Power Allocation Solution Research Articles

A novel user clustering and a low-complexity power allocation in multi-user and multi-cluster NOMA system via Stackelberg game competition

Non-orthogonal multiple access (NOMA) has drawn wide attentions in multi-user, resource-constrained mobile systems to improve the system spectral efficiency. In the conventional resource assignment approaches, the optimal power allocation solutions are usually performed among multiple clusters, which, however, incur a high computational complexity due to the diversity of multiple users. In this work, we aim at the practical scenarios of multi-user and multi-cluster NOMA networks with the proposed a low-complexity power allocation and a suboptimal user clustering approach. By using Stackelberg game competition, a closed-form solution with no iterative operations is given first to the power allocation problem. The main idea of the authors’ proposed scheme is to have the base station (BS) allocating resources to all the users freely to guarantee their required minimum transmission-rates, and obtaining the maximal revenue through selling the remaining resources. Then, a suboptimal user clustering approach is further proposed to obtain more revenue for selling the resource. Numerical results show that the proposed power allocation scheme approximates the optimal sum-rates performance, but with lower computational floating-point operations (FLOPs) for the multi-user scene. And, the given user clustering scheme can pursue more revenue of the BS.

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.

Joint Design of Channel Training and Data Transmission for MISO-URLLC Systems

For the ultra-reliable low-latency communication (URLLC), the existing joint design algorithms of channel training and data transmission are not applicable due to the stringent reliability requirement and limited blocklength. To address this issue, we develop a low-complexity joint design framework for MISO communication based on the finite blocklength code (FBC). Specifically, an approximate bound of the packet error probability (PEP) is first derived and validated by practical modulation and coding schemes. It reveals the inherent tension between reliability, latency, and information bit number. Then, we formulate the joint design into a nonconvex optimization problem with the objective to maximize the information bit number. By exploiting the monotonicity of the PEP approximate bound, we provide closed-form solutions of power and blocklength allocation. Thereby, we develop a low-complexity algorithm to support the URLLC services aiming at the information bit number maximization. Furthermore, we investigate the joint designs to optimize the reliability, latency, and total energy, respectively, to fully meet the diverse demands of URLLC services. Finally, numerical results are provided to validate the proposed joint designs. The results show the outage capacity-based design severely underestimates the required wireless resources.

Energy Efficiency Maximization for UAV-Assisted Full-Duplex NOMA System: User Clustering and Resource Allocation

In this paper, the problem of energy efficiency maximization of an unmanned-aerial-vehicle (UAV)-assisted full-duplex non-orthogonal-multiple-access (FD-NOMA) system is investigated. In order to reduce the interference among users in the considered system, we first propose a novel and less complex adaptive-geometric distribution (AGD) which adaptively maintain equal cluster size and allocate resource optimally among the users. Later, we formulate an optimization problem of energy efficiency maximization subject to the transmit power budget at the UAV for downlink (DL) users and for each uplink (UL) user and UAV placement. To transform the non-convex problem into convex one, we decouple the original optimization problem into different sub-problems in which the problems of power allocation and UAV placement are alternatively solved using Dinkelbach’s algorithm with successive convex approximation (SCA) and particle swarm optimization (PSO), respectively. Later, the unified solution for optimal power allocation and UAV placement is proposed based on block-coordinate descent (BCD) algorithm. Extensive simulation results validate the superiority of the proposed AGD clustering based proposed algorithm for UAV-assisted FD-NOMA system over the conventional clustering and other orthogonal schemes.

Resource Allocation for URLLC Service in In-Band Full-Duplex-Based V2I Networks

This paper investigates the first resource management problem for vehicular-to-infrastructure (V2I) networks under the in-band full-duplex (IBFD) backhauling scheme with guaranteed ultra-reliable and low-latency (URLLC) service. The considered networks suffers from interference caused by three node transmission in IBFD scheme and the mobility of vehicular user equipments (VUEs). The resource allocation problem is formulated to jointly optimize VUE association, resource block assignment (RA) and power allocation (PA), while satisfying the reliability and latency constraints at the same time. The formulated problem is a mixed integer non-linear problem with non-convex objective function and constraints. Finding globally optimal solution for this type of problem is still an open problem. To develop tractable solutions, the original problem is firstly simplified using derived equivalent expression for the objective function. The proposed method then decomposed it into RA and PA sub-problems. In each iteration, the PA sub-problem is solved for a set of given PA solution; while the solution for PA sub-problem is searched under determined RA results. The RA and PA sub-problems are solved iteratively until the converging condition is achieved. Theoretical analysis proves that the proposed method achieves Nash-stable equilibrium and local optimality for RA and PA sub-problems respectively. Simulation results verify the effectiveness of the derived performance analysis and demonstrate that the proposed algorithm outperforms the state-of-the-art algorithms in the literatures.

