Suboptimal Resource Allocation Algorithm Research Articles

A Resource Optimization Algorithm for Collaborative and Cognitive Systems Based on Sparse Clustering Algorithm

With the rapid development of wireless communication technology, new wireless applications and services are exploding. These new applications and services usually need wider bandwidth and higher speed. The voice of people calling for a fast and convenient network is getting stronger and stronger, and the wireless network must be the main development direction of the network in the future world. In order to make the development of wireless communication technology not limited by limited resources, resource optimization design has always been a research hotspot of scientists. Among them, the optimal resource algorithm can get the theoretical global optimal solution, while the sub-optimal resource allocation algorithm, as a supplement to the former, can be used to reduce costs and improve efficiency when the system structure is too complex. In this paper, a resource optimization method based on sparse clustering algorithm is proposed, and the matching relationship between spectrum resources and the services carried by cognitive terminals is established, so that the effectiveness of collaboration and cognitive systems is verified.

Energy-Spectrum Efficient Content Distribution in Fog-RAN Using Rate-Splitting, Common Message Decoding, and 3D-Resource Matching

Multi-objective resource allocation is studied for edge-caching enabled fog-radio access network. Notably, joint maximization of the energy-efficiency (EE) and spectrum-efficiency (SE) and interference management are investigated for distributing contents from the cache-enabled fog access points (F-APs) and cloud base station (CBS) to the user devices (UDs). In our envisioned system, the UDs are grouped into multiple non-overlapping device-clusters based on their locations. A rate-splitting with common message decoding based transmission strategy is applied to enable UDs of each device-cluster to receive data from a suitably selected F-AP and CBS over the same radio resource blocks. To maximize system EE and SE jointly, a multi-objective optimization problem (MOOP) is formulated and it is solved in three stages. At first, by employing the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> -constraint method, the MOOP is converted to an EE-SE trade-off optimization problem. Then, by leveraging iterative function evaluation based power control and generalized 3D-resource matching, the EE-SE trade-off optimization problem is solved and a novel resource allocation algorithm is proposed to obtain near-optimal Pareto-front for the proposed MOOP. To reduce the complexity of obtaining near-optimal Pareto-front, a sub-optimal resource allocation algorithm is proposed as well. Finally, a low-complexity algorithm is devised to select a suitable operating EE-SE pair from the obtained Pareto-front. The conducted simulations demonstrate that the proposed resource allocation schemes achieve substantial improvement of system EE and SE over the benchmark schemes.

Resource Allocation for Multi-User Downlink MISO OFDMA-URLLC Systems

This article considers the resource allocation algorithm design for downlink multiple-input single-output (MISO) orthogonal frequency division multiple access (OFDMA) ultra-reliable low latency communication (URLLC) systems. To meet the stringent delay requirements of URLLC, short packet transmission is adopted and taken into account for resource allocation algorithm design. The resource allocation is optimized for maximization of the weighted system sum throughput subject to quality-of-service (QoS) constraints regarding the URLLC users’ number of transmitted bits, packet error probability, and delay. Despite the non-convexity of the resulting optimization problem, the optimal solution is found via monotonic optimization. The corresponding optimal resource allocation policy can serve as a performance upper bound for sub-optimal low-complexity solutions. We develop such a low-complexity sub-optimal resource allocation algorithm based on successive convex approximation and difference of convex programming. Our simulation results reveal the importance of using multiple antennas for reducing the latency and improving the reliability of URLLC systems. Moreover, the proposed sub-optimal algorithm is shown to closely approach the performance of the proposed optimal algorithm and outperforms two baseline schemes by a considerable margin, especially when the users have heterogeneous delay requirements. Finally, conventional resource allocation designs based on Shannon’s capacity formula are shown to be not applicable in MISO OFDMA-URLLC systems as they are not able to guarantee the users’ delay constraints.

