Uplink Power Allocation Research Articles

Min-SINR Maximization With DL SWIPT and UL WPCN in Multi-Antenna Interference Networks

A novel multicell communication and energy harvesting scheme is proposed, in which downlink (DL) simultaneous wireless information and power transfer and uplink (UL) wireless powered communication network concepts are jointly considered. Specifically, a DL beamforming with energy harvesting and UL power allocation (PA) scheme is proposed, where each cell is composed of a base station with multiple antennas and power-limited users each with single antenna. The joint optimization of DL beamforming vector design and UL PA is known to be challenging due to unpredictable future channel conditions, and thus, we propose an efficient cascaded design for the DL and UL problems in pursuit of maximizing the minimum DL and UL signal-to-interference-plus-noise ratios (SINRs). Simulation results show that the proposed scheme achieves higher minimum DL and UL SINRs region as well as higher UL energy efficiency than the existing schemes.

Uplink resource allocation in multiuser multicarrier cognitive radio networks under power amplifier nonlinearity

AbstractIn this paper, the nonlinear distortion effects of power amplifiers are studied, in particular from uplink subcarrier and power allocation perspectives in multiuser multicarrier cognitive networks. The out‐of‐band emissions of a nonlinear power amplifier create interference to the other users. The target is then to maximize the achievable uplink rate in a multiuser multicarrier cognitive network where a cognitive user should not introduce interference to the other users more than a specified threshold level, called interference power constraint. This task is formulated as a convex optimization problem with interference power constraints. Obtaining a closed‐form solution for this problem is, however, not feasible due to its nonlinear nature. Accordingly, the problem is solved numerically, and extensive simulations are conducted to obtain performance results. The obtained results are also compared with a more simple heuristic approach. The results show that the proposed scheme provides a maximum rate while at the same time also guaranteeing that the interference levels are lower than the specified interference power constraint. The results also indicate that the maximum rate can be closely approximated by using the assisted or optimization‐directed heuristic approach.

Uplink optimal power allocation for heterogeneous multiuser SIMO SC‐FDMA networks

Power allocation techniques for heterogeneous orthogonal frequency division multiple access networks have been well studied. However, due to the different structure of single carrier frequency division multiple access (SC-FDMA), these techniques are not applicable to networks employing SC-FDMA. Optimal uplink power allocation in multiuser heterogeneous single-input–multiple-output (SIMO) SC-FDMA cellular networks is considered. Users’ sum-rate maximisation problem with per-user transmit power constraint is formulated which is a non-linear non-convex problem. This problem is converted to convex optimisation problem and optimal power allocation scheme is derived. Simulations results demonstrate the performance gain of the optimal scheme over the equal power allocation scheme.

Resource Allocation in Two-Tier Wireless Backhaul Heterogeneous Networks

This paper studies two-tier heterogeneous cellular networks, with wireless backhaul communication, consisting of macrocell and small cell tiers. A joint design of transmit beamforming, power allocation, and bandwidth partitioning for both uplink and downlink transmissions is considered. By assuming the reverse time division duplexing system, we propose a strategy to partition the bandwidth for two consecutive time slots by two separate partitioning factors. Under the proposed strategy, we formulate a constrained optimization problem with the objective of maximizing the sum rate of small cell uplink and downlink. For this non-convex problem, we leverage the sequential parametric convex approximation method to find the stationary point of the problem. In this method, a convex approximation of the problem is solved at each iteration. Furthermore, with appropriate transformations, we approximate the problem as second-order cone programming (SOCP) and propose a fast converging algorithm to attain the solution. We also evaluate the impact of imperfect channel state information by reformulating the optimization problem and applying the proposed algorithm to solve it. We conduct numerical simulations to show that the joint design of transmit beamforming, power allocation, and bandwidth partitioning leads to a better resource utilization and high spectral efficiency. Moreover, our results show that the proposed SOCP-based algorithm converges fast to a solution, which is shown to be closer to the global optimal solution achieved by the branch-and-bound algorithm compared with other works.

