Low earth orbit (LEO) satellite based non-terrestrial networks are a key theme of the upcoming 6G networks. These space networks are proposed to be used for high-mobility use-cases like airplanes and vehicles. The initial access process between a base station (BS) and a user equipment (UE) involves timing advance (TA) value computation at the BS, requiring precise BS location information at the UE. It becomes more challenging in LEO satellite networks due to the fast moving LEO satellites and large pathloss, in addition to the mobile UE. This paper aims to compute the TA and Doppler shift experienced at the UE by modeling the joint system dynamics in a LEO satellite-mobile UE network through an extended Kalman filter (EKF) based recursive Bayesian framework. The framework accurately models the joint system dynamics by considering the LEO satellite acceleration. It constructs the Jacobian to linearize the inherent non-linearities present in the motion. Probabilistic insights regarding the state-update and propagation are also provided. The analytical framework factors in the limited satellite visibility at the UE and the satellite-UE geometry w.r.t. the earth center. The proposed framework is also useful when the satellite and UE clocks are not in sync, with the corresponding clock drift a function of the measured time difference of arrivals. Our results showcase the efficacy and robustness of the proposed EKF framework to estimate the TA and Doppler shift, even at very high UE speeds. The work is expected to be extremely useful in realizing LEO satellite based non-terrestrial networks.

Grid connected and solar powered base stations (BSs) acting as distributed energy sources are increasingly becoming a popular solution to mobile operators. These networks experience double stochasticity due to the space-time variations in energy harvest and BS traffic. Hence, accurate and efficient green energy outage estimation in such networks is a challenging task. In this work, we propose a complutationally efficient cooperative energy transfer based distributed energy bank strategy to alleviate green energy outage and design energy sustainable networks. We first develop low-complexity Markovian frameworks to estimate green energy outage in a standalone BS without energy cooperation (WEC) and a multi-BS energy-cooperative (EC) setting, respectively. For the WEC system, we present a computationally efficient three-state discrete time Markovian statistical model, while the multi-BS EC framework is characterized by a two-state Markov model. The energy outage is studied as a function of capital expenditure (CAPEX), manifesting engineering insights from a service provider's perspective. Subsequently for the EC framework, we formulate a CAPEX optimization problem by jointly optimizing the BS cluster size and solar provisioning on individual BSs. Our results demonstrate that the proposed EC framework alleviates the green energy outage significantly, providing computational efficiency gains and CAPEX savings over the state-of-art approaches.

Realizing energy sustainability is a key theme in sixth generation communications. Along with the emphasis on energy efficiency, operator revenue has emerged as a crucial aspect to make the networks scalable. In this paper, we propose a high altitude platform station (HAPS) aided and power grid connected green communication framework. To design green network, the proposed framework aims to offload excess users with the solar powered terrestrial macro base station (tMBS) to the HAPS mounted MBS (hMBS) in the event of high traffic or low energy harvest. The solar powered tMBS utilizes the power grid connectivity purely for energy selling rather than energy procurement. The inherent communication and energy networks in the proposed system are studied and modeled jointly as a six state discrete time Markov chain. The paper also provides analytical bounds on the solar provisioning required at the hMBS for radio access network functions. The proposed framework is compared with a without offloading and grid energy procurement based competitive state of art, in terms of network quality of service (QoS) and annual operator profit. Our simulation based performance studies demonstrate that the proposed framework under limited hMBS offloading capability offers gains compared to the competitive state of the art, up to 21% enhanced network QoS and 64% increased operator profit.

In the post-fifth generation (5G) era, escalating user quality of service (QoS) strains terrestrial network capacity, especially in urban areas with dynamic traffic distributions. This paper introduces a novel cooperative unmanned aerial vehicle relay-based deployment (CUD) framework in satellite air-ground integrated networks (SAGIN). The CUD strategy deploys an unmanned aerial vehicle-based relay (UAVr) in an amplify-and-forward (AF) mode to enhance user QoS when terrestrial base stations fall short of network capacity. By combining low earth orbit (LEO) satellite and UAVr signals using cooperative diversity, the CUD framework enhances the signal to noise ratio (SNR) at the user. Comparative evaluations against existing frameworks reveal performance improvements, demonstrating the effectiveness of the CUD framework in addressing the evolving demands of next-generation networks.

Intelligent spectrum sharing is one of the key enablers of upcoming sixth generation (6G) communications. Unmanned aerial vehicles (UAVs) have emerged as an attractive low altitude aerial base station (BS), providing on demand capacity especially in urban areas. This work aims to demonstrate the feasibility of coexisting UAV to UAV communication based adhoc network over digital television (DTV) bands governed through latest ATSC 3.0 standards. We propose an adaptive modulation and dynamic subcarrier (AMDS) allocation framework to intelligently allocate the resources at the UAV network through adaptive bit loading and frequency allocations. The work aims to maximize the capacity of the coexisting UAV network in addition to protecting the performance of the TV-receiver from the resultant coexisting network interference. A rate maximization problem is formulated and solved using a low computation complexity based bi-section method. Extensive simulation results indicate that the connected UAV link can achieve up to 40 Mbps capacity when 1 km apart, while coexisting and guaranteeing the performance of the DTV network.

