Uplink Power Allocation Research Articles

Survey on Resource Allocation for Future 6G Network Architectures: Cell-Free and Radio Stripe Technologies

Future beyond fifth-generation (B5G) and sixth-generation (6G) communication systems require a higher quality of service (QoS) along with meeting multiple objectives and traffic demands. Consequently, new multi-antenna technologies and massive multiple-input-multiple-output (mMIMO) architectures have been proposed in recent years. This paper delves into the foundational concepts that form the basis for the design of two potential future mMIMO network topologies: cell-free (CF) network and its successor, the radio stripe (RS) system. Key aspects of the mMIMO and CF network concepts are addressed, along with a practical sequential implementation based on RSs. This exploration encompasses intricate details of the channel estimation (CE) phase, as well as the uplink (UL) and downlink (DL) transmission and reception phases. We then focus on analyzing optimization concepts that underpin resource allocation (RA) algorithms, specifically those applied in UL power allocation and access point selection (APS) schemes in both CF and RS networks. This comprehensive understanding serves as a robust foundation for addressing the challenges inherent in achieving the conflicting B5G and 6G major key performance indicators (KPIs), such as enhancements on spectral efficiency (SE), power efficiency (PE), and computational complexity or load balance (LB).

Joint computational and optimized resource allocation in edge computing networks

Fog computing has several undeniable benefits, such as enhancing near-real-time response, reducing transmission costs, and facilitating IoT analysis. This technology is poised to have a significant impact on businesses, organizations, and our daily lives. However, mobile user equipment struggles to handle the complex computing tasks associated with modern applications due to its limited processing power and battery life. Edge computing has emerged as a solution to this problem by relocating processing to nodes at the network’s periphery, which have more computational capacity. With the rapid evolution of wireless technologies and infrastructure, edge computing has become increasingly popular. Nevertheless, managing fog computing resources remains challenging due to resource constraints, heterogeneity, and distant nodes. For delay-sensitive intelligent IoT applications within the fog computing architecture, cooperation and communication processing resources in 6 G and future networks are essential. This study proposes a joint computational and optimized resource allocation (JCORA) technique to accelerate the processing of data from intelligent IoT sensors in a cell association environment. The proposed technique utilizes an uplink and downlink power allocation factor and the shortest job first (SJF) task scheduling system to optimize user fairness and decrease data processing time. This is a complex assignment due to several non-convex limitations. The suggested JCORA-SJF model simultaneously optimizes time partitioning, computing task processing mode selection, and target sensing location selection to maximize the weighted total of task processing and communication performance. The simulation results demonstrate the effectiveness of the proposed JCORA-SJF algorithms, and the system’s scalability is also examined.

A QoS-Aware Uplink Spectrum and Power Allocation With Link Adaptation for Vehicular Communications in 5G Networks

A QoS-Aware Uplink Spectrum and Power Allocation With Link Adaptation for Vehicular Communications in 5G Networks

Joint Subcarrier and Power Allocation for Uplink Integrated Sensing and Communication System

Joint Subcarrier and Power Allocation for Uplink Integrated Sensing and Communication System

User Subgrouping and Power Control for Multicast Massive MIMO Over Spatially Correlated Channels

Massive multiple-input-multiple-output (MIMO) is unquestionably a key enabler of the fifth-generation (5G) technology for mobile systems, enabling to meet the high requirements of upcoming mobile broadband services. Physical-layer multicasting refers to a technique for simultaneously serving multiple users, demanding for the same service and sharing the same radio resources, with a single transmission. Massive MIMO systems with multicast communications have been so far studied under the ideal assumption of uncorrelated Rayleigh fading channels. In this work, we consider a practical multicast massive MIMO system over spatially correlated Rayleigh fading channels, investigating the impact of the spatial channel correlation on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">favorable propagation</i> , hence on the performance. We propose a subgrouping strategy for the multicast users based on their channel correlation matrices’ similarities. The proposed subgrouping approach capitalizes on the spatial correlation to enhance the quality of the channel estimation, and thereby the effectiveness of the precoding. Moreover, we devise a max-min fairness (MMF) power allocation strategy that makes the spectral efficiency (SE) among different multicast subgroups uniform. Lastly, we propose a novel power allocation for uplink (UL) pilot transmission to maximize the SE among the users within the same multicast subgroup. Simulation results show a significant SE gain provided by our user subgrouping and power allocation strategies. Importantly, we show how spatial channel correlation can be exploited to enhance multicast massive MIMO communications.