Uplink Power Allocation for Distributed Interference Cancellation With Channel Estimation Error

A distributed interference cancellation scheme for cellular networks is considered, in which a subset of receivers forward their decoded messages to another subset of receivers. The messages can then be used to cancel interference with estimated cross-channel gains. A distributed power control algorithm is presented that harnesses the gain of interference cancellation and that considers channel estimation error. The algorithm is based on the exchange of interference prices, but in this case, the local power updates are not concave due to nonlinear terms introduced by channel estimation errors. A partial cancellation scheme is then presented, where the power updates are derived in closed form. We show that the algorithm converges to a local optimum of the weighted sum-rate maximization problem. Two methods for sorting users into the two groups that, respectively, forward and receive the decoded messages are presented. The first is sequential and is shown to converge to a local optimum. The second is a simple heuristic that is based on a stochastic geometric approach. Numerical results show that power control and adaptive partitioning of the users can add substantial gains to interference cancellation.

Uplink Achievable Rate and Power Allocation in Cooperative LTE-Advanced Networks

This paper studies the achievable rate and power allocation to improve the uplink (UL) spectrum efficiency in a Long-Term Evolution Advanced (LTE-A) cooperative cellular network with the deployment of Type-II in-band decode-and-forward (DF) relay stations (RSs). The physical-layer UL transmission technology is based on single-carrier frequency-division multiple access (SC-FDMA) with frequency-domain equalization (FDE). Different from the downlink (DL) orthogonal FDMA system, signals on all subcarriers in the SC-FDMA system are transmitted sequentially rather than in parallel; thus, the user's achievable rate is not simply the summation of the rates on all allocated subcarriers. Moreover, each user equipment (UE) device has its own transmission power constraint instead of a total power constraint at the base station in the DL case. Therefore, the UL resource allocation problem in the LTE-A system is more challenging. To this end, we first derive the achievable rates of the SC-FDMA system with two commonly used FDE techniques, namely, zero-forcing (ZF) equalization and minimum-mean-square-error (MMSE) equalization, based on the joint superposition coding for cooperative relaying. We then propose optimal power allocation schemes among subcarriers at both the UE and RS to maximize the overall throughput of the system. Both theoretical analysis and numerical results demonstrate that our proposed power allocation schemes can drastically improve system throughput.

Power allocation for uplink SC‐FDMA systems with arbitrary input distribution

Existing power allocation schemes for single-carrier frequency division multiple access (SC-FDMA) systems are based on the assumption of Gaussian inputs. However, in practical systems, the inputs belong to a set of finite symbol alphabets. In this reported work the power allocation problem in the uplink SC-FDMA system with arbitrarily distributed finite power inputs is considered. An optimal solution to the problem is found by using the relationship between minimum mean-square error and mutual information. Simulation results show that solution allocates power in accordance to the modulation scheme employed and achieves significant power saving compared to schemes that assume Gaussian inputs.

Dynamic User Clustering and Power Allocation for Uplink and Downlink Non-Orthogonal Multiple Access (NOMA) Systems

Non-orthogonal multiple access (NOMA) has recently been considered as a key enabling technique for 5G cellular systems. In NOMA, by exploiting the channel gain differences, multiple users are multiplexed into transmission power domain and then non-orthogonally scheduled for transmission on the same spectrum resources. Successive interference cancellation (SIC) is then applied at the receivers to decode the message signals. In this paper, first, we briefly describe the differences in the working principles of uplink and downlink NOMA transmissions in a cellular wireless system. Then, for both uplink and downlink NOMAs, we formulate a sum-throughput maximization problem in a cell such that the user clustering (i.e., grouping users into a single cluster or multiple clusters) and power allocations in NOMA clusters can be optimized under transmission power constraints, minimum rate requirements of the users, and SIC constraints. Due to the combinatorial nature of the formulated mixed integer non-linear programming problem, we solve the problem in two steps, i.e., by first grouping users into clusters and then optimizing their respective power allocations. In particular, we propose a low-complexity sub-optimal user grouping scheme. The proposed scheme exploits the channel gain differences among users in an NOMA cluster and groups them into a single cluster or multiple clusters in order to enhance the sum-throughput of the system. For a given set of NOMA clusters, we then derive the optimal power allocation policy that maximizes the sum-throughput per NOMA cluster and in turn maximizes the overall system throughput. Using Karush–Kuhn–Tucker optimality conditions, closed-form solutions for optimal power allocations are derived for any cluster size, considering both uplink and downlink NOMA systems. Numerical results compare the performances of NOMA and OMA and illustrate the significance of NOMA in various network scenarios.