Renewable power provisioning of the base stations (BS) in addition to the traditional power grid connectivity presents an interesting prospect towards realizing green future network services. Designing such dual-powered systems is challenging due to the presence of space-time varying stochasticity in traffic and green energy harvest at each BS. These traffic and green energy imbalances result in non-optimal network green energy utilization and thus resulting in a higher grid energy purchase to the mobile operator. In this paper, we present a novel coverage adjustment and sharing of energy (CASE) framework that exploits the user traffic load and green energy availability imbalances across the networked BSs towards maximizing the operator profit and designing energy sustainable system. The profit maximization problem is formulated considering the networked BSs to have the flexibility of load aware coverage adjustment and green energy sharing capability among themselves, in addition to trading energy with the grid. The proposed CASE framework first leverages the spatio-temporal traffic and energy inhomogeneities and performs load management for maximizing user quality of service (QoS). The CASE strategy then distributes the residual energy imbalance across the BSs and maximizes the utilization of temporal green energy harvest across the BSs. The proposed strategy is compared with only coverage adjustment, only sharing of energy, and a benchmark without CASE based framework. Our simulation results indicate significant improvement in user QoS and operator profit, up to 18% and 39% respectively at high skewness scenario, in addition to fully utilizing the green energy potential in the network.

Achieving self-sustainability has been one of the key challenges in designing smart grid connected residential systems. Solar enabled and power grid connected, dual-powered residential systems is an attractive solution, but it is not carbon free and cost optimal for the end user. This paper proposes temporal energy cooperation among the dual-powered residences as a potential cost and energy efficient solution. Through this paper, we present a microgrid based, multi-residence cooperative energy transfer mechanism to offset the power grid dependency. The developed analytical framework characterizes the green energy storage as a discrete time Markov model and aims to exploit the temporal residential load variations, towards designing self-sustainable systems at a much lower capital expenditure (CAPEX). Our simulation results capture the variation of optimum residence cluster size as a function of energy sharing price and load skewness to become cost profitable. The results also demonstrate a significant reduction in CAPEX, achieved through the proposed energy cooperative framework, over a non-cooperative residential system.

Designing solar-enabled and power grid connected, ‘dual-powered’, cellular networks is challenging due to the double stochasticity arising from energy harvest and user traffic, resulting in spatio-temporally varying traffic-energy imbalances. Improper strategy to optimize the power grid connectivity results in generation of significant carbon footprint. In this paper, we present an analytical framework to mathematically capture the traffic-energy imbalances in such a dual-powered network and propose to improve the temporal network energy utilization by exploiting these imbalances through a cooperative energy sharing mechanism among the base stations (BSs), via the grid infrastructure itself. The cooperative communication system is designed and optimized independently from two perspectives, namely, grid energy procurement and carbon emission minimization (in carbon free ‘energy producer’ mode) and operator revenue maximization (in ‘energy prosumer’ mode). The energy producer mode involves the BSs, without the flexibility to procure energy and acting as distributed energy source to the power grid. The energy prosumer mode provides additional flexibility of grid energy procurement to the BSs in addition to energy sharing and selling. For a given capital expenditure (CAPEX), both the optimization problems are reformulated into convex quadratic problems and closed form expressions for the optimal quanta of energies to be shared/procured through/from the grid are obtained. The optimal CAPEX for the proposed modes of network operation are obtained via linear revenue maximization problem formulation. The results demonstrate that the proposed cooperative energy framework significantly improves the temporal network energy utilization, thereby reducing the grid energy procurement and providing significant revenue gains compared to the state-of-art.

Solar enabled and grid connected “dual-powered” base stations (BSs) have developed as a cost effective solution to network operators. While these networks prevent energy outages and are effective in providing seamless operation for catering to the user quality of service (QoS), they still rely significantly on the power-grid, generating carbon footprint. In this paper, we propose a profitable energy sharing based cooperative framework to reduce the grid energy consumption and to facilitate better utilization of network energy. Traffic-energy imbalances in a dual-powered network often result in some BSs being energy deficient due to spatio-temporal variation of traffic. In such an event, it is proposed that the energy deficient BS, through the proposed energy cooperation (EC) framework, interact with the other networked BSs and requests them to share the deficient energy or a portion of it at a much lower price than the grid price via grid itself. We compare the proposed EC model with a without energy cooperation (WEC) model, where the BSs do not interact with one another and release energy above their storage capacity only to the power-grid for selling. Our results demonstrate that for a two BS network, the EC model provides a significant reduction in grid consumption up to 50% with a 38% gain in operator's revenue at 80% traffic skewness.

Solar-powered and power grid connected green cellular networks are becoming attractive due to low carbon footprint and cost-effectiveness in providing uninterrupted service. In this paper, we analyze the performance of such dual-powered multi-cell network in presence of skewed traffic load across the different base stations (BSs). Cell coverage is decided at the network design stage based on long-term average traffic intensity across the various regions of a multi-cell coverage area. In presence of dynamically-changing skewness of traffic loads across different cells, we propose to adjust the cell coverage to accommodate the traffic and energy availability imbalance in the cells, while the demand for residual energy deficiency for serving the customers is fulfilled through the power grid connectivity. Network service provider's cost with the proposed coverage adjustment based strategy is compared with that of the conventional approach where the individual BSs do not undergo any cell coverage adjustment and seek to provide the maximum network performance. Our analysis and simulation-based performance results demonstrate, that the network performance as well as monetary gains of the service provider are significantly higher with our proposed strategy. For example, at a moderate (30%) traffic skewness, the proposed strategy offers about 4% gain in operator's annual profit, while serving about 8% more users on average at the peak hour. At a very high (80%) skewness, these numbers are respectively about 50% and 39%.