The strategic deployment of energy storage by cellular operators offers a robust solution to improve the resilience and efficiency of smart grids. Traditionally used to ensure uninterrupted operation of cellular Base Stations (BSs) during power grid outages, these storages can now actively participate in the energy flexibility market. In this study, we examine the potential of BS storages to support smart grid ancillary services by allocating a portion of their capacity to balance demand in the smart grid while ensuring that enhanced Mobile Broadband (eMBB) user requirements, such as data rate, are met. This involves utilizing BS stored energy to feed back into the smart grid during peak demand periods or to balance demand within the smart grid. We analyze the impact of eMBB user communication requirements on smart grid Demand Response Management (DRM), formulating a novel joint cellular communication resource allocation and smart grid energy allocation problem. This problem aims to maximize the participation of BSs in DRM, taking into account battery aging and cycling constraints. Simulation results demonstrate significant improvements in DRM and cost-effectiveness for cellular network operators. For example, if a cellular network with 1,200 BSs reduces its users' data rate requirements from 6 Mbps to 1 Mbps, it can increase DRM participation from 30% to 45%. For a power vacancy of approximately -30 MW, this translates to an increase in power vacancy compensation from 9 MW to around 13.5 MW, exceeding the capacity of a regular wind turbine.

Downlink transmission of a nonorthogonal visible light communication (VLC) system, empowered by autonomous aerial vehicles (AAVs), is studied for coexisting enhanced mobile broadband (eMBB), ultrareliable low-latency communication (URLLC), and massive machine-type communication (mMTC) services. A joint resource allocation problem involving user association, transmit power, and flight trajectory of AAVs is formulated, with the goal of characterizing a multiobjective tradeoff as a weighted sum of the power consumption of each AAV and the perceived Quality of Experience (QoE) of its associated eMBB users, while ensuring the service-specific requirements for eMBB, mMTC, and URLLC are met. Assuming the imperfection of channel state information (CSI), we invoke a generalized Benders decomposition (GBD) methodology, leveraging tools from convex optimization and multiagent deep reinforcement learning to address this problem. We further analytically derive the upper and lower bounds on the reward function for each AAV as a learning agent. Extensive simulations confirm that our proposed method outperforms the single-agent counterpart in the literature, with up to a 22% reduction in power consumption and a 13% gain in perceived QoE. Additionally, compared to the globally optimal brute-force method for AAV-user association, our proposed method experiences only a trivial performance loss in a small-scale scenario.

Downlink transmission of non-orthogonal multiple access visible light communication systems empowered by an unmanned aerial vehicle (UAV) is considered for multiplexing enhanced mobile broadband (eMBB) and massive machine type communication (mMTC) services. Accordingly, a resource allocation problem of joint transmit power control and motion trajectory design of the DAVs is formulated, whose goal is to characterize a multi-objective trade-off as a weighted sum of the UAVs' power consumption and the perceived quality-of-experience (QoE) of eMBB users, while ensuring the eMBB and mMTC service-specific requirements. We leverage an alternative decomposition and tools from convex optimization and actorcritic multi-agent deep reinforcement learning to address this problem in an iterative fashion. We analytically derive the upper-and lower-bounds on the reward of the DAVs as the learning agents and demonstrate that the proposed resource allocation method outperforms the similar scheme in literature, by up to 17% average reduced power consumption, as well as 12% average perceived QoE gain.

Integrating the multitude of emerging internet of things (IoT) applications with diverse requirements in beyond fifth generation (B5G) networks necessitates the coexistence of enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) services. However, bandwidth limited and congested sub-6GHz bands are incapable of fulfilling this coexistence. In this paper, we consider a reconfigurable intelligent surface (RIS)-aided wideband terahertz (THz) communication system to this end. In specific, we formulate a resource management problem, aiming at jointly optimizing the reflection coefficient of the RIS elements and the transmit power of the base station, as well as the wideband THz resource block allocation. To solve this problem, we adopt a supervised learning approach relying on optimization, deep learning and ensemble learning methods. Simulation results show that for an RIS of size$11\times 11$, up to 49% spectral efficiency gain is achieved for the eMBB service compared to the counterparts, while ensuring the reliability and latency requirements of the URLLC service. Further, the ensemble learning model can perform real-time resource management at the expense of up to 1% performance loss, compared to the optimization approach.

