Optimal simultaneous wireless information and energy transfer in OFDMA decode-and-forward relay networks

In this paper, we develop an energy-efficient resource-allocation scheme with proportional fairness for downlink multiuser orthogonal frequency-division multiplexing (OFDM) systems with distributed antennas. Our aim is to maximize energy efficiency (EE) under the constraints of the overall transmit power of each remote access unit (RAU), proportional fairness data rates, and bit error rates (BERs). Because of the nonconvex nature of the optimization problem, obtaining the optimal solution is extremely computationally complex. Therefore, we develop a low-complexity suboptimal algorithm, which separates subcarrier allocation and power allocation. For the low-complexity algorithm, we first allocate subcarriers by assuming equal power distribution. Then, by exploiting the properties of fractional programming, we transform the nonconvex optimization problem in fractional form into an equivalent optimization problem in subtractive form, which includes a tractable solution. Next, an optimal energy-efficient power-allocation algorithm is developed to maximize EE while maintaining proportional fairness. Through computer simulation, we demonstrate the effectiveness of the proposed low-complexity algorithm and illustrate the fundamental trade off between energy and spectral-efficient transmission designs.

In this paper, a new mathematical approach for the analysis and optimization of cellular-enabled low-energy mobile devices with simultaneous wireless information and power transfer capabilities is introduced. The proposed methodology relies on modeling the locations of the base stations as points of a spatial Poisson point process, and it leverages stochastic geometry for system-level analysis. The tradeoffs emerging from simultaneous wireless information and power transfer transmission are characterized through the concept of “feasibility regions” and are quantified through the joint cumulative distribution function of harvested power and rate. To gain insight on the achievable performance, in addition, an upper bound is proposed, and its accuracy is discussed. The system model encompasses a realistic channel model that accounts for line-of-sight (LOS) and non-LOS (NLOS) links, different cell association criteria, practical receivers based on time switching and power splitting schemes, and directional beamforming. The analysis shows that optimal values for the time switching and power splitting ratios exist and that directional beamforming and network densification are capable of enhancing system performance. More specifically, high directional antennas lead cellular networks to operate in the noise-limited regime, which is proved to provide optimal performance, and because of the existence of LOS and NLOS links, an optimal deployment density for the base stations is proven to exist for typical system setups.

This paper considers simultaneous wireless information and power transfer (SWIPT) mechanisms in a relaying network with finite blocklength (FBL) codes. The reliability of the network is optimized under both a power splitting (PS) protocol and a proposed joint PS and time switching (TS) protocol. Under both protocols, we first determine the overall error probability formulation of the SWIPT-enabled two-hop transmission in the FBL regime. Subsequently, we state and solve optimization problems minimizing the overall error probability. Via numerical analysis, we show the appropriateness of our analytical model and demonstrate the performance advantage of the proposed protocol in comparison to TS and PS protocols. In addition, we provide interesting insights on the system behavior by characterizing the impact of the blocklength, transmit power and packet size on the reliability performance.

This paper considers the resource allocation for an orthogonal-frequency-division-multiplexing (OFDM)-based cognitive decode-and-forward (DF) relay network. Our objective is to maximize the sum rate (over subcarriers) of the cognitive-radio user with the interference introduced to the primary users (PUs) being managed. The optimization is over subcarrier pairing and power allocation, which leads to a mixed-integer programming (MIP) problem. To resolve this complicated MIP problem at a reasonable cost, we adopt the heterogeneous genetic algorithm (HGA) framework. The main motivation for the HGA framework comes from the idea that it can reduce the impact of extra assumptions made in previous works to simplify the problem. In our HGA, the chromosome is divided into an integer string for subcarrier pairing and a real-number string for power allocation. Two new initialization methods of these chromosomes, which are motivated by the convex optimization theory, are proposed. New crossover and mutation schemes are also devised to accommodate these new chromosomes, as well as to manage the interference to the PUs. Furthermore, we also propose a two-stage low-complexity genetic algorithm, which separately determines the proper subcarrier pairs and power allocations. Our simulations show that our HGAs and the two-stage algorithm provide competing performance, compared with similar state-of-the-art works.

