User Selection Scheme Research Articles

A Privacy-Preserving and Quality-Aware User Selection Scheme for IoT

In the Internet of Things (IoT), the selection of mobile users with IoT-enabled devices plays a crucial role in ensuring the efficiency and accuracy of data collection. The reputation of these mobile users is a key indicator in selecting high-quality participants, as it directly reflects the reliability of the data they submit and their past performance. However, existing approaches often rely on a trusted centralized server, which can lead to single points of failure and increased vulnerability to attacks. Additionally, they may not adequately address the potential manipulation of reputation scores by malicious entities, leading to unreliable and potentially compromised user selection. To address these challenges, we propose PRUS, a privacy-preserving and quality-aware user selection scheme for IoT. By leveraging the decentralized and immutable nature of the blockchain, PRUS enhances the reliability of the user selection process. The scheme utilizes a public-key cryptosystem with distributed decryption to protect the privacy of users’ data and reputation, while truth discovery techniques are employed to ensure the accuracy of the collected data. Furthermore, a privacy-preserving verification algorithm using reputation commitment is developed to safeguard against the malicious tampering of reputation scores. Finally, the Dirichlet distribution is used to predict future reputation values, further improving the robustness of the selection process. Security analysis demonstrates that PRUS effectively protects user privacy, and experimental results indicate that the scheme offers significant advantages in terms of communication and computational efficiency.

DNN-based algorithm for joint SIC ordering and power allocation in downlink NOMA-enabled heterogeneous networks

In the heterogeneous network (HetNet) employing downlink non-orthogonal multiple access (NOMA), we focus on the non-convex optimization problem to optimize the spectral efficiency (SE) while the users satisfy the quality-of-service (QoS) requirement. In the previous work, the optimal joint successive interference cancellation and power allocation (JSPA) algorithm for maximizing SE is proposed to solve the mixed-integer non-linear programming (MINLP) problem in NOMA-enabled HetNet. However, the optimal solution requires exponential complexity by the number of base stations (BSs). Therefore, we present a deep neural network (DNN)-based algorithm for JSPA to reduce the complexity. In particular, to deal with the MINLP-based JSPA problem, we reformulate it into an equivalently simple problem that optimizes only the power consumption of BSs. Then, we introduce the unsupervised DNN-based method for JSPA to handle the simplified problem. The presented scheme yields improved SE and outage performance compared with traditional DNN-based methods. Additionally, we propose a user selection scheme with low complexity to enhance the SE of the proposed DNN-based power allocation. Through simulations, we illustrate that the suggested DNN-based scheme can attain SE performance similar to that of the optimal scheme.

Low-Complexity User Scheduling Framework for mmWave Hybrid Beam-forming OFDM System under Non-Ideal CSI

Introduction: User scheduling in millimeter-wave (mmWave) multi-user hybrid beam-forming Orthogonal Frequency Division Multiplexing (OFDM) systems involves the joint optimization of resource block group (RBG) allocation, beam pairing, and user selection. However, choosing the optimal scheduled User Equipment (UE), allocated RBGs, and communicating beams in practical mmWave hybrid beam-forming systems with non-ideal Channel State Information (CSI) remains challenging. Method: In this paper, we propose a low-complexity user scheduling framework. On one hand, two user classification methods are proposed under non-ideal CSI, assisted by beam search and RBG allocation respectively. On the other hand, in order to ensure both the throughput and fairness performance, a novel user selection scheme, called weighted user selection based on feedback threshold (WUSFT) scheme is proposed, and approximate closed-form expression of fairness index for both full feedback and feedback based on threshold are derived. Our proposed methods consist of three steps. First, RBGs are allocated based on the maximum received signal power (MRSP) of each user on each RBG, utilizing quasi-omnidirectional beams. In the second step, the communicating beams are further searched to achieve the MRSP with the allocated RBG. Result: Finally, the allocated RBG, or the determined beam information obtained through the beam search, is used to represent the correlation of user channels and classify the UEs into groups. Only UEs whose reported factor is not less than the feedback threshold will report received information. This simplifies the operation of user scheduling. Moreover, simulation results demonstrate that our proposed schemes can achieve more than 92.3% of the sum rate performance of exhaustive search methods while maintaining relatively low complexity. Conclusion: In addition, the user feedback overhead (FO) reduces obviously, especially with large UE number.