Sparse Joint Transmission for Cloud Radio Access Networks With Limited Fronthaul Capacity

A cloud radio access network (C-RAN) is a promising cellular network, wherein densely deployed multi-antenna remote-radio-heads (RRHs) jointly serve many users using the same time-frequency resource. By extremely high signaling over-heads for both channel state information (CSI) acquisition and data sharing at a baseband unit (BBU), finding a joint transmission strategy with a significantly reduced signaling overhead is indispensable to achieve the cooperation gain in practical C-RANs. In this paper, we present a novel sparse joint transmission (sparse-JT) method for C-RANs, where the number of transmit antennas per unit area is much larger than the active downlink user density. Considering the effects of noisy-and-incomplete CSI and the quantization errors in data sharing by a finite-rate fronthaul capacity, the key innovation of sparse-JT is to find a joint solution for cooperative RRH clusters, beamforming vectors, and power allocation to maximize a lower bound of the sum-spectral efficiency under the sparsity constraint of active RRHs. To find such a solution, we present a computationally efficient algorithm that guarantees to find a local-optimal solution for a relaxed sum-spectral efficiency maximization problem. By system-level simulations, we exhibit that sparse-JT provides significant gains in ergodic spectral efficiencies compared to existing joint transmissions.

Resource allocation for cellular device‐to‐device‐aided vehicle‐to‐everything networks with partial channel state information

AbstractIn Vehicle‐to‐Everything (V2X), cellular Device‐to‐Device (D2D) communication can not only enhance system capacity and spectral utilization but also reduce the traffic load and communication delay. However, due to the fast variations of the channel state information (CSI) caused by high mobility of vehicles, the accurate CSI is difficult to be obtained in real‐time. In this paper, different from other previous researches that are subject to the perfect CSI, a more realistic channel state model is adopted that only the partial CSI of the communication links is available. Then we study the joint power allocation and spectrum sharing problem to maximize the total weighted ergodic capacity of all vehicular links, namely, Vehicle‐to‐Infrastructure (V2I) links and Vehicle‐to‐Vehicle (V2V) links. Meanwhile, the transmission reliability guarantees the quality of service (QoS) requirements of V2I and V2V. Since the formulated problem is a nonconvex mixed integer nonlinear programming (MINLP) problem, we first derive the strategy for obtaining the optimal solution of power allocation under arbitrary weighted factor and quantify different cases of feasible regions. Then we propose a two‐stage algorithm to complete the optimal resource allocation in polynomial time. Moreover, we further propose a low‐complexity spectrum sharing method based on the principles of Gale‐Shapley mechanism. The simulation results show that the proposed algorithms outperform the other baseline schemes significantly, and the low‐complexity scheme can effectively achieve a tradeoff between performance and complexity.

Effective Throughput Maximization of NOMA With Practical Modulations

Non-orthogonal multiple access (NOMA) has been considered as a promising technology for future wireless communications. In most of the existing NOMA schemes, the ideal information rate based on Shannon capacity is used as the performance metric, assuming perfect successive interference cancellation (SIC) and Gaussian transmit signals without considering practical modulations. The implicit assumptions and the resulting schemes may lead to suboptimal performance in practical NOMA systems. In this paper, we consider multi-user multi-channel NOMA systems using practical quadrature amplitude modulation (QAM) with imperfect SIC. We aim to maximize a more practical performance metric, namely the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">effective throughput</i> , which takes into account the data rate and error performance. To achieve this goal, we derive both the exact and approximate expressions of the effective throughput. We also formulate a joint resource optimization problem of the power allocation, channel assignment, and modulation selection to maximize the effective throughput. We develop an efficient power allocation solution by proposing a closed-form power allocation within channels and a waterfilling-form power budget allocation among channels. We also develop efficient channel assignment and modulation selection methods with the aid of matching theory and machine learning, respectively. Consequently, we provide an efficient joint resource allocation algorithm via iterative optimization to maximize the effective throughput. Numerical results are presented to verify the superiority of the proposed NOMA scheme over orthogonal multiple access (OMA) and other NOMA schemes.

