Fairness-improved and QoS-guaranteed resource allocation for NOMA-based S-IoT network

Jiao, Jian; Liao, Shiyi; Sun, Yunyu; Wu, Shaohua; Zhang, Qinyu

doi:10.1007/s11432-020-3091-6

Cited by 15 publications

(4 citation statements)

References 6 publications

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“…• Second, we monitor the data buffer queue Q i (t) for U E i at S. Considering that the data buffer queue Q i (t) stands for the queue backlog in time slot t, it can be expressed as follows [37],…”

Section: A Transformation Of Optimization Problemmentioning

confidence: 99%

Age of Incorrect Information Minimization for Semantic-Empowered NOMA System in S-IoT

Hong,

Jiao,

Yang

et al. 2024

IEEE Trans. Wireless Commun.

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Satellites can provide timely status updates to massive terrestrial user equipments (UEs) via nonorthogonal multiple access technology (NOMA) in satellite-based Internet of Things (S-IoT) network.However, most of the existing downlink NOMA system are content-independent, which may result redundant transmission in S-IoT with limited resources. In this paper, we design a content-aware sampling policy via a semantic-empowered metric, named Age of Incorrect Information (AoII) to evaluate the freshness and value of status updates simultaneously, and formulate a long-term average AoII minimization problem with three constraints, including average/peak power constraint, network stability and freshness requirement. By regarding the long-term average AoII and three constraints as Lyapunov penalty and Lyapunov drift, respectively, we transform the long-term average AoII minimization problem to minimize the upper bound of Lyapunov drift-plus-penalty (DPP). Then, we utilize the deep reinforcement learning (DRL) algorithm Proximal Policy Optimization (PPO) to design our AoII minimization resource allocation scheme, and solve the non-convex Lyapunov optimization problem to enable the semantic-empowered downlink NOMA system. Simulation results show that our proposed SAC-AMPA scheme can achieve the optimal long-term average AoII performance under less power and bandwidth consumption than state-of-the-art schemes.

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Section: A Transformation Of Optimization Problemmentioning

confidence: 99%

Age of Incorrect Information Minimization for Semantic-Empowered NOMA System in S-IoT

Hong,

Jiao,

Yang

et al. 2024

IEEE Trans. Wireless Commun.

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“…Thus, it is challenging to design an appropriate user-base station association method for 6G HetNets. Such a suitable method must verify the QoS for each user's equipment and guarantee fairness among UE and base stations (BSs) [6]. Following the established understanding of service quality outlined in ITU-T E.800, QoS should encompass all desired performance levels and the service s capacity utilization to address both explicit and implicit security requirements of the user [7].…”

Section: Introductionmentioning

confidence: 99%

Joint Optimization Scheme of User Association and Channel Allocation in 6G HetNets

Alhashimi,

Hindia,

Dimyati

et al. 2023

Symmetry

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The sixth-generation (6G) wireless cellular network integrates several wireless bands and modes with the objectives of improving quality of service (QoS) and increasing network connectivity. The 6G environment includes asymmetrical heterogeneous networks (HetNets) with the intention of making effective use of the available frequencies. However, selecting a suitable gNB and a communication channel that works for users in the network is an enormous challenge in 6G HetNets. This paper investigates a joint user association (UA) and channel allocation (CA) problem in two-tier HetNets by considering the downlink scenario to improve QoS. Our study presents an innovative scheme for user association and channel allocation, wherein the user can be connected to either the macro base station (MBS) or a possible small base station (SBS) in a direct or relay-assisted link. Furthermore, the proposed scheme identifies the optimal channel to be allocated to each user so that the overall network QoS can be maximized. A symmetric matching game-based user association is proposed to find the optimal association for users. Moreover, a modified auction game is applied to allocate the optimal channel by considering the quota of each gNB. Regarding connection probability, throughput, energy efficiency (EE), and spectrum efficiency (SE), the simulation results show that the proposed approach performs well over the state-of-the-art techniques.

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“…In this way, only the served UE has the chance to reduce its AoI, while all other UEs' AoI would increase, which might increase the average AoI and deteriorate the information freshness in the S-IoT downlink network. Therefore, we introduce the NOMA scheme in the S-IoT downlink network, and enable the satellite to simultaneously transmit status updates to multiple UEs via NOMA scheme [8]. Thus, the AoI of UEs can be lower down simultaneously, and the average AoI in S-IoT network can decrease to a lower level compared with OMA scheme.…”

Section: Introductionmentioning

confidence: 99%

Age-Optimal Downlink NOMA Resource Allocation for Satellite-Based IoT Network

Yang

Hong

Wang

et al. 2023

IEEE Trans. Veh. Technol.

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The upcoming satellite-based Internet of Things (S-IoT) has the capability to provide timely status updates to massive terrestrial user equipments (UEs) via non-orthogonal multiple access (NOMA), due to the worldwide coverage inherited from satellite. Considering the constrained power and storage resources while keeping the information freshness in S-IoT, we first propose three constraint conditions including average/peak power constraints, network stability and minimum throughput requirement. Then, we formulate a long-term age of information (AoI) minimization problem under the three constraint conditions. To solve this complex long-term problem, we transform the above mentioned constraints into three queue stability problems via the Lyapunov optimization framework, thus converting our long-term multi-slot stochastic optimization problem into a series of single time slot deterministic optimization problems. Moreover, we leverage the ListNet algorithm to derive the weights of the queue backlog and channel conditions to obtain an optimized power allocation order with linear complexity. Finally, we utilize the particle swarm optimization algorithm to derive the NOMA long-term AoI minimization (AM) power allocation problem within a practical complexity, named NOMA-AM scheme. Simulation results show that the proposed NOMA-AM scheme has the lowest expected weighted sum AoI compared to several benchmark schemes.

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Fairness-improved and QoS-guaranteed resource allocation for NOMA-based S-IoT network

Cited by 15 publications

References 6 publications

Age of Incorrect Information Minimization for Semantic-Empowered NOMA System in S-IoT

Age of Incorrect Information Minimization for Semantic-Empowered NOMA System in S-IoT

Joint Optimization Scheme of User Association and Channel Allocation in 6G HetNets

Age-Optimal Downlink NOMA Resource Allocation for Satellite-Based IoT Network

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