Resource allocation for integrated satellite‐terrestrial networks based on RSMA

AbstractTo achieve high data rate transmission of virtual reality (VR) applications globally, a resource allocation algorithm based on Rate‐Splitting Multiple Access (RSMA) in an integrated satellite‐terrestrial network (ISTN) is proposed. In this network, a user selection strategy is proposed. The maximum transmission power budgets of the base station and satellite, as well as the service quality constraints for all users, are considered. Then, a joint resource allocation problem is formulated. To tackle the non‐convex problem, auxiliary variables are introduced and the first‐order Taylor expansion method is used to convert it into a convex one. An iterative algorithm is proposed based on the successive convex approximation (SCA) to allocate rate and power resources. Simulation results demonstrate that the proposed algorithm significantly enhances the transmission rate and effectively satisfies the VR transmission rate requirements, when compared to benchmark algorithms.

Blockchain-Based Hybrid Reliable User Selection Scheme for Task Allocation in Mobile Crowd Sensing

Blockchain-Based Hybrid Reliable User Selection Scheme for Task Allocation in Mobile Crowd Sensing

On the performance of outage probability in cognitive NOMA random networks with hardware impairments

ABSTRACT The performance of the cognitive non-orthogonal multiple access (NOMA) systems with random locations is examined in the present paper. More precisely, the positions of the secondary receivers (SRs) are randomly distributed inside a disk with the centre at the secondary transmitter (ST). The primary networks (PNs), on the other hand, are not necessarily inside the transmission range of the ST. Two user selection schemes namely, path-loss-based and channel-gain-based approaches, are adopted to serve two SRs according to the power domain NOMA technique. Additionally, the impact of the imperfect hardware impairments (HIs) on both transmitters and receivers is also taken into consideration. Furthermore, a recently proposed transmit power allocation at the ST is employed to concurrently maximize the system performance of the secondary networks (SNs) and to strictly protect the quality-of-service (QoS) of the PNs. The correctness of the developed mathematical frameworks is corroborated by simulation results based on the Monte Carlo method. Finally, we unveil that there is no scheme that always outperforms another. Thus, an adaptive scheme can be proposed to optimize the networks' performance. Our findings also reveal that the outage probability (OP) under the harmful effect of HI can be effectively mitigated by increasing the transmit power of the ST. Additionally, increasing the transmit power of the PT is also beneficial for the SNs with the adopted transmit power allocation at the ST.

An efficient federated learning framework deployed in resource-constrained IoV: User selection and learning time optimization schemes

An efficient federated learning framework deployed in resource-constrained IoV: User selection and learning time optimization schemes

User Pairing for Delay-Limited NOMA-Based Satellite Networks with Deep Reinforcement Learning.

In this paper, we investigate a user pairing problem in power domain non-orthogonal multiple access (NOMA) scheme-aided satellite networks. In the considered scenario, different satellite applications are assumed with various delay quality-of-service (QoS) requirements, and the concept of effective capacity is employed to characterize the effect of delay QoS limitations on achieved performance. Based on this, our objective was to select users to form a NOMA user pair and utilize resource efficiently. To this end, a power allocation coefficient was firstly obtained by ensuring that the achieved capacity of users with sensitive delay QoS requirements was not less than that achieved with an orthogonal multiple access (OMA) scheme. Then, considering that user selection in a delay-limited NOMA-based satellite network is intractable and non-convex, a deep reinforcement learning (DRL) algorithm was employed for dynamic user selection. Specifically, channel conditions and delay QoS requirements of users were carefully selected as state, and a DRL algorithm was used to search for the optimal user who could achieve the maximum performance with the power allocation factor, to pair with the delay QoS-sensitive user to form a NOMA user pair for each state. Simulation results are provided to demonstrate that the proposed DRL-based user selection scheme can output the optimal action in each time slot and, thus, provide superior performance than that achieved with a random selection strategy and OMA scheme.

NOMA or OMA in Delay-QoS Limited Satellite Communications: Effective Capacity Analysis

In this paper, we theoretically study the achievable capacity of orthogonal and non-orthogonal multiple access (OMA and NOMA) schemes in supporting downlink satellite communication networks. Considering that various satellite applications have different delay quality-of-service (QoS) requirements, the concept of effective capacity is introduced as a delay-guaranteed capacity metric to represent users’ various delay requirements. Specifically, the analytical expressions of effective capacities for each user achieved with the NOMA and OMA schemes are first studied. Then, approximated effective capacities achieved in some special cases, exact closed-form expressions of users’ achievable effective capacity, and the capacity difference between NOMA and OMA schemes are derived. Simulation results are finally provided to validate the theoretical analysis and show the suitable limitations of the NOMA and OMA schemes, such as the NOMA scheme is more suitable for users with better channel quality when transmit signal-to-noise (SNR) is relatively large, while it is suitable for users with worse link gain when transmit SNR is relatively small. Moreover, the influences of delay requirements and key parameters on user selection strategy and system performance are also shown in the simulations.