Non-Orthogonal Multiple Access: The Case of Improper Gaussian Signaling and Imperfect Successive Interference Cancellation

This paper studies a two-user downlink non-orthogonal multiple access (NOMA) system that adopts an improper Gaussian signaling (IGS) strategy to compensate for the performance loss due to imperfect successive interference cancellation (SIC). Joint optimization problems are formulated to maximize the overall spectral efficiency and energy efficiency of a two-user NOMA system under minimum user-rate requirements and total power constraints. Sub-optimal solutions of IGS circularity coefficients and power allocation are obtained for the formulated problems. Furthermore, improper constellation diagrams are designed using widely linear transformation with predefined optimized IGS coefficients to study the impact of IGS on throughput and error performance. Simulation results show that the performance of IGS-based NOMA systems, where the IGS strategy is adopted by both users, outperforms that of the proper Gaussian signal (PGS)-based NOMA system under imperfect SIC.

User scheduling and power allocation for downlink multi-cell multi-carrier NOMA systems

In Non-Orthogonal Multiple Access (NOMA), the best way to fully exploit the benefits of the system is the efficient resource allocation. For the NOMA power domain, the allocation of power and spectrum require solving the mixed-integer nonlinear programming NP-hard problem. In this paper, we investigate user scheduling and power allocation in Multi-Cell Multi-Carrier NOMA (MCMC-NOMA) networks. To achieve that, we consider Weighted Sum Rate Maximization (WSRM) and Weighted Sum Energy Efficiency Maximization (WSEEM) problems. First, we tackle the problem of user scheduling for fixed power using Fractional Programming (FP), the Lagrange dual method, and the decomposition method. Then, we consider Successive Pseudo-Convex Approximation (SPCA) to deal with the WSRM problem. Finally, for the WSEEM problem, SPCA is utilized to convert the problem into separable scalar problems, which can be parallelly solved. Thus, the Dinkelbach algorithm and constraints relaxation are used to characterize the closed-form solution for power allocation. Extensive simulations have been implemented to show the efficiency of the proposed framework and its superiority over other existing schemes.

Hierarchical Symbiotic Transmission Strategy With Cooperative-NOMA for Cognitive Radio Networks

We propose a hierarchical symbiotic transmission strategy with cooperative-nonorthogonal multiple access (C-NOMA) for multi-carrier cognitive radio networks, where the mutualistic and commensualistic transmission modes are employed in the primary network (PN) and secondary network (SN), by promoting some secondary users (SUs) as relays to assist communication of both PN and SN. The proposed strategy enables higher system throughput and supports much more user connectivity than the existing schemes with C-NOMA that focused on maintaining the performance of PN. Sub-carrier assignment and power allocation solutions are presented to further improve system throughput. Simulation results demonstrate the effectiveness of the proposed strategy.

Joint Power and Data Allocation in Multi-Carrier Full-Duplex Relaying Networks Operating With Finite Blocklength Codes

In this paper, we study a full-duplex (FD) relaying network operating with finite blocklength (FBL) codes. Based on Polyanskiy’s FBL model, we characterize the FBL reliability of the relaying network under both decode-and-forward (DF) and amplify-and-forward (AF) relaying schemes. Based on the characterisation, we provide reliability-optimal designs via optimal power allocation for both schemes in a single-carrier scenario. In particular, we prove that under the FD DF relaying scheme the (tightly approximated) overall error probability is convex in the transmit power at the relay. In addition, we show that minimizing the overall error probability of the FD AF relaying is equivalent to maximizing the overall signal to interference plus noise ratio (SINR), which is further proved to be pseudo-concave. Then, the designs for a single-carrier scenario are further extended to a multi-carrier scenario with a joint power and data allocation among carriers. In particular, for either the FD DF or FD AF relaying scheme, a joint optimization problem is reformulated to a single problem maximizing the reliability via finding and achieving the optimal SINRs, while auxiliary variables are introduced in FD AF relaying to facilitate the reformulation. Based on mathematical analysis, we respectively construct convex approximations and subsequently propose iterative algorithms, with which the error probability is reduced iteratively until an eventual convergence to an efficient suboptimal value. Hence, a corresponding suboptimal data and power allocation solution can be constructed for the multi-carrier scenario. Via numerical analysis, we validate our analytical model and the proposed allocation algorithms. The FD DF and FD AF relaying schemes are compared with direct transmission in both single-carrier and multi-carrier scenarios, and the benefits of applying FD relaying schemes and joint optimization among multiple carriers are observed.