Passive beamforming in reconfigurable intelligent surfaces (RISs) enables a feasible and efficient way of communication when the RIS reflection coefficients are precisely adjusted. In this paper, we present a framework to track the RIS reflection coefficients with the aid of deep learning from a time-series prediction perspective in a terahertz (THz) communication system. The proposed framework achieves a two-step enhancement over the similar learning-driven counterparts. Specifically, in the first step, we train a liquid state machine (LSM) to track the historical RIS reflection coefficients at prior time steps (known as a time-series sequence) and predict their upcoming time steps. We also fine-tune the trained LSM through Xavier initialization technique to decrease the prediction variance, thus resulting in a higher prediction accuracy. In the second step, we use ensemble learning technique which leverages on the prediction power of multiple LSMs to minimize the prediction variance and improve the precision of the first step. It is numerically demonstrated that, in the first step, employing the Xavier initialization technique to fine-tune the LSM results in at most 26% lower LSM prediction variance and as much as 46% achievable spectral efficiency (SE) improvement over the existing counterparts, when an RIS of size 11×11 is deployed. In the second step, under the same computational complexity of training a single LSM, the ensemble learning with multiple LSMs degrades the prediction variance of a single LSM up to 66% and improves the system achievable SE at most 54%.

Ultra-low latency is the most important requirement of the Tactile Internet (TI), which is one of the proposed services for the next-generation wireless network (NGWN), e.g., fifthgeneration (5G) network. In this paper, a new queuing model for the TI is proposed for the cloud radio access network (CRAN) architecture of the NGWN by applying power domain non-orthogonal multiple access (PD-NOMA) technology. In this model, we consider both the radio remote head (RRH) and baseband processing unit (BBU) queuing delays for each endto-end (E2E) connection between a pair of tactile users. In our setup, to minimize the transmit power of users subject to guaranteeing an acceptable delay of users, and fronthaul and access constraints, we formulate a resource allocation (RA) problem. Furthermore, we dynamically set the fronthaul and access links to minimize the total transmit power. Given that the proposed RA problem is highly non-convex, in order to solve it, we utilize diverse transformation techniques such as successive convex approximation (SCA) and difference of two convex functions (DC). Numerical results show that by dynamic adjustment of the access and fronthaul delays, transmit power reduces in comparison with the fixed approach per each connection. Also, energy efficiency of orthogonal frequency division multiple access (OFDMA) and PD-NOMA are compared for our setup.

The Tactile Internet (TI) is one of the next generation wireless network services with end to end (E2E) delay as low as 1 ms. Since this ultra low E2E delay cannot be met in the current 4G network architecture, it is necessary to investigate this service in the next generation wireless network by considering new technologies such as networks function virtualization (NFV). On the other hand, given the importance of E2E delay in the TI service, it is crucial to consider the delay of all parts of the network, including the radio access part and the NFV core part. In this paper, for the first time, we investigate the joint radio resource allocation (R-RA) and NFV resource allocation (NFV-RA) in a heterogeneous network where queuing delays, transmission delays, and delays resulting from virtual network function (VNF) execution are jointly considered. For this setup, we formulate a new resource allocation (RA) problem to minimize the total cost function subject to guaranteeing E2E delay of each connection. Since the proposed optimization problem is highly non-convex, we exploit alternative search method (ASM), successive convex approximation (SCA), and heuristic algorithms to solve it. Besides, for the NFV-RA, we propose an online heuristic algorithm, and analyze its performance for the TI service. Simulation results reveal that the proposed scheme can significantly reduce the network costs compared to the case where the two problems are optimized separately. Moreover, we compare the online algorithm with its offline counterpart as well as a baseline approach and it is shown that the online algorithm outperforms both of them.