Joint Computational and Wireless Resource Allocation in Multicell Collaborative Fog Computing Networks

In 6G and future networks, joint optimization of communication and computational resources lays the foundation for various delay-sensitive intelligent IoT services in the fog computing architecture. In this paper, we present a multi-device collaborative computing architecture in the cell association environment to accelerate the processing procedure of data generated by smart IoT devices. In this scenario, a two-tier task scheduling scheme and an uplink and downlink power allocation factor are jointly optimized to reduce the data processing delay and improve fairness among different users, which is in nature a hard problem due to a series of non-convex constraints. To make the problem tractable, the problem is transformed into a smooth non-convex problem with the introduction of auxiliary variables and then decoupled into two subproblems based on the data transmission and processing procedure. Thereafter, different methods such as Successive Convex Approximation (SCA) and Block Successive Upperbound Minimization (BSUM) are employed to reconstruct several upper-bound convex optimization subproblems. Besides, a fast 0–1 binary offloading scheme is proposed based on the original algorithm. Finally, the simulation results depict the effectiveness of the proposed algorithms in detail, and the scalability of the system is also examined.

Traffic-Aware Mean-Field Power Allocation for Ultradense NB-IoT Networks

The narrowband Internet of Things (NB-IoT) is a cellular technology introduced by the third-generation partnership project (3GPP) to provide connectivity to a large number of low-cost Internet of Things (IoT) devices with strict energy consumption limitations. However, in an ultradense small cell network employing NB-IoT technology, intercell interference can be a problem, raising serious concerns regarding the performance of NB-IoT, particularly in uplink transmission. Thus, a power allocation method must be established to analyze uplink performance, control and predict intercell interference, and avoid excessive energy waste during transmission. Unfortunately, standard power allocation techniques become inappropriate as their computational complexity grows in an ultradense environment. Furthermore, the performance of NB-IoT is strongly dependent on the traffic generated by IoT devices. In order to tackle these challenges, we provide a consistent and distributed uplink power allocation solution under spatiotemporal fluctuation incorporating NB-IoT features, such as the number of repetitions and the data rate, as well as the IoT device’s energy budget, packet size, and traffic intensity, by leveraging stochastic geometry analysis and mean-field game (MFG) theory. The effectiveness of our approach is illustrated via extensive numerical analysis, and many insightful discussions are presented.

Deep Learning-Aided Downlink Beamforming Design and Uplink Power Allocation for UAV Wireless Communications with LoRa

In this paper, we consider an unmanned aerial vehicle (UAV) wireless communication system where a base station (BS) equipped multi antennas communicates with multiple UAVs, each equipped with a single antenna, using the LoRa (Long Range) modulation. The traditional approaches for downlink beamforming design or uplink power allocation rely on the convex optimization technique, which is prohibitive in practice or even infeasible for the UAVs with limited computing capabilities, because the corresponding convex optimization problems (such as second-order cone programming (SOCP) and linear programming (LP)) requiring a non-negligible complexity need to be re-solved many times while the UAVs move. To address this issue, we propose novel schemes for beamforming design for downlink transmission from the BS to the UAVs and power allocation for uplink transmission from the UAVs to the BS, respectively, based on deep learning. Numerical results demonstrate a constructed deep neural network (DNN) can predict the optimal value of the downlink beamforming or the uplink power allocation with low complexity and high accuracy.

Dynamic User Clustering and Optimal Power Allocation in UAV-Assisted Full-Duplex Hybrid NOMA System

This paper investigates unmanned aerial vehicles (UAVs)-assisted full-duplex (FD) non-orthogonal multiple access (NOMA) system based cellular network, aiming to improve overall sum-rate throughput of the system through dynamic user clustering, optimal UAV placement and power allocation. Since each UAV operates in FD mode, self-interference (SI), co-channel interference (CCI), inter-UAV interference (IUI) and intra-node interference (INI) dominate the system&#x2019;s performance. Consequently, we propose an unconventional two-stage dynamic user clustering for user nodes (UNs) to reduce the cross-interference in multi-UAV aided FD-NOMA system. Particularly, all UNs are initially clustered into <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> clusters using k-means clustering in the first stage where each cluster is served by an UAV. Furthermore, each cluster is further divided into sub-clusters and each sub-clusters are operated in FD-NOMA scheme. Finally, to control interferences, a sum-rate throughput maximization problem is formulated for each UAV to jointly optimize uplink and downlink power allocation and UAV placement. The joint optimization problem is non-convex and difficult to solve directly, for which we decoupled the original problem by addressing UAV placement and power allocation separately. We first fix the UAV position and then solve the problem iteratively using successive convex approximation (SCA) method. By utilizing brute-force search algorithm, an optimal UAV placement is later performed which corresponds to maximum possible sum-rate throughput. Simulation results demonstrate that the proposed solution for the considered FD-NOMA system outperforms the conventional schemes.