Artificial-Noise-Aided Coordinated Secure Transmission Design in Multi-Cell Multi-Antenna Networks With Limited Feedback

In this paper, we consider secure communications in a multi-cell downlink multi-antenna system, where each multi-antenna base station (BS) sends confidential messages to one intended legitimate user (LU) with a passive multi-antenna eavesdropper trying to intercept the messages in this cell. To enhance the secure communications of the cell-edge users, the multiple BSs employ coordinated beamforming with the aid of artificial-noise (AN) beamforming. We conduct a mathematically rigorous secrecy performance analysis of the considered system and obtain new accurate closed-form expressions of a lower bound on the ergodic rate of each LU and an upper bound of the ergodic rate of each eavesdropper without assuming asymptotes for any system parameter (except for the “worst-case” scenario with zero noise power). Then, a lower bound on the ergodic secrecy rate (ESR) of each LU follows. Based on the derived analytical results, we propose a low-complexity algorithm to optimize the channel sate information (CSI) feedback bits allocation for the channels of target signal link and inter-cell interference links. Furthermore, we develop explicit sufficient conditions on the set of system parameters under which there are at least two BSs to coordinated secure transmission. Through both theoretical analysis and numerical results, we can verify that the obtained analytical lower bound on ESR of each LU as a function of the set of power allocation coefficients is generally <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">neither convex nor concave</i> . Thus, we propose to employ some numerical method to find a sub-optimal power allocation solution. Finally, numerical results are also provided to validate our theoretical results and the proposed feedback bits and power allocation methods.

Joint subcarrier and power allocation with proportional fairness in multicarrier nonorthogonal multiple access systems

SummaryIn this paper, we investigate joint subcarrier and power allocation in multicarrier nonorthogonal multiple access (MC‐NOMA) systems aims to guarantee rate proportional fairness among all users. To this end, we adopt the sum logarithmic rate function as the objective and formulate a resource optimization problem which is a mixed‐integer nonlinear programming (MINLP) problem. Based on variable relaxation and replacement, we convert this problem into a convex optimization problem. On this basis, the expressions of joint subcarrier and power allocation solutions are obtained by using convex optimization theory and the Karush–Kuhn–Tucker (KKT) condition. However, power and subcarrier allocation solutions are highly coupled, and it is difficult to derive both solutions simultaneously. Fortunately, when the subcarrier allocation strategy is given, the optimal power allocation solution can be derived by solving the equations. Furthermore, we develop an iterative resource allocation algorithm to implement joint subcarrier and power allocation among users. Numerical results reveal that the proposed algorithm is effective in general multiuser and multicarrier scenarios, and a substantial performance improvement can be achieved by the proposed algorithm in contrast with the traditional orthogonal multiple access (OMA) schemes. In specific, the proposed algorithm achieves 63% and 59% gains in terms of utility and fairness performance, respectively.

Socio-Technical Modeling to Manage Power Distribution for Microgrid Systems With Limited Production Capacity

Abstract The quality of life (QOL) in rural communities is improved through electrification. Microgrids can provide electricity in areas where grid access to electricity is infeasible. Still, insufficient power capacity hinders the very progress that microgrids promote. Therefore, we propose a decision-making framework to manage power distribution based on its impact on the rural QOL. Parameters are examined in this paper to represent the QOL pertaining to water, safety, education, and leisure/social activities. Each parameter is evaluated based on condition, community importance, and energy dependence. A solution for power allocation is developed by executing the compromise decision support problem (cDSP) and exploring the solution space. Energy loads, such as those required for powering water pumps, streetlamps, and household devices, are prioritized in the context of the QOL. The technique also allows decision-makers to update the power distribution scheme as the dynamics between energy production and demand change over time. In this paper, we propose a framework for connecting QOL and power management. The flexibility of the approach is demonstrated using a problem with varying scenarios that may be time dependent. The work enables sustainable energy solutions that can evolve with community development.

Rate-Splitting Multiple Access to Mitigate the Curse of Mobility in (Massive) MIMO Networks

Rate-Splitting Multiple Access (RSMA) is a robust multiple access scheme for downlink multi-antenna wireless networks. RSMA relies on multi-antenna Rate-Splitting (RS) at the transmitter and Successive Interference Cancellation (SIC) at the receivers. In this work, we study the performance of RSMA under the important setup of imperfect Channel State Information at the Transmitter (CSIT) originating from user mobility and latency/delay (between CSI acquisition and data transmission) in the network. We derive a lower bound on the ergodic sum-rate of RSMA for an arbitrary number of transmit antennas, number of users, user speed and transmit power. Then, we study the power allocation between common and private streams and obtain a closed-form solution for optimal power allocation that maximizes the obtained lower bound. The proposed power allocation greatly reduces precoder design complexity for RSMA. By Link-Level Simulations (LLS), we demonstrate that RSMA with the proposed power allocation is robust to the degrading effects of user mobility and has significantly higher performance compared to conventional multi-user (massive) Multiple-Input Multiple-Output (MIMO) strategies. The work has important practical significance as results demonstrate that, in contrast to conventional multi-user (massive) MIMO whose performance collapse under mobility, RSMA can maintain reliable multi-user connectivity in mobile deployments.

