Reconfigurable intelligent surface (RIS) is envisioned to be an essential component of the paradigm for beyond 5G networks as it can potentially provide similar or higher array gains with much lower ...hardware cost and energy consumption compared with the massive multiple-input multiple-output (MIMO) technology. In this paper, we focus on one of the fundamental challenges, namely the channel acquisition, in a RIS-assisted multiuser MIMO system. The state-of-the-art channel acquisition approach in such a system with fully passive RIS elements estimates the cascaded transmitter-to-RIS and RIS-to-receiver channels by adopting excessively long training sequences. To estimate the cascaded channels with an affordable training overhead, we formulate the channel estimation problem in the RIS-assisted multiuser MIMO system as a matrix-calibration based matrix factorization task. By exploiting the information on the slow-varying channel components and the hidden channel sparsity, we propose a novel message-passing based algorithm to factorize the cascaded channels. Furthermore, we present an analytical framework to characterize the theoretical performance bound of the proposed estimator in the large-system limit. Finally, we conduct simulations to verify the high accuracy and efficiency of the proposed algorithm.
In mobile edge computing (MEC) systems, edge service caching refers to pre-storing the necessary programs for executing computation tasks at MEC servers. Service caching effectively reduces the ...real-time delay/bandwidth cost on acquiring and initializing service applications when computation tasks are offloaded to the MEC servers. The limited caching space at resource-constrained edge servers calls for careful design of caching placement to determine which programs to cache over time. This is in general a complicated problem that highly correlates to the computation offloading decisions of computation tasks, i.e., whether or not to offload a task for edge execution. In this paper, we consider a single edge server that assists a mobile user (MU) in executing a sequence of computation tasks. In particular, the MU can upload and run its customized programs at the edge server, while the server can selectively cache the previously generated programs for future reuse. To minimize the computation delay and energy consumption of the MU, we formulate a mixed integer non-linear programming (MINLP) that jointly optimizes the service caching placement, computation offloading decisions, and system resource allocation (e.g., CPU processing frequency and transmit power of MU). To tackle the problem, we first derive the closed-form expressions of the optimal resource allocation solutions, and subsequently transform the MINLP into an equivalent pure 0-1 integer linear programming (ILP) that is much simpler to solve. To further reduce the complexity in solving the ILP, we exploit the underlying structures of caching causality and task dependency models, and accordingly devise a reduced-complexity alternating minimization technique to update the caching placement and offloading decision alternately. Extensive simulations show that the proposed joint optimization techniques achieve substantial resource savings of the MU compared to other representative benchmark methods considered.
Finite battery lifetime and low computing capability of size-constrained wireless devices (WDs) have been longstanding performance limitations of many low-power wireless networks, e.g., wireless ...sensor networks and Internet of Things. The recent development of radio frequency-based wireless power transfer (WPT) and mobile edge computing (MEC) technologies provide a promising solution to fully remove these limitations so as to achieve sustainable device operation and enhanced computational capability. In this paper, we consider a multi-user MEC network powered by the WPT, where each energy-harvesting WD follows a binary computation offloading policy, i.e., the data set of a task has to be executed as a whole either locally or remotely at the MEC server via task offloading. In particular, we are interested in maximizing the (weighted) sum computation rate of all the WDs in the network by jointly optimizing the individual computing mode selection (i.e., local computing or offloading) and the system transmission time allocation (on WPT and task offloading). The major difficulty lies in the combinatorial nature of the multi-user computing mode selection and its strong coupling with the transmission time allocation. To tackle this problem, we first consider a decoupled optimization, where we assume that the mode selection is given and propose a simple bi-section search algorithm to obtain the conditional optimal time allocation. On top of that, a coordinate descent method is devised to optimize the mode selection. The method is simple in implementation but may suffer from high computational complexity in a large-size network. To address this problem, we further propose a joint optimization method based on the alternating direction method of multipliers (ADMM) decomposition technique, which enjoys a much slower increase of computational complexity as the networks size increases. Extensive simulations show that both the proposed methods can efficiently achieve a near-optimal performance under various network setups, and significantly outperform the other representative benchmark methods considered.