Network sum-rate maximization via joint power allocation and antenna selection for clustered downlink/uplink NOMA networks

This paper considers the problem of joint power allocation and antenna selection (J-PA-AS) for downlink (DL) and uplink (UL) clustered non-orthogonal multiple-access (NOMA) networks. In particular, the goal is to perform antenna selection for each user cluster and allocate transmit power to its users so as to maximize the network sum-rate in the DL and UL directions, while satisfying quality-of-service (QoS) requirements. The formulated problem happens to be non-convex and NP-hard, and thus, there is no systematic or computationally-efficient approach to solve it directly. In turn, a low-complexity two-stage algorithm is proposed. Specifically, the first stage optimally solves the sum-rate maximizing power allocation for each (antenna, user cluster) pair. After that, antenna selection is optimally solved in polynomial-time complexity via the Kuhn–Munkres with backtracking (KMB) algorithm. Extensive simulation results are provided to validate the proposed algorithm, which is shown to efficiently yield the optimal network sum-rate in each link direction, in comparison to the optimal J-PA-AS scheme (solved via a global optimization package), and superior to other benchmark schemes. Light is also shed on the impact of spatial-diversity on the network sum-rate, where it is shown that the greater the number of base-station antennas is, the higher the network sum-rate, and the lower the outage events. Additionally, the significance of decoupling antenna selection in each link direction on the network sum-rate is highlighted. Lastly, the cases of imperfect channel state information (CSI) and imperfect successive interference cancellation (SIC) have been investigated, where it is demonstrated that spatial-diversity gains reduce the adverse effects of imperfect CSI and SIC on the network sum-rate.

Multi-server federated edge learning for low power consumption wireless resource allocation based on user QoE

Federated edge learning (FEL) deploys a machine learning algorithm by using devices distributed on the edge of a network, trains massive local data, uploads the local model to update the parameters after training, and performs alternate updating with global model parameters to reduce the pressure for uplink data transmission, prevent systematic time delay and ensure data security. This paper proposes that an optimal balance between time delay and energy consumption be achieved by optimizing the transmission power and bandwidth allocation based on user quality of experience (QoE) in a multi-server intelligent edge network. Given the limited computing capability of devices involved in FEL local training, the transmission power is modeled as a quasi-convex uplink power allocation (UPA) problem, and a lower energy consumption bandwidth allocation algorithm is proposed for solution-seeking. The proposed algorithm allocates appropriate power to the device by adapting the computing power and channel state of the device, thereby reducing energy consumption. As the theoretical deduction result suggests that additional bandwidth should be allocated to those devices with weak computing capabilities and poor channel conditions to realize minimal energy consumption within the restraint time. The simulation result indicates that, the maximum gain of the proposed algorithm can be optimized by 31% compared with the baseline.

Games in Normal and Satisfaction Form for Efficient Transmission Power Allocation Under Dual 5G Wireless Multiple Access Paradigm

In this paper, to exploit the challenges and potential offered by the simultaneous use of non-orthogonal multiple access (NOMA) and orthogonal frequency division multiple access (OFDMA) transmission options in future 5G wireless systems, we aim at the proper modeling and transformation of the uplink power allocation problem. In particular, in this setting, each user has two degrees of freedom in the decision making process, namely its overall transmission power level, and the corresponding power investment to the OFDMA and/or NOMA based transmissions. The resulting multi-variable power allocation problem is treated and solved under three different perspectives, namely: 1) Games in Normal Form and Nash Equilibrium (NE); 2) Optimization techniques targeting system social welfare through a centralized optimal solution; and 3) Games in Satisfaction Form and Efficient Satisfaction Equilibrium (ESE). Based on these approaches, different solutions and stable operation points are identified and their properties are analyzed. An in depth evaluation and comparison of the various obtained outcomes is achieved, via modeling and simulations. The focus is placed on the impact and the interplay of the NOMA specific features, including the potential over-exploitation of the available bandwidth, the fairness in accessing it, and the interference treatment. It is also shown that, using the satisfaction form games for the users to converge to the ESE, provides an efficient and promising user-centric modeling approach to the power allocation problem, as the system adapts to the users’ application needs, while at the same time eliminates a significant amount of interference.