Combined power and rate allocation in self-optimized multi-service two-tier femtocell networks

In this paper a novel utility-based game theoretic framework is proposed towards simultaneously allocating users’ uplink transmission power and rate in multi-service two-tier open-access femtocell networks. Each user is associated with an appropriately designed utility function, depending on both transmission powers and rates, while accounting for the tier each user belongs to (i.e. Macro User (MU) and Femto User (FU)), and the type of service he requests (i.e. Real Time (RT) or Non-Real Time (NRT)). Towards mitigating the intercell and cross-tier interference caused by FUs and giving higher priority to the MUs, a convex pricing mechanism with respect to FUs’ bit energy to interference density ratio is also incorporated. The overall resource allocation problem is formulated as a Utility-based uplink Power and Rate Allocation game in Multi-Service two-tier Femtocell networks (UPRA-MSF), where each user aims selfishly at maximizing his utility-based performance. The existence and uniqueness of UPRA-MSF game's Nash equilibrium with respect to both uplink transmission power and rate is analytically shown. A distributed algorithm is proposed towards obtaining the corresponding equilibrium point and its convergence is proven. Finally, the operational effectiveness and superiority of the proposed framework is evaluated through modeling and comparative simulation.

Resource Allocation for Cognitive Small Cell Networks: A Cooperative Bargaining Game Theoretic Approach

Cognitive small cell networks have been envisioned as a promising technique for meeting the exponentially increasing mobile traffic demand. Recently, many technological issues pertaining to cognitive small cell networks have been studied, including resource allocation and interference mitigation, but most studies assume non-cooperative schemes or perfect channel state information (CSI). Different from the existing works, we investigate the joint uplink subchannel and power allocation problem in cognitive small cells using cooperative Nash bargaining game theory, where the cross-tier interference mitigation, minimum outage probability requirement, imperfect CSI and fairness in terms of minimum rate requirement are considered. A unified analytical framework is proposed for the optimization problem, where the near optimal cooperative bargaining resource allocation strategy is derived based on Lagrangian dual decomposition by introducing time-sharing variables and recalling the Lambert-W function. The existence, uniqueness, and fairness of the solution to this game model are proved. A cooperative Nash bargaining resource allocation algorithm is developed, and is shown to converge to a Pareto-optimal equilibrium for the cooperative game. Simulation results are provided to verify the effectiveness of the proposed cooperative game algorithm for efficient and fair resource allocation in cognitive small cell networks.

Uplink Decentralized Joint Bandwidth and Power Allocation for Energy-Efficient Operation in a Heterogeneous Wireless Medium

In this paper, energy efficient uplink communications are investigated for battery-constrained mobile terminals (MTs) with service quality requirements and multi-homing capabilities. A heterogeneous wireless medium is considered, where MTs communicate with base stations (BSs) and access points (APs) of different networks with overlapped coverage. Different from the existing works, we develop a quality of service (QoS)-based optimization framework for joint uplink bandwidth and power allocation to maximize energy efficiency for a set of MTs with multi-homing capabilities. The proposed framework is implemented in a decentralized architecture, through coordination among BSs/APs of different networks and MTs, which is a desirable feature when different networks are operated by different service providers. A suboptimal framework is presented with a reduced computational complexity as compared with the optimal framework. Simulation results demonstrate the improved performance of both the optimal and suboptimal frameworks over a state-of-the-art benchmark.

Design a Cross-layer Uplink Green Resource and Power Allocation Method over LTE-A Relay Network

The Relay Node (RN) in Long-Term Evolution-Advanced (LTE-A) networks is used to enhance the coverage of high data rate and solve the coverage hole problem. However, for User Equipments (UEs), using a higher transmission rate means more energy consumption; especially they are operated by batteries. In this paper, we consider an uplink resource and power allocation problem for energy conservation in LTE-A relay networks. The objective is to minimize the total energy consumption of UEs while guarantee their quality of services (QoSs). We prove that this uplink resource and power allocation problem to be NP-complete and we develop a method to solve the problem. Simulation results show that our algorithm can effectively reduce the energy consumption and guarant ee users' service quality.