Energy efficient resource allocation for D2D multicast communications

The resource allocation for device-to-device (D2D) multicast communications is investigated. To achieve fair energy efficiency (EE) among different multicast groups, the max-min fairness criterion is used as the optimization criterion and the EE of D2D multicast groups are taken as the optimization objective function. The aim is to maximize the minimum EE for different D2D multicast groups under the constraints of the maximum transmit power and minimum transmit rate, which is modeled as a non-convex and mixed-integer fractional programming problem. Here, suboptimal resource allocation algorithms are proposed to solve this problem. First, channel assignment scheme is performed to assign channel to D2D multicast groups. Second, for a given channel assignment, iterative power allocation schemes with and without loss of cellular users’ rate are completed, respectively. Simulation results corroborate the convergence performance of the proposed algorithms. In addition, compared with the traditional throughput maximization algorithm, the proposed algorithms can improve the energy efficiency of the system and the fairness achieved among different multicast groups.

Robust and Secure Resource Allocation for Full-Duplex MISO Multicarrier NOMA Systems

In this paper, we study the resource allocation algorithm design for multiple-input single-output (MISO) multicarrier non-orthogonal multiple access (MC-NOMA) systems, in which a full-duplex base station serves multiple half-duplex uplink and downlink users on the same subcarrier simultaneously. The resource allocation is optimized for maximization of the weighted system throughput while the information leakage is constrained and artificial noise is injected to guarantee secure communication in the presence of multiple potential eavesdroppers. To this end, we formulate a robust non-convex optimization problem taking into account the imperfect channel state information of the eavesdropping channels and the quality-of-service requirements of the legitimate users. Despite the non-convexity of the optimization problem, we solve it optimally by applying monotonic optimization which yields the optimal beamforming, artificial noise design, subcarrier allocation, and power allocation policy. The optimal resource allocation policy serves as a performance benchmark since the corresponding monotonic optimization-based algorithm entails a high computational complexity. Hence, we also develop a low-complexity suboptimal resource allocation algorithm which converges to a locally optimal solution. Our simulation results reveal that the performance of the suboptimal algorithm closely approaches that of the optimal algorithm. Besides, the proposed optimal MISO NOMA system can not only ensure downlink and uplink communication security simultaneously but also provides a significant system secrecy rate improvement compared with the traditional MISO orthogonal multiple access systems and two other baseline schemes.

Joint Beamforming and Power Allocation in Downlink NOMA Multiuser MIMO Networks

In this paper, a novel joint design of beamforming and power allocation is proposed for a multi-cell multiuser multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) network. In this network, base stations (BSs) adopt coordinated multipoint (CoMP) for downlink transmission. We study a new scenario where the users are divided into two groups according to their quality-of-service (QoS) requirements, rather than their channel qualities as investigated in the literature. Our proposed joint design aims to maximize the sum-rate of the users in one group with the best-effort while guaranteeing the minimum required target rates of the users in the other group. The joint design is formulated as a non-convex NP-hard problem. To make the problem tractable, a series of transformations are adopted to simplify the design problem. Then, an iterative suboptimal resource allocation algorithm based on successive convex approximation is proposed. In each iteration, a rank-constrained optimization problem is solved optimally via semidefinite program relaxation. Numerical results reveal that the proposed scheme offers significant sum-rate gains compared to the existing schemes and converges fast to a suboptimal solution.

Power-Efficient and Secure WPCNs With Hardware Impairments and Non-Linear EH Circuit