Uplink Power Allocation for Throughput and Energy Efficiency in Cellular Networks

Uplink Power Allocation for Throughput and Energy Efficiency in Cellular Networks

An efficient data transmission scheme through 5G D2D-enabled relays in wireless sensor networks

Energy-efficient data transmission is a key challenge in Wireless Sensor Networks (WSNs). Cluster heads (CHs) are often used to relay aggregated data from sensor nodes to a central base station (BS). However, the transmission range of such relay nodes is limited, necessitating multihop communication for forwarding data from edge relays to BS. Further, links may not be created due to the unavailability of suitable relays resulting in unreliable networks. The use of cellular device-to-device (D2D) communication eliminates the requirement of very short-range communication of WSNs and also helps in maintaining a good link throughput. In this paper, a cellular D2D-assisted relay communication scheme has been proposed for forwarding data where the issue of link disconnection is solved using dual connectivity of D2D and cellular communication among cluster heads. The proposed scheme, DSPA (D2D Relay Node (DRN) Selection and uplink Power Allocation), uses the Maximum Weight Bipartite Matching for selection of relays. Performance analysis shows that the higher sleeping probability of the sensor nodes improves the overall energy efficiency of the network and also increases the average hop gain.

Joint Uplink Power Allocation Method in Wireless Communication and Positioning Integrated System

With the development of wireless communication, there are usually both communication and positioning requirements. In our previous work, we studied the power allocation algorithm for positioning user (P-User) based on Multi-Scale Non-Orthogonal Multiple Access (MS-NOMA) downlink signal where all communication users (C-Users) have the same power. In the uplink case, the power allocation scheme for both C-User and P-User needs to be considered jointly as all C-Users may have different power. In this paper, we analyze the Quality of Service (QoS) and ranging accuracy based on the MS-NOMA signal by deriving their simple expressions. Then, a two stage joint iterative power allocation algorithm (JIPAA) is proposed. In the first stage, considering the QoS of C-User requirement and total power limitation, the power of P-Users is allocated to obtain optimal ranging performance. In the second stage, the power of C-Users is allocated to minimize the power consumption. With the iterative process, the optimal power of each C-User and P-User is calculated. The numerical results show that the proposed JIPAA algorithm dramatically promotes ranging accuracy under the same energy consumption.

Uplink Power Allocation for Throughput and Energy Efficiency Over Nakagami-m Fading Channels

In this article, power allocation in cellular networks considering Nakagami- m fading is proposed. The objective is to optimize the network energy efficiency and throughput subject to user outage probability constraints. The moment generating function (MGF) is used to derive the exact outage probability over Nakagami- m fading channels. Further, tight upper and lower bounds on the outage probability are derived using the Weierstrass, Bernoulli and exponential inequalities. These bounds are used to characterize the relationship between outage probability and normalized signal to interference plus noise ratio (SINR) in Nakagami- m fading. Power allocation algorithms for throughput maximization and energy efficiency are proposed. The throughput maximization problem has a logarithmic form so a differential method is used to solve this problem. The proposed energy efficiency problem has a nonconvex fractional program form so a parametric transformation is used to convert it to a subtractive optimization problem which can be solved iteratively. Simulation results are presented which show that the proposed schemes provide better performance than existing methods in terms of power consumption, throughput, energy efficiency and outage probability.

Spectral Efficiency of Dynamic Licensed Shared Access

In licensed shared access (LSA) the radio spectrum is dynamically shared between an incumbent and one or more licensee systems. Protective measures are applied to the licensees’ communication activity to protect the normal operation of the incumbent system. Such measures are therefore crucial components of the LSA, and thus fundamentally affect the achievable spectrum efficiency. In this paper, we investigate a vertical LSA including an airport traffic control system, as the incumbent, and a mobile network as the licensee. While some previous works only consider the licensee uplink, we analytically obtain the interference received by the incumbent from the licensee's transmission both in the uplink and downlink. We then obtain optimal uplink and downlink power allocation in the licensee using an optimisation problem with the objective of maximizing licensee's spectral efficiency (SE) subject to the incumbent interference threshold. Furthermore, we investigate the effect of the number of users and cell size on the SE. Our results provide quantitative insights for practical system design and deployment of LSA system. We then examine the whole LSA spectrum utilization by characterising the availability of the LSA spectrum using a tandem queue setting. Using this model we obtain an expression for the spectral utilization as a function of the licensee's achievable spectral efficiency and the statistics of the LSA spectrum availability. Simulation results show more than a seven-fold improvement in the licensee SE using the optimal power allocation. It is also seen that a higher SE gain is achieved with the proposed optimal power allocation in cases where the number of user equipment in the eNodeB coverage area is very small. Furthermore, higher spectrum utilization efficiency is achieved as a result of shorter busy period and higher achievable SE for distant cells.

