Nitin Sharma

A Novel Genetic Algorithm for Adaptive Resource Allocation in MIMO-OFDM Systems with Proportional Rate Constraint

This paper considers base station allocation of subcarriers and power to each user to maximize the sum of user data rates, subject to constraints on total power, bit error rate, and proportionality among user data rates in Multiple Input Multiple Output Orthogonal Frequency Division Multiple access (MIMO-OFDMA) system. Previous allocation methods have been iterative nonlinear methods suitable for offline optimization. The subcarrier allocation is tackled using a novel algorithm which combines the aspects of both deterministic and Genetic Algorithms (GA). This modified GA gave very encouraging results as can be seen from the simulation results shown. The simulation results show a marked improvement in the performance of the algorithm as the number of users increase. The capacity attained from the subcarrier allocation scheme generated by our algorithm is found to be comparable to that attained by previous algorithms.

Rate adaptive resource allocation for multiuser OFDM using NSGA - II

This paper presents a new rate adaptive resource allocation technique for multiuser orthogonal frequency division multiplexing (OFDM) systems. We optimize both bit and subcarrier allocation by considering rate maximization and total power constraint satisfaction. We solve them effectively by combining them into a multi-objective optimization problem. We propose using a non dominated sort genetic algorithm (NSGA-II) - a multi-objective optimization using genetic algorithm. Instead of combining many conflicting objectives into a single function, the NSGA-II uses multiple objective optimizations and brings out solutions which provide a better trade-off taking all conditions into consideration. The simulation results and their marked improvement over previous algorithms provide the basis to this.

On the use of particle swarm optimization for adaptive resource allocation in orthogonal frequency division multiple access systems with proportional rate constraints

Orthogonal frequency division multiple access (OFDMA) is a promising technique, which can provide high downlink capacity for future wireless systems. The total capacity of OFDMA can be maximized by adaptively assigning subchannels to the user with the best gain for that subchannel, with power subsequently distributed by water-filling algorithm. In this paper we have proposed the use of a customized particle swarm optimization (PSO) aided algorithm to allocate the subchannels. The PSO algorithm is population-based: a set of potential solutions evolves to approach a near-optimal solution for the problem under study. The customized algorithm works for discrete particle positions unlike the classical PSO algorithm which is valid for only continuous particle positions. It is shown that the proposed method obtains higher sum capacities as compared to that obtained by previous works, with comparable computational complexity.

Genetic Algorithm Aided Proportional Fair Resource Allocation in Multicast OFDM Systems

The next-generation wireless communication networks are envisioned to offer many multimedia services such as audio/video clips, mobile TV, web browsing, video conference, etc., with diverse quality of service (QoS) requirements. Multicasting/broadcasting has been recognized as an emerging and enabling technique for such multimedia transmissions over wireless networks. By employing multicast transmission, a base station can transmit the same data content to several groups of users requiring flexible QoS. In this paper, we investigate subchannel and power allocation problems in an OFDM-based wireless multicast system. With the goal of maximizing the total system capacity, a low complexity, novel genetic algorithm aided efficient subchannel allocation scheme taking into account the constraints of total available bandwidth, proportional data rate fairness and total transmit power budget at the base station is proposed. The subchannel allocation is then followed by optimal power allocation. Unlike previous work where either no fairness or fairness based on number of subchannels allocated to the different groups was considered, we incorporate a set of system parameters in the problem formulation such that the ratio of the group data rates strictly follow the set of system parameters after resource allocation. Simulation results show that the proposed method obtains higher sum capacities while maintaining proportional data rate fairness among different multicast groups, without being awfully complex.

Multiobjective Subchannel and Power Allocation in Interference-Limited Two-Tier OFDMA Femtocell Networks

The cochannel deployment of femtocells in a macrocell network is a cost-effective and efficient way to increase network coverage and capacity. However, such deployment is exigent due to the presence of inter- and intratier interference and the ad hoc operation of femtocells. Motivated by the flexible subchannel allocation capability of orthogonal frequency-division multiple access, in this paper, we consider the problem of joint subchannel and power allocation in both the uplink and downlink of a two-tier orthogonal-frequency-division-multiplexing-based femtocell network. It is a multiobjective optimization problem that aims to maximize the throughput of all users, simultaneously increasing the power efficiency of femtocell base stations. Interference to macrocell users is kept below a certain tolerable threshold. The minimum-rate requirements of delay-sensitive users are also taken into consideration. The problem is solved using nondominated sorting genetic algorithm version II, and the results are compared with the existing solution.

On the use of NSGA-II for multi-objective resource allocation in MIMO-OFDMA systems

This paper investigates the problem of dynamic subcarrier and bit allocation in downlink of Multiple Input Multiple Output (MIMO) Orthogonal Frequency Division Multiple Access (OFDMA) Systems. Using Singular Value Decomposition, the MIMO fading channel of each subcarrier is transformed into an equivalent bank of parallel Single Input Single Output sub-channels. To achieve the capacity bound, one must solve a multiuser subcarrier allocation and the optimal bit allocation jointly. To alleviate the computational complexity of joint subcarrier and bit allocation, several suboptimal solutions have been proposed. These suboptimal solutions handle subcarrier and bits individually. We propose the use of Non-dominated Sorting Genetic Algorithm (NSGA)-II, which is a multi-objective Genetic Algorithm, for joint allocation of bits and subcarriers, in the downlink of MIMO-OFDMA system. NSGA-II is intended for optimization problems involving multiple conflicting objectives. Here the two conflicting objectives are Rate Maximization and Transmit Power Minimization. The simulation results indicate remarkable improvement in terms of convergence over previous approaches involving Evolutionary algorithms. At the same time capacity achieved by the proposed algorithm is found to be comparable with that of previous algorithms.