To attain superior fire safety epoxy resins (EP), aluminum diethylphosphonate (AlPi) and nickel alginate were incorporated to EP in different proportions. The synergistic flame retardant effects ...between AlPi and nickel alginate on fire safety and mechanical properties of EP were investigated in detail. EP/AlPi9.5‐Nickel Alginate0.5 acquired the UL‐94 V‐0 rating with the highest limiting oxygen index value (28.9%). Besides, the thermal decomposition behaviors of the samples were researched by thermogravimetric analysis, implying that EP/AlPi‐Nickel Alginate exhibited the better thermal stability and char‐forming ability. Compared with EP, the peak heat release rate and total heat release were declined by 58.3% and 12.8%. And the addition of nickel alginate reduced the release of smoke. In particular, the incorporation of AlPi and nickel alginate increased the impact strength, flexural strength and glass transition temperature of EP. In perspective, the synergistic effect of bio‐based nickel alginate and AlPi opens a practicable avenue in decreasing the fire risk and improving the mechanical properties of EP.
Opportunistic computation offloading is an effective method to improve the computation performance of mobile-edge computing (MEC) networks under dynamic edge environment. In this paper, we consider a ...multi-user MEC network with time-varying wireless channels and stochastic user task data arrivals in sequential time frames. In particular, we aim to design an online computation offloading algorithm to maximize the network data processing capability subject to the long-term data queue stability and average power constraints. The online algorithm is practical in the sense that the decisions for each time frame are made without the assumption of knowing the future realizations of random channel conditions and data arrivals. We formulate the problem as a multi-stage stochastic mixed integer non-linear programming (MINLP) problem that jointly determines the binary offloading (each user computes the task either locally or at the edge server) and system resource allocation decisions in sequential time frames. To address the coupling in the decisions of different time frames, we propose a novel framework, named LyDROO, that combines the advantages of Lyapunov optimization and deep reinforcement learning (DRL). Specifically, LyDROO first applies Lyapunov optimization to decouple the multi-stage stochastic MINLP into deterministic per-frame MINLP subproblems. By doing so, it guarantees to satisfy all the long-term constraints by solving the per-frame subproblems that are much smaller in size. Then, LyDROO integrates model-based optimization and model-free DRL to solve the per-frame MINLP problems with very low computational complexity. Simulation results show that under various network setups, the proposed LyDROO achieves optimal computation performance while stabilizing all queues in the system. Besides, it induces very low computation time that is particularly suitable for real-time implementation in fast fading environments.
In this paper, we propose a novel transactive energy trading (TET) framework to deal with the economic issues in energy trading and the technical issues in distribution system operation in a holistic ...manner. In particular, we innovatively integrate a bilateral energy trading mechanism with the optimal power flow (OPF) technique to increase economic benefits to individual participants, and meanwhile ensure the reliability and security of the system operation. In order to resolve the inherent conflict of interests, Nash bargaining theory is used to model the TET problem, which is further decomposed into a multiperiod OPF problem and a payment bargaining problem. Moreover, we develop an efficient distributed algorithm for solving the TET problem base on alternating direction method of multipliers (ADMM). Instead of directly solving optimization subproblems like most ADMM-based distributed algorithms, we derive closed-form solutions to all subproblems to significantly improve the computational efficiency. Finally, numerical tests on the IEEE 37-bus and 123-bus distribution systems demonstrate the effectiveness of our proposed framework and the efficiency of our distributed algorithm.
Reconfigurable intelligent surfaces (RISs) are regarded as a promising emerging hardware technology to improve the spectrum and energy efficiency of wireless networks by artificially reconfiguring ...the propagation environment of electromagnetic waves. Due to the unique advantages in enhancing wireless channel capacity, RISs have recently become a hot research topic. In this article, we focus on three fundamental physical-layer challenges for the incorporation of RISs into wireless networks, namely, channel state information acquisition, passive information transfer, and low-complexity robust system design. We summarize the state-of-the-art solutions and explore potential research directions. Furthermore, we discuss other promising research directions of RISs, including edge intelligence and physical-layer security.