Real-time power optimization for application server clusters based on Mixed-Integer Programming

In this paper, resource allocation for energy efficient communication in multi-cell orthogonal frequency division multiple access (OFDMA) downlink networks with cooperative base stations (BSs) is studied. The considered problem is formulated as a non-convex optimization problem which takes into account the circuit power consumption, the limited backhaul capacity, and the minimum required data rate for joint BS zero-forcing beamforming (ZFBF) transmission. By exploiting the properties of fractional programming, the considered non-convex optimization problem in fractional form is transformed into an equivalent optimization problem in subtractive form, which enables the derivation of an efficient iterative resource allocation algorithm. For each iteration, the optimal power allocation solution is derived with a low complexity suboptimal subcarrier allocation policy for maximization of the energy efficiency of data transmission (bit/Joule delivered to the users). Simulation results illustrate that the proposed iterative resource allocation algorithm converges in a small number of iterations, and unveil the trade-off between energy efficiency and network capacity.

In this paper, we explore a joint subcarrier assignment and power allocation with statistical delay QoS guarantees for full-duplex OFDMA systems. Effective capacity theory which evaluates wireless channel capacity from a novel view of link layer provides a theory basis for modeling the system capacity under the conditions of delay QoS constrains. After modeling the aggregate effective capacity of the system, we formulate joint subcarrier assignment and power allocation problem as a mixed integer programming problem, whose target is to maximize the aggregate effective capacity. To solve this integer programming problem, we allocate initial power on each subcarrier and decompose it into two subproblems, which are subcarrier allocation problem and power allocation problem respectively. Further, we propose an alternative algorithm to achieve joint subcarrier assignment and power allocation, which can meet the “subcarrier continuity” constraint of uplink resource allocation. Simulations have shown that the performance of our proposed scheme is favorite. Additionally, we investigate the effect of the residual selfinterference, inter-node interference (INI) and statistical delay QoS exponent on the performance of systems.

SummaryWe investigate the performance of multiuser Internet of Things (IoT) relay networks in the presence of transceiver hardware impairments. In particular, IoT users communicate with a destination via a simultaneous wireless information and power transfer (SWIPT) amplify‐and‐forward relay. We exploit both time‐switching (TS) and power‐splitting (PS) protocols at the SWIPT‐aided relay terminal. Specifically, we derive the exact outage probability (OP) expressions for both TS and PS schemes under transceiver hardware imperfections over Nakagami‐m fading channels. In addition, we derive the asymptotic OP expressions for both TS and PS schemes in the high signal‐to‐noise ratio regime. From which, the diversity order of can be achieved, where denotes the fading severity parameter for the channel between IoT users and relay. Furthermore, we present a tight upper bound for the ergodic capacity (EC) under both TS and PS schemes with transceiver hardware impairments over Nakagami‐m fading channels. Moreover, our results demonstrate the impact of number of IoT users, fading severity parameters, TS factor, PS factor, and energy conversion efficiency, on the system performance. We also validate our analytical findings via numerical and simulation results.

Joint resource and trajectory optimization in a UAV-enabled dual-function radar-communication network

In this paper, the problem of subcarrier allocation, and power allocation is addressed in an OFDM-based two-tier femto-cell network comprising of a conventional macro-cell and multiple femto-cells. The macro-cell users near the femto-cell causes significant interference to the femto-cell users as well gets interfered by the femto-cell transmissions. We present a subcarrier and power allocation method in underlay femto-cell network while keeping the total interference to macro-cell users under acceptable levels. We maximize the multiple femto-cell users throughput by jointly allocating subcarriers to users within a femto-cell, and also power allocation, under the constraints of cross-tier interference between macro-cell and multiple femto-cells as well as interference among femto-cells. Then, based on this subcarrier and power allocation the performance of the two-tier network is analysed with capacity as the performance metric.

An Application of Interior Point Method to 0-1 Mixed Integer Programing Problems

Future wireless communication systems are expected to provide a broad range of multimedia services that have a significant traffic asymmetry between uplink and downlink. The code division multiple access‐time division duplex (CDMA‐TDD) system is a promising solution to cope with the problem of traffic asymmetry. However, the TDD system is subject to inter‐cell interference compared to frequency division duplex (FDD) system. Since both uplink and downlink share the same frequency in TDD, uplink and downlink may interfere each other especially when neighboring cells require different rates of traffic load. Thus, the resource allocation among cells is an important issue in TDD. In this paper, the resource allocation in the CDMA‐TDD is formulated as a mixed integer programming (MIP) problem. A dynamic resource allocation algorithm (DRAA) is provided that effectively solves the traffic asymmetry problem. The MIP problem is also solved by a well‐known branch and bound procedure. Both the crossed slot and non‐crossed slot allocation are examined and compared to the DRAA. Computational result shows that proposed DRAA gives a good performance as the traffic asymmetry increases between the uplink and downlink. Copyright © 2003 John Wiley & Sons, Ltd.

