Chunk-Based Resource Allocation in OFDMA Systems—Part II: Joint Chunk, Power and Bit Allocation

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.

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

The resource allocation of Orthogonal Frequency Division Multiple Access (OFDMA) is one of the core issues in the next generation mobile systems. The improvement in the performance and quality of service (QoS) of communication systems is relying upon the efficient utilization of the available communication resources. The resource allocation of the OFDMA systems is mainly depends on both power and subcarrier allocations of each user for different operation scenarios and channel conditions. This paper proposes and applies Firefly Pack Algorithm (FPA) to find the optimal or near optimal power and subcarrier allocations for OFDMA systems. It takes into consideration the power and subcarrier allocations constrains, channel and noise distributions, distance between users equipments and base station, user priority weight to approximate the most of the variables, constrains, and parameters that encounter in the OFDMA systems. Four important cases for the number of subcarriers and users are addressed, simulated, and analyzed with employing the FPA algorithm under specific operation scenarios to meet the standard specifications. The results demonstrate that FPA is an effective algorithm in finding the optimal or near optimal for both subcarrier and power allocation.

Orthogonal frequency division multiple access (OFDMA) is one of the most popular access schemes adopted in recent wireless systems including, but not limited to, 4G-LTE. Consequently, OFDMA resource allocation raises as one of the key research topics since the capacity and quality of service depend on optimising the usage of the available resources. Resource allocation of OFDMA systems relies on power and subcarrier allocations of each user for different operational scenarios and channel conditions. In this paper, we propose and implement bat pack algorithm (BPA) to find the optimal, or near optimal, power and subcarrier allocations for OFDMA systems. The proposed algorithm considers power and subcarrier allocation constrains, channel and noise distributions, distance between user equipment and base station, user priority weight. Four cases are addressed, simulated, and analysed employing the BPA algorithm under specific operational scenarios to meet the standard specifications of current communication systems.

In orthogonal frequency division multiple access (OFDMA) systems, efficient resource allocation is very important to improve the performance of the OFDMA system. The general principle of resource allocation in an OFDMA system is to assign each subcarrier to the user with the best channel condition for that subcarrier. However, in delay tolerant traffic, current work did not consider that packets on retransmissions may have different allocation criteria from those on their first transmissions. In this paper, the resource allocation scheme is investigated by considering different criterions to packets on first transmissions and on retransmissions. Under a bit error rate (BER) constraint, the objective is to maximize the throughput of packets on first transmissions, meanwhile decreasing the longest packet delay caused by retransmitted packets and guaranteeing all packets on retransmissions to be sent out. A low-complexity suboptimal algorithm that separates allocation for packets on retransmissions and on first transmissions is proposed. In the proposed algorithm, for packets on retransmissions the resource allocation can be formulated to minimize power consumption; for packets on first transmissions the resource allocation is carried out to maximize throughput. Adaptive modulation scheme is adopted to guarantee the BER constraint, except the modulation for each retransmitted packet remains the same. In order to improve retransmission liability, the retransmitted packets are combined with its failed packets in previous transmission.

In the orthogonal frequency division multiple access (OFDMA) system, one of the efficient and low complex methods to allocate radio resources among multiple users is chunk-based resource allocation, which groups a number of adjacent subcarriers into a chunk and allocates resources chunk by chunk. In this paper, performance analysis of chunk-based resource allocation is studied in the multi-cell OFDMA environment. Fractional frequency reuse (FFR) is considered in the cellular OFDMA. Basically, FFR divides each cell into central and edge areas where two different values of the frequency reuse factor are assumed. This paper analytically evaluates how spectral efficiency performance is affected by system parameters, including radius ratio of the central area to the whole cell, transmit signal to noise ratio (SNR), number of users, number of subcarriers per chunk, and coherence bandwidth. The numerical results show that there exists an optimal radius ratio to achieve the highest spectral efficiency in the proposed research. The optimal radius ratio is about 0.7, which is almost irrespective of the SNR, number of users, and number of subcarriers per chunk. In other words, the sizes of the central area and the edge area of the whole cell are almost equal when achieving the optimal performance.

In this paper, a downlink multi-carrier cognitive radio (CR) network is considered. The CR network consists of one cognitive base station (CBS) and a set of secondary users (SUs) sharing the same spectrum with the primary user (PU). Chunk-based resource allocation is adopted where subcarriers are grouped into chunks for allocation to the SUs. The problem of chunk-based resource allocation under the interference power constraint and the transmit power constraint is investigated. The objective is to maximize the sum rate of the SUs. For this, based on Lagrange dual method, a near-optimal joint chunk and power allocation scheme is proposed. The complexity of the optimal scheme is exponential in the number of chunks, while the complexity of the proposed scheme is reduced significantly to only linear in the number of chunks, and at the same time, it is shown that the proposed scheme achieves almost the same performance that can be achieved by the optimal scheme. The impacts of the interference power constraint, the transmit power constraint, number of subcarriers within the chunk and the channel coherence bandwidth on the performance of the proposed scheme are investigated. Particularly, it is shown that increasing the channel coherence bandwidth does not always lead to improvement of the SU performance.

Orthogonal frequency division multiple access (OFDMA), which is widely used in the wireless sensor networks, allows different users to obtain different subcarriers according to their subchannel gains. Therefore, how to assign subcarriers and power to different users to achieve a high system sum rate is an important research area in OFDMA systems. In this paper, the focus of study is on the rate adaptive (RA) based resource allocation with proportional fairness constraints. Since the resource allocation is a NP-hard and non-convex optimization problem, a new efficient resource allocation algorithm ACO-SPA is proposed, which combines ant colony optimization (ACO) and suboptimal power allocation (SPA). To reduce the computational complexity, the optimization problem of resource allocation in OFDMA systems is separated into two steps. For the first one, the ant colony optimization algorithm is performed to solve the subcarrier allocation. Then, the suboptimal power allocation algorithm is developed with strict proportional fairness, and the algorithm is based on the principle that the sums of power and the reciprocal of channel-to-noise ratio for each user in different subchannels are equal. To support it, plenty of simulation results are presented. In contrast with root-finding and linear methods, the proposed method provides better performance in solving the proportional resource allocation problem in OFDMA systems.