Joint subcarrier and power allocation in downlink OFDMA systems: an multi-objective approach

In this paper, we present a new technique for resource allocation in multi-user orthogonal frequency division multiple access systems. The goal is to maximise the minimum data rate available to any user while minimising the total transmitted power. In order to achieve an optimal solution and capacity bounds, the subcarrier and power should be allocated simultaneously. Multi-objective genetic algorithm can be used for joint allocation of subcarriers and power in such a case, and in this paper, it is achieved using non-dominated sorting genetic algorithm-II. The simulation results indicate that the proposed algorithm achieves high data rates as compared with previous algorithms. The algorithm allocates both subcarriers and bits jointly without being computationally expensive. The faster convergence of the algorithm to near optimal value, as compared with previous algorithms, is indicative of its less complexity. Copyright

Differential evolution aided adaptive resource allocation in OFDMA systems with proportional rate constraints

Graphical abstractDisplay Omitted HighlightsWe propose use of CMODE for resource allocation in OFDMA systems.We use CMODE for both joint as well as separate subcarrier and power allocation.Proposed solutions achieve better capacity as compared to traditional methods.Because of lower complexity the proposed schemes are faster as compared to traditional methods. Orthogonal frequency division multiple access (OFDMA) is a promising technique, which can provide high downlink capacity for the future wireless systems. The total capacity of OFDMA systems can be maximized by adaptively assigning subcarriers to the user with the best gain for that subcarrier, with power subsequently distributed by water-filling. In this paper, we propose the use of a differential evolution combined with multi-objective optimization (CMODE) algorithm to allocate the resources to the users in a downlink OFDMA system. Specifically, we propose two approaches for resource allocation in downlink OFDMA systems using CMODE algorithm. In the first approach, CMODE algorithm is used only for subcarrier allocation (OSA), while in the second approach, the CMODE algorithm is used for joint subcarrier and power allocation (JSPA). The CMODE algorithm is population-based where a set of potential solutions evolves to arrive at a near-optimal solution for the problem under study. During the past decade, solving constrained optimization problems with evolutionary algorithms has received considerable attention among researchers and practitioners. CMODE combines multi-objective optimization with differential evolution (DE) to deal with constrained optimization problems. The comparison of individuals in CMODE is based on multi-objective optimization, while DE serves as the search engine. In addition, infeasible solution replacement mechanism based on multi-objective optimization is used in CMODE, with the purpose of guiding the population towards the promising solutions and the feasible region simultaneously. It is shown that both the proposed approaches obtain higher sum capacities as compared to that obtained by previous works, with comparable computational complexity. It is also shown that the JSPA approach provides near optimal results at the slightly higher computational cost.

Bee colony optimization aided adaptive resource allocation in OFDMA systems with proportional rate constraints

Orthogonal frequency division multiple access (OFDMA) is a promising technique, which can provide high downlink capacity for the emerging wireless systems. The total capacity of OFDMA can be maximized by adaptively assigning subcarriers to the users with the best gains for those subcarriers, with power subsequently distributed by water-filling. In this paper, we propose the use of artificial bee colony (ABC) algorithm combined with Deb’s selection mechanism to handle the constraints. In this scheme, a probabilistic selection scheme assigns probability values to feasible solutions based on their fitness values and to infeasible individuals based on their violations, to allocate the resources to the users in downlink OFDMA system. Specifically we propose two approaches for resource allocation in downlink OFDMA systems using ABC algorithm. In the first approach, ABC algorithm is used for subcarrier allocation only, while in second approach the ABC algorithm is used for joint subcarrier and power allocation. It is shown that both these approaches obtain higher sum capacities as compared to that obtained by previous works, with comparable computational complexity. It is also shown that the joint subcarrier and power allocation approach provides near optimal results at the cost of slightly higher computational cost.

Composite differential evolution aided channel allocation in OFDMA systems with proportional rate constraints

Orthogonal frequency division multiple access (OFDMA) is a promising technique, which can provide high downlink capacity for the future wireless systems. The total capacity of OFDMA can be maximized by adaptively assigning subchannels to the user with the best gain for that subchannel, with power subsequently distributed by water-filling. In this paper, we propose the use of composite differential evolution (CoDE) algorithm to allocate the subchannels. The CoDE algorithm is population-based where a set of potential solutions evolves to approach a near-optimal solution for the problem under study. CoDE uses three trial vector generation strategies and three control parameter settings. It randomly combines them to generate trial vectors. In CoDE, three trial vectors are generated for each target vector unlike other differential evolution (DE) techniques where only a single trial vector is generated. Then the best one enters the next generation if it is better than its target vector. It is shown that the proposed method obtains higher sum capacities as compared to that obtained by previous works, with comparable computational complexity.