In this paper, we design a robust resource allocation algorithm for a wireless-powered communication network (WPCN) taking into account residual hardware impairments (HWIs) at the transceivers, the imperfectness of the channel state information, and the non-linearity of practical radio frequency energy harvesting circuits. In order to ensure power-efficient secure communication, physical layer security techniques are exploited to deliberately degrade the channel quality of a multiple-antenna eavesdropper. The resource allocation algorithm design is formulated as a non-convex optimization problem for minimization of the total power consumption in the network, while guaranteeing the quality of service of the information receivers in terms of secrecy rate. The globally optimal solution of the optimization problem is obtained via a 2-D search and semidefinite programming relaxation. To strike a balance between computational complexity and system performance, a low-complexity iterative suboptimal resource allocation algorithm is also proposed. Numerical results demonstrate that both the proposed optimal and suboptimal schemes can significantly reduce the total system power consumption required for guaranteeing secure communication, and unveil the impact of HWIs on the system performance: 1) residual HWIs create a system performance bottleneck in WPCN in the high transmit/receive power regimes; 2) increasing the number of transmit antennas can effectively reduce the power consumption of wireless power transfer and alleviate the performance degradation due to residual HWIs; and 3) imperfect CSI exacerbates the impact of residual HWIs, which increases the power consumption of both wireless power and wireless information transfer.

Heterogeneous Spectrum Aggregation: Coexistence From a Queue Stability Perspective

Spectrum aggregation (SA) across heterogeneous channels, including both dedicated and shared channels, provides the potential for improving spectrum utilization and fulfilling the requirement of broadband services. Heterogeneous SA brings new technical challenges on multisystem coexistence on shared channels and the resource allocation over heterogeneous channels. In this paper, we develop an analytical framework for heterogeneous SA from a queue stability perspective. To make all systems on the shared channels stable, we design a resource allocation algorithm for the coexistence of multiple systems. Specifically, we derive the closed-form modified water-filling power control for the single-pair case by Lyapunov optimization and prove that it achieves the queue stability for all systems. Based on the results, we propose a low-complexity suboptimal resource allocation algorithm for multipair SA, which is a NP-hard problem. We partition user pairs into groups by using graph coloring and allocate the shared channels to pair groups according to the maximal weight bipartite matching model. The simulation results verify the queue stability and show that the proposed schemes outperform the conventional schemes.

Energy-Efficient Resource Allocation for Mobile-Edge Computation Offloading

Mobile-edge computation offloading (MECO) off-loads intensive mobile computation to clouds located at the edges of cellular networks. Thereby, MECO is envisioned as a promising technique for prolonging the battery lives and enhancing the computation capacities of mobiles. In this paper, we study resource allocation for a multiuser MECO system based on time-division multiple access (TDMA) and orthogonal frequency-division multiple access (OFDMA). First, for the TDMA MECO system with infinite or finite cloud computation capacity, the optimal resource allocation is formulated as a convex optimization problem for minimizing the weighted sum mobile energy consumption under the constraint on computation latency. The optimal policy is proved to have a threshold-based structure with respect to a derived offloading priority function , which yields priorities for users according to their channel gains and local computing energy consumption. As a result, users with priorities above and below a given threshold perform complete and minimum offloading, respectively. Moreover, for the cloud with finite capacity, a sub-optimal resource-allocation algorithm is proposed to reduce the computation complexity for computing the threshold. Next, we consider the OFDMA MECO system, for which the optimal resource allocation is formulated as a mixed-integer problem. To solve this challenging problem and characterize its policy structure, a low-complexity sub-optimal algorithm is proposed by transforming the OFDMA problem to its TDMA counterpart. The corresponding resource allocation is derived by defining an average offloading priority function and shown to have close-to-optimal performance in simulation.

Power optimization and subcarrier allocation for downlink MIMO-OFDMA based cognitive radio networks