User Selection and Power Allocation Scheme With SINR-Based Deep Learning for Downlink NOMA

In non-orthogonal multiple access (NOMA) systems, transmit power allocation plays a crucial role in maximizing the sum rate, while satisfying quality of service requirements of users. In prior study, an interior point method (IPM) has been presented as an iterative algorithm for power allocation, which can maximize the sum rate and minimize the outage rate. However, such iterative algorithms typically suffer from low computational efficiency and excessively high latency. To overcome these limitations, machine learning-based power allocation schemes have been widely studied. Along the same line, in this paper, focusing on unsupervised learning to improve the computational complexity for power allocation as well as user selection, we propose a deep neural network (DNN)-based joint user selection and power allocation technique for the cellular downlink multiple-input single-output(MISO) NOMA systems, where the transmit antenna selection and multi-cell cooperation are considered. Especially, an optimization problem for user selection and power allocation is formulated to maximize the sum rate under the constraint of minimum data-rate requirement. We propose a new loss function and training model for user selection and power allocation to solve the optimization problem, where the training and testing models are based on the signal-to-interference-plus-noise-ratio (SINR) information instead of channel state information, which can significantly reduce the input data size in DNN. Through simulations, we show that our proposed scheme provides comparable performances in terms of sum rate and outage rate to the optimal solutions, while requiring lower computational complexity compared to the optimal ones.

Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning

Secure aggregation is a critical component in federated learning (FL), which enables the server to learn the aggregate model of the users without observing their local models. Conventionally, secure aggregation algorithms focus only on ensuring the privacy of individual users in a single training round. We contend that such designs can lead to significant privacy leakages over multiple training rounds, due to partial user selection/participation at each round of FL. In fact, we show that the conventional random user selection strategies in FL lead to leaking users' individual models within number of rounds that is linear in the number of users. To address this challenge, we introduce a secure aggregation framework, Multi-RoundSecAgg, with multi-round privacy guarantees. In particular, we introduce a new metric to quantify the privacy guarantees of FL over multiple training rounds, and develop a structured user selection strategy that guarantees the long-term privacy of each user (over any number of training rounds). Our framework also carefully accounts for the fairness and the average number of participating users at each round. Our experiments on MNIST, CIFAR-10 and CIFAR-100 datasets in the IID and the non-IID settings demonstrate the performance improvement over the baselines, both in terms of privacy protection and test accuracy.

Optimizing Training Efficiency and Cost of Hierarchical Federated Learning in Heterogeneous Mobile-Edge Cloud Computing

Federated learning (FL), an emerging distributed machine learning (ML) technique, allows massive embedded devices and a server to work together for training a global ML model without collecting user data on a server. Most existing approaches adopt the traditional centralized FL paradigm with a single server: one is the cloud-centric FL paradigm and the other is the edge-centric FL paradigm. The cloud-centric FL paradigm is able to manage a large-scale FL system across massive user devices with high communication cost, whereas the edge-centric FL paradigm is capable of coordinating a small-scale FL system benefiting from the low communication delay over wireless networks. To fully exploit the advantages of both, in this paper, we develop a distinctive hierarchical FL framework for the promising mobile-edge cloud computing (MECC) system, called HELCHFL, to achieve high-efficiency and low-cost hierarchical FL training. In particular, we formulate the corresponding theoretical foundation for our HELCHFL to ensure hierarchical training performance. Furthermore, to address the inherent communication and user heterogeneity issues of FL training, our HELCHFL develops a utility-driven and heterogeneity-aware heuristic user selection strategy to enhance training performance and reduce training delay. Subsequently, by analyzing and utilizing the slack time in FL training, our HELCHFL introduces a device operating frequency determination approach to reduce training energy cost. Experiments demonstrate that our HELCHFL can enhance the highest accuracy by up to 52.93%, gain the training speedup of up to 483.74%, and obtain up to 45.59% training energy savings compared to state-of-the-art baselines.