Distributed pricing‐based resource allocation for dense device‐to‐device communications in beyond 5G networks

AbstractConsidering the dramatic increase of data rate demand in beyond fifth generation (5G) networks due to the numerous transmitting nodes, dense device‐to‐device (D2D) communications where multiple D2D pairs can simultaneously reuse a cellular link can be considered as a communication paradigm for future wireless networks. Since distributed methods can be more practical compared to complex centralized schemes, we propose a low‐complexity distributed pricing‐based resource allocation algorithm to allocate power to cellular user equipments (CUEs) and D2D pairs constrained to the minimum quality‐of‐service (QoS) requirements of CUEs and D2D pairs in two phases. The price of each link is set by the base station (BS). CUEs and D2D pairs maximize their rate‐and‐interference‐dependent utility function by adjusting their power in the first phase, while the price of each link is updated at the BS based on the minimum QoS requirements of CUEs and D2D pairs in the second phase. The proposed utility function controls D2D admission, hence it is suitable for dense scenarios. The proposed method is fast due to the closed‐form solution for the first phase power allocation. Numerical results verify the effectiveness of the proposed method for practical dense beyond 5G scenarios by allocating resources to multiple D2D pairs and taking advantage of the spatial reuse gain of D2D pairs. Furthermore, the utility function definition is discussed to illustrate its effectiveness for SE maximization. Finally, the SE of the proposed method is compared to other centralized and distributed algorithms to demonstrate the higher sum‐rate performance of the proposed algorithm.

Downlink Power Allocation Optimization in Pattern Division Multiple Access

Non-orthogonal multiple access (NOMA), in particular pattern division multiple access (PDMA), has been recently considered as the critical multiple access technologies deployed for 5G applications. However, optimizing power allocation (PA) at the base station (BS) to increase the sum throughput of the downlink (DL) PDMA system still remains a challenge. Although this problem has been explored to some extent by assuming that the BS can obtain perfect channel state information (CSI), this assumption is unfeasible in harsh environments. Therefore, to effectively improve the sum throughput of communications in the real world, we propose using PDMA with optimized PA. With perfect CSI, iterative power allocation (IPA) that can iteratively update the Lagrange multipliers is proposed. Using the Karush-Kuhn-Tucher (KKT) conditions, a PA solution with closed-form expressions is derived from increasing the sum throughput. With imperfect CSI, the outage probability is considered when maximizing the sum throughput. The probabilistic mixture problem can be converted into a non-probability problem, and thus, PA under imperfect CSI can be optimized with the proposed IPA algorithm. The simulation results demonstrate that the proposed scheme outperforms the existing schemes and can be effective in harsh environments.

Efficient Downlink Power Allocation Algorithms for Cell-Free Massive MIMO Systems

Cell-free Massive MIMO systems consist of a large number of geographically distributed access points (APs) that serve the users by coherent joint transmission. The spectral efficiency (SE) achieved by each user depends on the power allocation: which APs that transmit to which users and with what power. In this article, we revisit the max-min and sum-SE power allocation policies, which have previously been approached using high-complexity general-purpose solvers. We develop and compare several different high-performance low-complexity power allocation algorithms that are appropriate for use in large systems. We propose two new algorithms for sum-SE power optimization inspired by weighted minimum mean square error (WMMSE) minimization and fractional programming (FP). Further, one new FP-based algorithm is proposed for max-min fair power allocation. The alternating direction method of multipliers (ADMM) is used to solve specific convex subproblems in the proposed algorithms. Our ADMM reformulations lead to multiple small-sized subproblems with closed-form solutions. The proposed algorithms find global or local optimal power allocation solutions for large-scale systems but with reduced computational time compared to previous work.

Power Allocation for Multiple User-Type Massive MIMO Systems

In this paper, we propose a multiple user-type massive multiple-input-multiple-output (MIMO) system and present power allocation solutions for this proposed network. Unlike conventional massive MIMO systems in which all users are treated equally, the proposed system consists of two types of users: quality-of-service (QoS) users, who are guaranteed to have good and stable quality, and non-QoS users, who are similar to users in conventional massive MIMO systems. We formulate two power allocation optimization problems which guarantee quality-of-service for QoS users and maximize the sum-rate of non-QoS users simultaneously. These two problems are solved by two proposed successive convex approximation algorithms, and we show that these two algorithms surely converge to a local maximum Karush-Kuhn-Tucker point. The numerical results show the effectiveness of our proposed power allocation algorithms in multiple user-type massive MIMO systems.