This paper presents a novel resource allocation framework for downlink transmissions in MIMO-OFDMA based cognitive radio (CR) networks. In this literature, due to the coexistence of primary and secondary users (SUs) in the same geographical area, the interference induced to the primary users’ (PUs) bands from SUs’ transmissions should be less than the tolerable interference threshold predefined by PU. Hence, the optimal resource allocation strategy is performed subject to the total power constraint in the cognitive base station and the interference power constraint for PUs so that the downlink sum throughput of the CR network can be maximize without causing unacceptable interference. To this aim, two sub-problems are presented and solved theoretically based on convex optimization framework that can be used in the main body of the proposed algorithm. Regarding to the first sub-problem, the resources are distributed among all SUs in a way that just the total power constraint is satisfied. Also, the sum throughput is maximized as the interference power onto the PUs’ bands keeps in a tolerable range in the second sub-problem. So, in order to find the optimum solution, our algorithm consists of three parts. The first two parts include these sub-problems while the last section includes a combination of them. For complexity reduction, two sub-optimal resource allocation algorithms are introduced based on the simplification of main problem constraints. The performance of different approaches is investigated via simulation. Conducted simulation results show that the optimal algorithm achieves better performance in comparison with benchmark algorithms and converges to sub-problem solutions in high and low regimes of interference thresholds.

Joint Optimization Methods for Nonconvex Resource Allocation Problems of Decode-and-Forward Relay-Based OFDM Networks

Generally, resource allocation for multicarrier cooperation communication networks includes subcarrier and power allocation; however, it is difficult to solve because of the 0-1 integer programming of subcarrier allocation, which makes the problem nonconvex. This paper focuses on solving the nonconvex problems and provides a general solution to the resource allocation for relay-enhanced multicarrier systems. An established scenario, namely, a decode-and-forward (DF) relay-assisted orthogonal frequency-division multiplexing (OFDM) system, is considered, and we formulate the resource allocation as a joint subcarrier pairing, assignment, and power allocation problem, in which heterogeneous users' data rate requirements are also considered. To make the expression of the proposed optimization approaches for the original maximization problem clear, a four-step methodology is given. First, we transform the original nonconvex problem into a standard convex problem by imposing a new constraint of subcarrier allocation index, regardless of the convexity of the objective function. Furthermore, we prove that the optimum resource allocation algorithm (ORAA) for the dual problem of the transformed optimization problem is equivalent to the optimization of the original function. Subsequently, the optimum solution could be obtained by the golden section search method and the iterative resource allocation algorithm. Finally, a suboptimal resource allocation algorithm (SRAA) that solves the primal problem in an asymptotic manner is proposed. Simulation results illustrate that our proposed SRAA achieves comparable performance to the ORAA with satisfied complexity.

Power Efficient Resource Allocation for Full-Duplex Radio Distributed Antenna Networks

In this paper, we study the resource allocation algorithm design for distributed antenna multiuser networks with full-duplex (FD) radio base stations (BSs), which enable simultaneous uplink and downlink communications. The considered resource allocation algorithm design is formulated as an optimization problem taking into account the antenna circuit power consumption of the BSs and the quality of service (QoS) requirements of both uplink and downlink users. We minimize the total network power consumption by jointly optimizing the downlink beamformer, the uplink transmit power, and the antenna selection. To overcome the intractability of the resulting problem, we reformulate it as an optimization problem with decoupled binary selection variables and nonconvex constraints. The reformulated problem facilitates the design of an iterative resource allocation algorithm, which obtains an optimal solution based on the generalized Bender’s decomposition (GBD). For this algorithm, we also propose a simple technique to improve the speed of convergence. Furthermore, to strike a balance between computational complexity and system performance, a suboptimal resource allocation algorithm with polynomial time complexity is proposed. Simulation results illustrate that the proposed GBD-based iterative algorithm converges to the globally optimal solution and the suboptimal algorithm achieves a close-to-optimal performance. Our results also demonstrate the tradeoff between power efficiency and the number of active transmit antennas when the circuit power consumption is taken into account. In particular, activating an exceedingly large number of antennas may not be an efficient approach for reducing the total system power consumption. In addition, our results reveal that FD systems facilitate significant power savings compared to traditional half-duplex systems, despite the nonnegligible self-interference.