Location-aided user selection and sum-rate analysis for mmWave NOMA

In this paper, we propose a user selection scheme based on location-aided interference prediction to reduce the training overhead in a non-orthogonal multiple access (NOMA) system. First, we cluster the users based on their location information, enabling the use of non-orthogonal pilot sequence within a cluster and orthogonal pilot sequence between different clusters to reduce the uplink pilot training length. Secondly, we exploit the location information in the computation of the covariance matrices, enabling the prediction of the interference between users. The predicted interference is employed to select the set of users with minimum interference for uplink channel estimation and downlink NOMA data transmission. Finally, the achievable sum-rate of the massive multiple-input multiple-output millimeter wave NOMA system is analyzed. The analytical and numerical results reveal that the location information can be exploited for user selection to reduce the effect of pilot contamination, enhancing the uplink channel estimation and downlink achievable sum-rate.

Outage analysis for user selection based downlink cooperative NOMA network over generalized fading channels

Recently, non-orthogonal multiple access (NOMA) scheme is integrated with cooperative communication (C-NOMA) to increase the connectivity and coverage area. In this paper, we analyzed the outage performance of downlink C-NOMA system with the imperfect successive interference cancellation (I-SIC) and imperfect channel state information (I-CSI) aiding multiple users over κ−μ and η−μ independent and identically distributed fading environments. Further, a user selection scheme is adopted for downlink C-NOMA scheme for enhancing the performance of the system. To be precise, the system model considers multiple users at the destination, out of which two users are selected to serve with NOMA standards. The analytical expressions of outage probability (OP) are derived, analyzed, and compared with cooperative-orthogonal multiple access (C-OMA) scheme over generalized fading environments. Moreover, the accuracy of analytical results is validated through Monte Carlo simulation. In addition, the analytical expression of OP for the paired user is also derived, analyzed, and correlated with the C-OMA scheme. Furthermore, through κ−μ and η−μ fading distribution, the outage performance of Rayleigh, Nakagami-m, Rice, Hoyt, and one-sided Gaussian fading environment is also investigated. From the obtained results, it is evident that when compared to C-OMA scheme, the C-NOMA scheme achieves 4.4 and 6.6 percentage gain in terms of SNR for a near user and a far user, respectively at κ=1.0,μ=1.0 to attain outage of 10−1. On the other hand, for the same outage, C-NOMA achieves 4.6 and 6.7 percentage gain in terms of SNR for a near user and a far user, respectively at η=1.0,μ=1.5.

Blockchain-Enabled Conditional Decentralized Vehicular Crowdsensing System

The rapid growth of connected and autonomous vehicles (CAVs) shows an urgent demand for driving and transportation-related data, which gives rise to vehicular crowdsensing systems (VCSs). Nevertheless, the existing centralized VCS framework mainly faces the system reliability problem while the decentralized one cannot satisfy the management flexibility. In addition, when the privacy preservation scheme that prevents information leakage encounters the user selection scheme that desires detailed information of participants, how to balance this seemingly irreconcilable contradiction is inevitable for VCS. To remedy that, we take the first research attempt and explore the balance point between the system management, privacy preservation, and quality of experience (QoE) of participants. By fully exploiting the characters of participating entities, a blockchain-enabled conditional decentralized VCS is proposed in this paper. Firstly, we propose a privacy-preserving scheme where the zk-SNARK proof combines with the mixed-task smart contract to guarantee the interaction process will not reveal any private information of participants. Secondly, we propose an efficient reputation management mechanism that renders certain the participants can get a satisfactory QoE even under the condition that the private information of users is secured. And also, the malicious operations in the system will be effectively supervised. Theoretical analysis and extensive simulations demonstrate the security and efficiency properties of privacy preservation and indicate the effectiveness of reputation management.

Adaptive Multiuser Cooperative NOMA Scheme With Energy Buffer-Aided Near-Users for High Spectral and Energy Efficiency

In this article, a multiuser cooperative nonorthogonal multiple access (NOMA) network is considered. In order to improve their battery lifetime, the near-user (NU) Internet of Things (IoT) nodes relay information to the far-user (FU) IoT nodes using only the energy harvested from the ambience. The harvested energy at all the NU IoT nodes is stored in energy buffers. To improve the throughput performance, the best NU and best FU (BNBF) user selection scheme is employed. Moreover, unlike conventional orthogonal multiple access (OMA) networks, an adaptive NOMA network is considered in this article that switches between direct NOMA, NOMA with relaying, and OMA modes to maximize throughput. To improve accuracy and reduce the computational complexity, the energy buffer states at the NU IoT nodes are modeled using a continuous state-space Markov chain (CSMC) instead of discrete state-space Markov chain (DSMC). We derive the limiting distributions of the stored energy in energy buffers with either the best effort policy (BEP) or the on–off policy (OOP) applied for buffer energy management. Expressions for throughput are derived for both harvest-store-use (HSU) and harvest-use (HU) architectures. The Monte Carlo simulations are used to validate the derived analytical expressions.