MMSE‐based transmission method for wireless powered communication networks

In this study, the authors consider the user rate optimisation for wireless powered communication networks (WPCNs). To improve the user rate, a novel minimum mean square error (MMSE)-based transmission method is proposed for WPCNs. For maximising the user rate, a user rate optimisation problem with MMSE constraints in the uplink transmission is formulated, which is considered as a convex optimisation problem. Considering the uplink power allocation, downlink energy beamforming, and the time ratio between uplink and downlink durations, a novel Newton iterative algorithm is proposed to obtain the optimal solution of the optimisation problem. Furthermore, the computational complexity of the MMSE-based method is illustrated by theoretical analysis. Simulation results show that the MMSE-based method can effectively improve the user rate compared to the zero-forcing suboptimal method and time division multiple access-energy harvesting method. Moreover, simulation results also show that the experimental runtime tends to coincide with the theoretical runtime in the MMSE-based method.

Joint energy efficient power and subchannel allocation for uplink MC‐NOMA networks

SummaryDue to the exponential rise in the number of subscribers in existing wireless networks, energy‐efficient distribution and utilization of network resources have become the preliminary objective of researchers nowadays. Nonorthogonal multiple access (NOMA) has attained widespread significance in this regard and has become a strong candidate for enhancing energy efficiency (EE) performance of existing networks. Multicarrier NOMA (MC‐NOMA) technique is an extension of NOMA that breaks the available resource block into various subchannels and allocates them efficiently to NOMA users (NUs). MC‐NOMA networks have the ability to further enhance system performance by efficiently exploiting channel diversity for addressing the problems of spectral inefficiency and power limitations. Therefore, in this paper, we have considered MC‐NOMA technology for an uplink scenario to investigate its performance for enhancing system EE by performing energy‐efficient power and subchannel allocation. To this end, we have formulated a joint user clustering, subchannel allocation, and power allocation problem for EE maximization (JSPEE) of uplink MC‐NOMA scenario. Due to the nonconvex, combinatorial nature of the problem, we propose a two‐step solution: that is, for each subchannel, first, subchannel allocation and user clustering are attained through low‐complexity suboptimal algorithm, which is followed by energy‐efficient power allocation of NUs. For subchannel allocation and user clustering, we exploit the channel diversity in the form of difference in channel gain values of various users. For power allocation, the nonconvex nature of the formulated problem is tackled by employing Dinkelbach and successive convex approximation (SCA) techniques to attain an energy‐efficient solution. Our simulation results portray that the JSPEE algorithm clearly outperforms the current NOMA as well as OMA works and enhances the system EE by efficient user clustering and exploiting channel diversity.

Joint uplink and downlink power allocation for maximizing the energy efficiency in ultra-dense networks

Ultra-dense network (UDN) is a promising solution to meet the huge data requirements of future 5G wireless communication systems. With many irregularly deployed small cell base stations (SBSs), it is very difficult to accurately measure network energy efficiency (EE) in UDN. Considering the necessity of saving energy in modern-day networks, in this paper, I present the EE evaluation of UDN based on the random spatial network model. In this considered system, the SBSs and the macro cell users (MUEs) are modeled as a homogeneous Poisson point process (PPP). Unlike the existing literature, I derive the simple closed-form expressions for the successful transmission probabilities of MUEs and small cells by Laplace transformation. Then, based on the obtained successful transmission probabilities, I derive the closed form expression for EE of the small cells in both uplink and downlink under some practical constraints. Further, to obtain the optimal SBS density in UDN, I propose a joint uplink and downlink power allocation (JUDPA) algorithm. The JUDPA algorithm obtains the maximum EE in small cells by iteratively optimizing the uplink and downlink powers. Our theoretical analysis and simulated results demonstrate the accuracy of the proposed model with better power allocation and less complexity than conventional solutions.