Resource Allocation for Dynamic Intra-Cell Subcarrier Reuse in Cooperative OFDMA Wireless Networks

Resource reuse schemes in relay-enhanced cooperative orthogonal frequency-division multiple access (OFDMA) networks have been well studied in the literature from an interference mitigation perspective and with cell-partitioning being the basis. In this paper however, we study dynamic intra-cell subcarrier reuse in cooperative OFDMA networks in which the users are allowed to share any subcarrier in the relay links provided that the resultant intra-cell interference is managed; an option not available in non-cooperative networks. Specifically, when the target data-rates of the users are not reachable in a conventional cooperative OFDMA network, dynamic intra-cell subcarrier reuse enables the users to achieve their target data-rates, even when the number of users surpasses the number of subcarriers. We formally define the problem of maximizing the system sum-rate subject to per node power and target data-rate constraints in a cooperative OFDMA single-cell network in which subcarriers are allowed to be reused in the relay links. After analyzing the problem in the dual domain and deriving the performance bounds, we propose a suboptimal resource allocation algorithm where subcarriers and links (relay and/or direct) are assigned to the users. Simulation results show that by employing our resource allocation algorithm, the system sum-rate and the outage ratio are significantly improved, specially in an overpopulated network.

Collaborative Self-Healing With Opportunistic IBS Selection in Indoor Wireless Communication Systems

To resolve coverage hole problems caused by indoor base-station (IBS) faults, we propose a new frame structure and a practical algorithm based on an optimization technique. Our main contributions can be described as follows: 1) a frame structure with healing subchannels for overcoming abnormal IBS faults and 2) an efficient suboptimal resource allocation algorithm with opportunistic IBS selection to reduce the complexity of the optimization problem. Through intensive simulations, we evaluate the performance of our proposed algorithm with respect to average cell capacity, user fairness, and outage probability and compare it with the performance of conventional algorithms.

Energy-Per-Bit Minimized Radio Resource Allocation in Heterogeneous Networks

In this paper, we present an energy-per-bit minimized radio resource allocation scheme in heterogeneous networks equipped with multi-homing capability, simultaneously connecting to different wireless interfaces. Specifically, we formulate an optimization problem related to minimization of energy-per-bit which takes a form of nonlinear fractional programming. Then we derive a parametric optimization problem out of that fractional programming and solve the original problem by using a double-loop iteration method. In each iteration, we derive the optimal resource allocation policy by applying Lagrangian duality and an efficient dual update method. In addition, we present suboptimal resource allocation algorithms using the properties of the optimal resource allocation policy. Numerical results reveal that the optimal allocation algorithm improves energy efficiency significantly over the existing resource allocation algorithms designed for homogeneous networks and its performance is superior to suboptimal algorithms in reducing energy consumption as well as in enhancing network energy efficiency.

Power and Subcarrier Allocation for Multicast Service in OFDMA Decode-and-Forward Relay Systems

Orthogonal Frequency Division Multiplexing Access (OFDMA) relay system is a promising technique for increasing the capacity and the range of wireless communication systems. Dynamic resource allocation for OFDMA relay systems plays an important role in improving the system performance. However, most of the resource allocation algorithms are designed for the unicast traffics, and resource allocation algorithm for multicast traffics in OFDMA relay systems has received little attention. In this paper, the resource allocation problem for the decode-and-forward relay assisted OFDMA multicast system is considered. Our goal is to maximize the sum rate of the multicast system under the constraints of total power limit and exclusive subcarrier usage. The multicast resource allocation problem is formulated as a mixed integer programming problem. The problem is firstly solved by using Lagrangian dual decomposition method, resulting in an asymptotically optimal joint subcarrier and power allocation algorithm with computational complexity being linear with the number of multicast groups and subcarriers. Furthermore, a low complexity suboptimal resource allocation algorithm is presented to further reduce the computational complexity. Simulation results show that both the proposed optimal and suboptimal algorithms can significantly improve the system performance and the proposed suboptimal algorithm can achieve a near optimal solution.