Cooperative spectrum sensing algorithm based on cognitive users selection and Adaboost

In cognitive radio networks, it is a challenge to increase the performance of cooperative spectrum sensing when the number of cognitive users become large. In this paper, a novel spectrum sensing algorithm based on the cognitive users selection strategy and Adaboost classification is proposed by adaptively selecting uncorrelated cognitive radio users with the difference of the maximum degree based on the K‐medoids clustering. Since the proposed users selection strategy is based on the correlation evaluation among the cognitive radio users, it is mandatory to have parameters able to measure the correlation among them and divide into distinct clusters. For this, the spatial correlation coefficient and the clustering quality coefficient are proposed to express the correlation characteristics of cognitive radio users in sensing environments. Performance evaluation is conducted through simulations, and the results are shown to the superior detection performance of the proposed CR user selection strategy based on the K‐medoids clustering for cooperative spectrum sensing.

Performance analysis of SWIPT‐assisted adaptive NOMA/OMA system with hardware impairments and imperfect CSI

AbstractThis paper investigates the effect of hardware impairments (HIs) and imperfect channel state information (ICSI) on a SWIPT‐assisted adaptive nonorthogonal multiple access (NOMA)/orthogonal multiple access (OMA) system over independent and nonidentical Rayleigh fading channels. In the NOMA mode, the energy‐constrained near users act as a relay to improve the performance for the far users. The OMA transmission mode is adopted to avoid a complete outage when NOMA is infeasible. The best user selection scheme is considered to maximize the energy harvested and avoid error propagation. To characterize the performance of the proposed systems, closed‐form and asymptotic expressions of the outage probability for both near and far users are studied. Moreover, exact and approximate expressions of the ergodic rate for near and far users are investigated. Simulation results are provided to verify our theoretical analysis and confirm the superiority of the proposed NOMA/OMA scheme in comparison with the conventional NOMA and OMA protocol with/without HIs and ICSI.

Performance of Adaptive Multi-User Underlay NOMA Transmission With Simple User Selection

In this paper, we demonstrate that despite the power constraints on all transmit nodes caused by the interference temperature limit (ITL) imposed by the primary user, power domain cooperative non-orthogonal multiple access (NOMA) can ensure much higher throughput in underlay cognitive radio networks than conventional orthogonal multiple access (OMA), provided the network is adaptive. We use intelligent user-selection and switching between cooperative NOMA, (non-cooperative) NOMA and OMA to ensure high throughput at the selected far-user while ensuring a desired performance at the selected near-user. The suggested user and mode selection scheme is simple and easy to implement, and yet ensures performance comparable to that attained by a scheme using more information. When the ITL is broadcast by the primary receiver in every coherence interval based on the primary channel, we show that large gains in throughput accrue. Accurate expressions are derived for throughput of the near and far users with these schemes using peak power and ITL-based power constraints. Performance with imperfect successive interference cancellation is also analyzed. Useful asymptotic expressions are derived. Computer simulation results validate the derived expressions.

A Deep Learning-Based User Selection Scheme for Cooperative NOMA System with Imperfect CSI

This paper studies the user selection problem for a cooperative nonorthogonal multiple access (NOMA) system consisting of a base station, a far user, and N near users. The selected near user receives its own message and assists the far user by relaying the far user’s message. Firstly, we propose a user selection strategy to maximize the selected near user’s data rate while satisfying the quality-of-service (QoS) requirement of the far user. Considering that the channel state information (CSI) of users in actual communication is usually imperfect, we then analyze the outage probability of the NOMA system based on the user selection strategy under imperfect CSI and obtain a closed-form expression. The theoretical analysis shows that the diversity order of the NOMA system under imperfect CSI is 0, which means the multiuser diversity order disappears. In order to improve the impact of imperfect CSI on system performance, we use the deep learning method to identify and classify channels of imperfect CSI and improve the accuracy of CSI. The simulation results show that the theoretical analysis of outage performance is consistent with the numerical results. Compared with the strategy without the deep learning method, the proposed deep learning-based user selection scheme significantly improves the system performance. Furthermore, we verify that our scheme recovers the diversity gain.