In this paper, we formulate a joint optimization problem for resource allocation and scheduling in full-duplex orthogonal frequency division multiple access (OFDMA) relaying systems with amplify-and-forward (AF) and decode-and-forward (DF) relaying protocols. Our problem formulation takes into account heterogeneous data rate requirements for delay sensitive users. Besides, a theoretically optimal hybrid relaying, which allows a dynamic selection between AF relaying and DF relaying protocols with full-duplex relays or half-duplex relays, is also considered in the problem formulation and serves as a performance benchmark. A dual decomposition method is employed to solve the resulting optimization problem and a novel distributed iterative resource allocation and scheduling algorithm with closed-form power and subcarrier allocation is derived. Simulation results illustrate that the proposed distributed algorithm requires only a small number of iterations to achieves practically the same performance as the optimal centralized algorithm.

In this paper, resource allocation for energy-efficient communication in an orthogonal frequency division multiple access (OFDMA) downlink network with a large number of transmit antennas is studied. The considered problem is modeled as a non-convex optimization problem which takes into account the circuit power consumption, imperfect channel state information at the transmitter (CSIT), and different quality of service (QoS) requirements including a minimum required data rate and a maximum tolerable channel outage probability. The power allocation, data rate adaptation, antenna allocation, and subcarrier allocation policies are optimized for maximization of the energy efficiency of data transmission (bit/Joule delivered to the users). By exploiting the properties of fractional programming, the resulting non-convex optimization problem in fractional form is transformed into an equivalent optimization problem in subtractive form, which leads to an efficient iterative resource allocation algorithm. In each iteration, the objective function is lower bounded by a concave function which can be maximized by using dual decomposition. Simulation results illustrate that the proposed iterative resource allocation algorithm converges in a small number of iterations and demonstrate the trade-off between energy efficiency and the number of transmit antennas.

In this paper, resource allocation for energy efficient communication in orthogonal frequency division multiple access (OFDMA) downlink networks with large numbers of base station (BS) antennas is studied. Assuming perfect channel state information at the transmitter (CSIT), the resource allocation algorithm design is modeled as a non-convex optimization problem for maximizing the energy efficiency of data transmission (bit/Joule delivered to the users), where the circuit power consumption and a minimum required data rate are taken into consideration. Subsequently, by exploiting the properties of fractional programming, an efficient iterative resource allocation algorithm is proposed to solve the problem. In particular, the power allocation, subcarrier allocation, and antenna allocation policies for each iteration are derived. 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 the number of antennas.

Resource allocation for orthogonal frequency-division multiple access (OFDMA) systems is a challenging issue for next-generation wireless communications. In OFDMA systems, adaptive resource allocations can considerably improve the system performance [1]. Previous research works mainly focused on OFDMA systems for unicast transmissions, wherein each subcarrier is assigned to one user exclusively [1–4]. However, many emerging wireless applications, such as mobile TV and video conference, take the form of multicast transmissions.

Non-Orthogonal Multiple Access (NOMA) is a popular solution for supporting a high number of users and along with significant bandwidth in 5G cellular communication. By using a technique called cooperative relaying, the same data is sent to all the users, and one user can relay data to the other. In order to provide enough power for the users, energy harvesting techniques have been introduced with Simultaneous Wireless Information and Power Transfer (SWIPT) coming to prominence in recent times. In this paper, analysis has been made comparing two different power allocation schemes in NOMA, Fixed Power allocation Scheme (FPS) and Dynamic Power allocation Scheme (DPS). The comparisons were made in terms of their performance and characteristics while undergoing SWIPT. It has been found that by using DPS, an almost 25% increase in peak spectral efficiency can be obtained compared to FPS. However, DPS suffers from a higher outage probability as the increase of power causes the signal bandwidth to drop below the target rate a significant number of times. Based on the detailed results, conclusions were drawn as to which power allocation coefficient scheme would be used in real-time and non-real time communication standards, respectively. The results suggest that for real-time communication, FPS is more suitable while for non-real time communication, DPS appears to work better than FPS.

In this paper, we propose a resource allocation scheme to maximize users' minimum rate with a guaranteed outage probability for Orthogonal Frequency Division Multiple Access (OFDMA) systems with imperfect CSI. To avoid the system degradation caused by the noisy and outdated CSI, we consider the minimum mean square error (MMSE) channel prediction scheme at the base station (BS). We derive the parameter, namely, the equivalent channel gain, which is determined by the requirement of the outage probability and the channel estimates at the BS. With this parameter, we can maximizes users' minimum rates under a transmit power constraint and given outage probabilities. To reduce the complexity, we propose a two-step suboptimal approach that separately performs subcarrier and power allocation. Simulation results show that the performance of the resource allocation scheme is robust against channel estimation errors and feedback delays in OFDMA systems.

Orthogonal frequency division multiplexing (OFDM)‐based orthogonal frequency division multiple access (OFDMA) has emerged as a promising transmission technology for next generation wireless systems. In a multiuser scenario, adaptive radio resource allocation can significantly improve the performance of OFDMA systems. In this article, an overview of the major state‐of‐the‐art approaches to adaptive resource allocation in the OFDMA systems is provided. Several open research issues are outlined. Copyright © 2008 John Wiley & Sons, Ltd.