Multi-Antenna Wireless Powered Communication With Energy Beamforming

The newly emerging wireless powered communication networks (WPCNs) have recently drawn significant attention, where radio signals are used to power wireless terminals for information transmission. In this paper, we study a WPCN where one multi-antenna access point (AP) coordinates energy transfer and information transfer to/from a set of single-antenna users. A harvest-then-transmit protocol is assumed where the AP first broadcasts wireless power to all users via energy beamforming in the downlink (DL), and then the users send their independent information to the AP simultaneously in the uplink (UL) using their harvested energy. To optimize the users' throughput and yet guarantee their rate fairness, we maximize the minimum throughput among all users by a joint design of the DL-UL time allocation, the DL energy beamforming, and the UL transmit power allocation plus receive beamforming. We solve this non-convex problem optimally by two steps. First, we fix the DL-UL time allocation and obtain the optimal DL energy beamforming, UL power allocation and receive beamforming to maximize the minimum signal-to-interference-plus-noise ratio (SINR) of all users. This problem is shown to be in general non-convex; however, we convert it equivalently to a spectral radius minimization problem, which can be solved efficiently by applying the alternating optimization based on the non-negative matrix theory. Then, the optimal time allocation is found by a one-dimension search to maximize the minimum rate of all users. Furthermore, two suboptimal designs of lower complexity are proposed, and their throughput performance is compared against that of the optimal solution.

Hierarchical-Game-Based Uplink Power Control in Femtocell Networks

Femtocells are viewed as a promising option for mobile operators to improve coverage and provide high-data-rate services in a cost-effective manner. However, cross-tier interference mitigation is considered to be one of the major challenges. Distributed-game-based power control approaches are effective for interference management in wireless networks. In this paper, we focus on the optimal power allocation for uplink transmission in two-tier femtocell networks and take into account the different service requirements and design objectives of macrocell user equipment (MUE) and femtocell user equipment (FUE) devices. The framework of hierarchical game with a multiple-leader-multiple-follower model is adopted to investigate the uplink power allocation problem. By using hierarchical game, on one hand, the utilities of both MUE and FUE devices are maximized; on the other hand, the uplink protection of MUE devices is highlighted. To obtain the game equilibrium (GE) distributively, we develop the iterative power update rules for both MUE and FUE devices. Moreover, given channel uncertainty, a robust hierarchical game is formulated and solved distributively. For implementations, the running process of the proposed hierarchical games is analyzed in detail. Finally, numerical results show the convergence of the hierarchical games without and with channel uncertainty. Moreover, it is also demonstrated that, in either case, the GE is unique and that the game is effective.

Distributed Inter-Cell Interference Mitigation Via Joint Scheduling and Power Control Under Noise Rise Constraints

Consider the problem of joint uplink scheduling and power allocation. Being inherent in almost any wireless system, this resource allocation problem has received extensive attention. Yet, most common techniques either adopt classical power control, in which mobile stations are received with the same Signal-to-Interference-plus-Noise Ratio, or use centralized schemes, in which base stations coordinate their allocations. In this work, we suggest a novel scheduling approach in which each base station, besides allocating the time and frequency according to given constraints, also manages its uplink power budget such that the aggregate interference, "Noise Rise", caused by its subscribers at the neighboring cells is bounded. Our suggested scheme is distributed, requiring neither coordination nor message exchange between the base stations. We rigorously define the allocation problem under noise rise constraints. Inspired by the fact that under the noise-rise constraints, interference experienced by base stations is bounded and its variance is expected to be low, we suggested an approximation in which each base station assumes fixed interference. Under this approximation we formalize the joint scheduling and power control under the noise rise constraints as an optimization problem and characterize the optimal solution. For the special case of homogeneous deployment we give the optimal solution and derive an efficient iterative algorithm to achieve it. We then discuss a relaxed problem, where the noise rise is constrained separately for each sub-channel or resource unit. While sub-optimal, this view renders the scheduling and power allocation problems separate, yielding an even simpler and more efficient solution, while the essence of the scheme is kept. Via extensive simulations, we show that the suggested approach increases overall performance dramatically, with the same level of fairness and power consumption.

Resource Allocation for M2M Communication in Heterogeneous Network: Coalitional Game Theory Approach

The heterogeneous cellular network (HCN) is a promising technology to handle the rising number of devices due to their universal presence. This rising popularity HCN based Machine-to-Machine (M2M) communications is opening new opportunities and also bringing forth new system design issues. However, the main challenge of M2M communication is the possibility of huge traffic and significant difference in the nature of M2M traffic than the current commercial traffic for which current cellular network is designed and optimized. In this article, we investigate the uplink resource allocation problem of M2M devices (MDs) in the multiple Femto base station's coverage. We first model the uplink power and sub-carrier allocation in femtocells independently; Based on the cooperative game resource allocation among MDs is analyzed through non-transferable utility game to enhance the data rate performance with minimum utilization of power. Simulation results show that the resource allocation model based on cooperative game is able to provide a fair distribution of data rate compared with non-cooperative and greedy type of MDs.