Multiuser orthogonal frequency division multiplexing (OFDM) is a very promising multiple access technique to efficiently utilize limited RF bandwidth and transmit power in wideband transmission over multipath fading channels. When a wideband spectrum is shared by multiple users in multiuser OFDM-based systems, different users may experience different fading conditions at all subcarriers. Each user is assigned a subset of all subcarriers by some allocation algorithm. Thus, multiuser diversity can be achieved by adaptively adjusting subcarrier, bit, and power allocation depending on channel status among users at different locations (Wong et al., 1999a). In (Wong et al., 1999a), Wong applies a Lagrangian optimization technique and an iterative algorithm to solve the subcarrier, bit and power allocation problem. The suboptimal scheme for the NP-hard joint optimization problem is decoupled into two steps while it has a high computational complexity. A sub-optimal algorithm has been proposed to solve a related problem (Wong et al., 1999b). In (Wong et al., 1999b), Wong presents a real-time subcarrier allocation (SA) algorithm. It is a two-phase algorithm, including the constructive initial assignment (CIA) and the subcarrier swapping steps. The initial subcarrier allocation algorithm needs to pre-determine the numbers of subcarriers for each user before the allocation process starts. The performance of the SAbased algorithm will be compared in the simulation. In (Kim et al., 2006), Kim shows that the allocation problem in (Wong et al., 1999a) can be transformed into an integer programming (IP) problem. The branch-and-bound algorithm (Wolsey, 1998) can be employed to find the optimal solution of the allocation problem which has exponential computational complexity in the worse cases. We utilize the approach to obtain the optimal solution as the performance bound for comparison. Evolutionary algorithms (EA) are used to solve extremely complex search and optimization problems which are difficult to solve through simple methods. EAs are intended to provide a better solution, as it is based on the natural theory of evolution. Evolutionary algorithm (EA)-based schemes have been applied to solve subcarrier, bit, and power allocation problems (Wang et al., 2005) (Reddy et al., 2007) (Reddy & Phora, 2007) (Pao & Chen, 2008). In general, chromosomes can be designed with binary, integer, or real representation. The chromosome lengths are related to the number of subcarriers. Each element in the chromosome is a subcarrier allocated to a user. In this research, a subset of subcarriers can be assigned to one user depending upon the availability of subcarriers at a particular time

In this paper, we study relay selection and power allocation in multiple decode-and-forward (DF) relay assisted OFDM cognitive radio (CR) relay networks. We consider the scenario where only the fading statistics between the cognitive and primary users are known at the CR transmitters. The objective of resource allocation is to maximize the instantaneous capacity of the CR network when the primary users are subject to an average interference constraint and the CR transmitters are subject to maximum transmit power constraint. The joint optimization is a mixed binary integer programming problem which is NP-hard. Thus, we propose a simple suboptimal resource allocation method.

This paper investigates the network slicing technique based resource allocation in small-cell networks. Motivating to maximize the sum rate of the network, it considers the joint design of network slicing (NS), subcarrier allocation and power allocation, optimization problem is formulated as a mixed integer non-linear programing (MINLP) problem. To tackle the formulated MINLP problem, we propose the low-complexity resource allocation scheme, including the heuristic NS assignment algorithm, and the many-to-one matching sub-carrier allocation (M2OM-SA) algorithm. In particular, the heuristic NS algorithm is first carried out, in order to efficiently assign the different number of subbands to different types of users with diverse service requirements. Then, based on the NS result, the M2OM-SA algorithm is implemented independently for each NS group, in which the best available subcarriers can be assigned to the users within the same NS group. Finally, our simulation results show that the proposed resource allocation scheme can significantly outperform the existing schemes in terms of the sum rate of the networks. Therefore, we can conclude that the proposed resource allocation scheme can be considered as a promising candidate for future small-cell networks with diverse service requirements coexist.