by the weakest channel among all devices, causing severe straggler issues. In this paper, we propose a relay-assisted cooperative FL scheme to effectively address the straggler issue. In particular, we deploy multiple half-duplex relays to cooperatively assist the devices in uploading the local model updates to the edge server. The nature of the over-the-air computation poses system objectives and constraints that are distinct from those in traditional relay communication systems. Moreover, the strong coupling between the design variables renders the optimization of such a system challenging. To tackle the issue, we propose an alternating-optimization-based algorithm to optimize the transceiver and relay operation with low complexity. Then, we analyze the model aggregation error in a single-relay case and show that our relay-assisted scheme achieves a smaller error than the one without relays provided that the relay transmit power and the relay channel gains are sufficiently large. The analysis provides critical insights on relay deployment in the implementation of cooperative FL. Extensive numerical results show that our design achieves faster convergence compared with state-of-the-art schemes.
This paper considers the problem of charging station pricing and plug-in electric vehicles (PEVs) station selection. When a PEV needs to be charged, it selects a charging station by considering the ...charging prices, waiting times, and travel distances. Each charging station optimizes its charging price based on the prediction of the PEVs' charging station selection decisions, and the other station's pricing decision, in order to maximize its profit. To obtain insights of such a highly coupled system, we consider a 1-D system with two competing charging stations and Poisson arriving PEVs. We propose a multileader-multifollower Stackelberg game model, in which the charging stations (leaders) announce their charging prices in stage I and the PEVs (followers) make their charging station selections in stage II. We show that there always exists a unique charging station selection equilibrium in stage II, and such equilibrium depends on the charging stations' service capacities and the price difference between them. We then characterize the sufficient conditions for the existence and uniqueness of the pricing equilibrium in stage I. We also develop a low complexity algorithm that efficiently computes the pricing equilibrium and the subgame perfect equilibrium of the two-stage Stackelberg game.
In this paper, we investigate over-the-air model aggregation in a federated edge learning (FEEL) system. We introduce a Markovian probability model to characterize the intrinsic temporal structure of ...the model aggregation series. With this temporal probability model, we formulate the model aggregation problem as to infer the desired aggregated update given all the past observations from a Bayesian perspective. We develop a message passing based algorithm, termed temporal-structure-assisted gradient aggregation (TSA-GA), to fulfil this estimation task with low complexity and near-optimal performance. We further establish the state evolution (SE) analysis to characterize the behaviour of the proposed TSA-GA algorithm, and derive an explicit bound of the expected loss reduction of the FEEL system under certain standard regularity conditions. In addition, we develop an expectation maximization (EM) strategy to learn the unknown parameters in the Markovian model. We show that the proposed TSA-GA significantly outperforms the state-of-the-art analog compression scheme, and is able to achieve comparable learning performance as the error-free benchmark in terms of final test accuracy.
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