Robust Distributed Power Control in Cognitive Radio Networks

We propose a robust distributed uplink power allocation algorithm for underlay cognitive radio networks (CRNs) with a view to maximizing the social utility of secondary users (SUs) when channel gains from SUs to primary base stations, and interference caused by primary users (PUs) to the SUs' base station are uncertain. In doing so, we utilize the worst case robust optimization to keep the interference caused by SUs to each primary base station below a given threshold, and satisfy each SU's quality of service in terms of its required SINR for all realizations of uncertain parameters. We model each uncertain parameter by a bounded distance between its estimated and exact values, and formulate the robust power allocation problem via protection values for constraints. We demonstrate that the convexity of our problem is preserved, and converts into a geometric programming problem, which we solve via a distributed algorithm by using Lagrange dual decomposition. To reduce the cost of robustness, defined as the reduction in the social utility of SUs and the increase in message passing, we utilize the D-norm approach to trade off between robustness and optimality, and propose a distributed power allocation algorithm with infrequent message passing. Simulation results validate the effectiveness of our proposed approach.

Resource allocation with interference mitigation in OFDMA femtocells for co-channel deploymenta

Femtocells have been considered as a promising technology to provide better indoor coverage and spatial reuse gains. However, the co-channel deployment of macrocells and femtocells is still facing challenges arising from potentially severe inter-cell interference. In this article, we investigate the uplink resource allocation problem of femtocells in co-channel deployment with macrocells. We first model the uplink power and subchannel allocation in femtocells as a non-cooperative game, where inter-cell interference is taken into account in maximizing the femtocell capacity and uplink femto-to-macro interference is alleviated by charging each femto user a price proportional to the interference that it causes to the macrocell. Based on the non-cooperative game, we then devise a semi-distributed algorithm for each femtocell to first assign subchannels to femto users and then allocate power to subchannels. Simulation results show that the proposed interference-aware femtocell uplink resource allocation algorithm is able to provide improved capacities for not only femtocells, but also the macrocell, as well as comparable or even better tiered fairness in the two-tier network, as compared with existing unpriced subchannel assignment algorithm and modified iterative water filling-based power allocation algorithm.

Coverage and capacity optimization in LTE network based on non-cooperative games

As to provide the optimal coverage and capacity performance, support high-data-rate service and decrease the capital expenditures and operational expenditures (OPEX) (CAPEX) for operator, the coverage and capacity optimization (CCO) is one of the key use cases in long term evolution (LTE) self-organization network (SON). In LTE system, some factors (e.g. load, traffic type, user distribution, uplink power setting, inter-cell interference, etc.) limit the coverage and capacity performance. From the view of single cell, it always pursuits maximize performance of coverage and capacity by optimizing the uplink power setting and intra-cell resource allocation, but it may result in decreasing the performance of its neighbor cells. Therefore, the benefit of every cell conflicts each other. In order to tradeoff the benefit of every cell and maximize the performance of the whole network, this paper proposes a multi-cell uplink power allocation scheme based on non-cooperative games. The scheme aims to make the performance of coverage and capacity balanced by the negotiation of the uplink power parameters among multi-cells. So the performance of every cell can reach the Nash equilibrium, making it feasible to reduce the inter-cell interference by setting an appropriate uplink power parameter. Finally, the simulation result shows the proposed algorithm can effectively enhance the performance of coverage and capacity in LTE network.

Joint utility-based uplink power and rate allocation in wireless networks: A non-cooperative game theoretic framework

In this paper a novel utility-based game theoretic framework is proposed to address the problem of joint transmission power and rate allocation in the uplink of a cellular wireless network. Initially, each user is associated with a generic utility function, capable of properly expressing and representing mobile user’s degree of satisfaction, in relation to the allocated system’s resources for heterogeneous services with various transmission rates. Then, a Joint Utility-based uplink Power and Rate Allocation (JUPRA) game is formulated, where each user aims selfishly at maximizing his utility-based performance under the imposed physical limitations, and its unique Nash equilibrium is determined with respect to both variables, i.e. uplink transmission power and rate. The JUPRA game’s convergence to its unique Nash equilibrium is proven and a distributed, iterative and low complexity algorithm for computing JUPRA game’s equilibrium is introduced. The performance of the proposed approach is evaluated in detail and its superiority compared to various state of the art approaches is illustrated, while the contribution of each component of the proposed framework in its performance is quantified and analyzed.