Soil microbes play important roles in plant growth and in the biogeochemical cycling of earth’s elements. However, the structure and functions of the microbial community associated with the growth of ...second-generation energy crops, such as
Miscanthus
, remain unclear. Thus, in this study, the composition and function of the bacterial and fungal communities associated with
Miscanthus
cultivation were analyzed by MiSeq sequencing combined with PICRUSt and FUNGUIld analyses. The results of community composition and diversity index analyses showed that
Miscanthus
cultivation significantly altered the bacterial and fungal community composition and reduced bacterial and fungal diversity. In addition,
Miscanthus
cultivation increased the soil organic matter (SOM) and total nitrogen (TN) contents. The correlation analysis between microbial community composition and environmental factors indicated that SOM and TN were the most important factors affecting bacterial and fungal communities.
Miscanthus
cultivation could enrich the abundances of
Pseudomonas
,
Rhizobium
,
Luteibacter
,
Bradyrhizobium
,
Phenylobacterium
and other common plant-promoting bacteria, while also increasing
Cladophialophora
,
Hymenula
,
Magnaporthe
,
Mariannaea
, etc., which predicted corresponded to the saprotrophic, plant pathogenic, and pathotrophic trophic modes. The PICRUSt predictive analysis indicated that
Miscanthus
cultivation altered the metabolic capabilities of bacterial communities, including the metabolism of carbon, nitrogen, and phosphorus cycle. In addition, FUNGUIld analysis indicated that
Miscanthus
cultivation altered the fungal trophic mode. The effects of
Miscanthus
on the communities and function of bacteria and fungi varied among
Miscanthus
species.
Miscanthus
specie Xiangdi NO 1 had the greatest impact on soil bacterial and fungal communities, whereas
Miscanthus
specie Wujiemang NO 1 had the greatest impact on soil bacteria and fungi functions. The results of this study provide a reference for the composition and function of microbial communities during the growth of
Miscanthus
.
We consider a two-level profit-maximizing strategy, including planning and control, for battery energy storage system (BESS) owners that participate in the primary frequency control market. ...Specifically, the optimal BESS control minimizes the operating cost by keeping the state of charge (SoC) in an optimal range. Through rigorous analysis, we prove that the optimal BESS control is a "state-invariant" strategy in the sense that the optimal SoC range does not vary with the state of the system. As such, the optimal control strategy can be computed offline once and for all with very low complexity. Regarding the BESS planning, we prove that the minimum operating cost is a decreasing convex function of the BESS energy capacity. This leads to the optimal BESS sizing that strikes a balance between the capital investment and operating cost. This paper here provides a useful theoretical framework for understanding the planning and control strategies that maximize the economic benefits of BESSs in ancillary service markets.
In this paper, we consider a battery aggregator that coordinates a number of distributed battery energy storage systems (BESSs) to provide primary frequency control service in the ancillary service ...market. In particular, we propose a profit-maximizing BESS coordination strategy that is concerned with two operational phases, namely a frequency regulation phase and a state-of-charge (SoC) recovery phase. Regarding the frequency regulation phase, we minimize the regulation failure penalty by optimally coordinating the operation of multiple BESSs in response to local frequency deviations. The proposed coordination algorithm is "online optimal" in the sense that it does not require any knowledge of the future information, and yet achieves exactly the same optimal performance as if the entire future information is known. On the other hand, during idle periods, the BESSs shall recover their SoCs to a proper range to avoid regulation failure in the next frequency excursion event. We propose a SoC recovery strategy that is not only optimal, but also state invariant and separable in the sense that the target SoC range of each BESS neither varies with its own SoC nor depends on the operation of other BESSs. As such, the target SoC ranges can be calculated once and for all, resulting in extremely low run-time complexity. Numerical results based on real power system frequency measurement data show that the proposed algorithm significantly outperforms a number of benchmark algorithms.
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