Resource allocation in orthogonal frequency division multiple access‐based cognitive radio systems with minimum rate constraints

SUMMARYIn this paper, resource allocation problem in orthogonal frequency division multiple access‐based cognitive radio (CR) systems to maintain minimum transmission rate constraints of CR users (CRUs) with the specified interference thresholds is investigated. Firstly, a single primary user (PU) CR system is considered, and a suboptimal resource allocation algorithm to maximize the sum transmission rate of all CRUs is proposed. Secondly, the single‐PU scenario is extended to multiple‐PU case, and an asymptotically optimal resource allocation algorithm is proposed using dual methods subject to constraints on both interference thresholds of PUs and total transmit power of all CRUs. Analysis and numerical results show that, in contrast to classical resource allocation algorithms, the proposed algorithm can achieve higher transmission rate and guarantee each CRU's minimum transmission rate in both scenarios. Copyright © 2012 John Wiley &amp; Sons, Ltd.

Dynamic Radio Resource Allocation for Macro-Femto Hybrid Cellular Network Maintaining Fairness

Macro-Femto Hybrid Cellular Network (MFHCN) has been considered as one of the most promising infrastructure for the upcoming next generation cellular network. Femto-Cell (FC) is very effective when it comes to reducing poor signal coverage. The range of the device might be low but it functions to reduce the traffic congestion of the larger Macro Network as well as provide enhanced data rate for both voice and data transmission. In short, it provides better Quality of Service (QoS), i.e., reduce congestion, increase capacity, reduce system outage, while using minimal power consumption. In this work we propose a suboptimal Resource Allocation algorithm for MFHCN. Multiuser and Multiservice are considered in this proposed model. A priority parameter is introduced to maintain fairness amongst all users. By doing so, we were successful in achieving improved outage probability without compromising the total system throughput.

Optimal Resource Allocation for Increasing Strictly Concave Utility Functions in Wireless Networks

Utility functions are widely used to model user-perceived service quality. For elastic traffic, the utility function is often concave. An elastic allocation (EA) algorithm has recently been proposed to maximize the total utility obtained by users, assuming resource is infinitesimally divisible and queues are constantly backlogged. We found that the EA algorithm is not always optimal. In fact, the solution it obtains can be infeasible. In this paper, we present a modified EA algorithm that is guaranteed to find the optimal solution under the same assumptions. The result is generalized for a system where queues are generally backlogged. In a real system, there is normally a basic unit for resources. Therefore, we further extend the designs to such a system for both constantly backlogged and generally backlogged queues. To reduce computational complexity, we also propose suboptimal resource allocation algorithms. Simulations are conducted to evaluate the proposed algorithms in terms of utility sum and execution time. Results show that our proposed algorithms perform better than previous works. Moreover, the performances of the proposed suboptimal algorithms are close to those of the optimal algorithms.

Optimal and Efficient Graph-Based Resource Allocation Algorithms for Multiservice Frame-Based OFDMA Networks

This paper addresses the resource allocation problem in Orthogonal Frequency Division Multiple Access (OFDMA)-based wireless networks. The resource allocation problem is posed as an optimization problem with individual user constraints. This formulation provides a special structure that lends to efficient solution of the problem. We develop an optimal algorithm based on standard graph theory and Lagrangian relaxation. Based on the special structure of the problem, the proposed resource allocation algorithm attains the optimal solution at a much lower complexity compared to general-purpose optimization algorithms used by previous OFDMA resource allocation approaches. Moreover, the resource allocation problem solved by the proposed algorithm supports practical features such as discrete modulation set and multiple OFDM symbols per resource allocation decision. Furthermore, by assuming even power allocation across the OFDM subchannels, a suboptimal resource allocation algorithm with lower complexity is developed. The proposed algorithms enable the system designer to control the tradeoffs among system performance, system complexity, and the quality of service (QoS) experienced by the users. Extensive simulations are conducted to evaluate the performance and complexity of the proposed algorithms under different system operating conditions.