A near-optimal randomized algorithm for uplink resource allocation in OFDMA systems

Traditional algorithms for dynamic OFDMA resource allocation have relatively deterministic system capacity and user fairness. Thus, in this letter, an efficient scheme is proposed to flexibly adjust quality-of-service for users, which is achieved by appropriately setting minimum data-rate of each user.

This paper investigates the radio resource allocation for the OFDMA systems. Two kinds of share auction mechanism are proposed, where the base station possesses of the subcarriers and plays as auctioneer, while the users play as bidders completing for them. These two kinds of auction are designed as first-price seal-bid, in which, the SINR auction aims at the desire for high throughput, and the fairness auction pay more attention to the fairness among users in the system. The auction-based resource allocation algorithm can reduce the burden of base station with a kind of distributed computation manner. Furthermore, the fairness auction only needs partial channel quality information with less overhead of signaling. Simulation results evaluate the performance of our proposed scheme.

We study the problem of admission control and resource (subcarriers, power and bit-loading) allocation in a heterogeneous OFDMA wireless network serving both QoS-constrained high-priority users and best-effort users. By clustering subcarriers, efficient algorithms for cluster and power allocation have been proposed. Our strategy maximizes the total network utility of the best-effort users while satisfying the QoS request for maximum number of high-priority users. We approximate best-effort user utility function as a piece-wise linear function and propose a linear programming based cluster allocation algorithm. The feasibility of the resource allocation problem depends on the number of HP users in the network. Since a large number of demanding HP users would render the resource allocation problem infeasible, a joint admission control and resource allocation scheme could be an efficient way of tackling both problems with less overhead. By incorporating admission control, we propose an efficient and optimal joint admission control and resource allocation algorithm.

In the upcoming fifth-generation (5G) wireless communication system, the massive MIMO and mmWave technologies will play a key role to improve the system performance. With the rapid development of the network communications, optimizing radio resources of wireless communication has been one of the challenging issues in 5G networks. In order to effectively improve network performance, an efficient resource allocation and power optimization algorithm is necessary. In this paper, we study joint resource allocation and power optimization (JRAPO) problem in multiuser mmWave massive MIMO system, which target at increasing energy efficiency (EE) and decreasing power consumption of systems. First, the problem of JRAPO is formulated as a multivariate mixed integer nonlinear programming problem. Second, because the non-convexity of this problem, we transformed it into a convex optimization problem by relaxing the resource block (RB) allocation indicator. Finally, we used Lagrangian dual decomposition algorithm and water-filling scheme to resolve this joint optimization problem. Simulation results demonstrated the effectiveness of the proposed algorithm.

Optimum allocation of resources is demand of every modern communication system. In this way the transmission parameters like code rate, modulation size and power are optimally chosen to enhance the overall system throughput while satisfying certain constraints at the same time. A similar constrained optimization problem is focused for OFDM environment and solved by proposed Fuzzy Rule Based System (FRBS) and Particle Swarm Optimization (PSO). In this proposal FRBS is used for adapting code rate and modulation size while PSO is used for adaptive power. Significance of the proposed scheme is shown through the simulations.

This paper studies the problem of adaptive resource allocation in OFDMA system. An ant colony algorithm based adaptive resource allocation is proposed in this paper. The problem is modeled as finding a minimum cost path in a graph. The ants' solution construction is guided by pheromone trail and heuristic information. Due to the unique heuristic searching mechanism of the ant colony algorithm, the proposed algorithm is guaranteed to converge quickly to an approximately optimal solution. Simulation results showed that the performance of the proposed algorithm is close to the optimal solution, and gains 1-2 dB in comparison with the fixed allocation scheme.

In this paper subcarrier and power allocation to each user at base-station maximizes the user data rates, subject to constraints on total power and bit error rate. First, each subchannel is assigned to the user with best channel-to-noise ratio for the channel, with random power distributed by water filling algorithm. The Gao's (2006) subcarrier allocation algorithm was used to calculate the power for each population in of genetic algorithm model. With the goal of minimize the overall transmit power while ensuring the fulfillment of each user's data rate and bit error rate (BER), the needed allocation is proposed through genetic search. The proposed genetic search helps fast convergence and can handle large allocations of subcarriers to users without performance degradation. The simulation results show that genetic algorithm approach will be used where complex computations are involved and near optimal solution are acceptable for optimum resource allocation

Orthogonal Frequency Division Multiplexing (OFDM) is a promising technology for high data rate transmission in wideband wireless systems for achieving high downlink capacities in future cellular systems. In this paper subcarrier and power allocation to each user at base-station is allocated to maximize the user data rates, subject to constraints on total power and bit error rate. First, each sub-channel is assigned to the user with best channel-to-noise ratio for the channel, with random power distributed by water filling algorithm. With the goal of minimize the overall transmit power while ensuring the fulfillment of each user’s data rate and bit Error Rate (BER), the needed allocation is proposed through genetic search. The proposed genetic search helps fast convergence and can handle large allocations of subcarriers to users without performance degradation. The simulation results show that genetic algorithm approach will be used where complex computations are involved and near optimal solution are acceptable for optimum resource allocation.

We study the performance of generalized cμ-rule implementation in OFDMA systems, namely bit-based subcarrier allocation and packet-based subchannel allocation. While cμ-rule is asymptotically optimum in continuous-time server allocation, it needs to be adjusted when applied to discrete-time server allocation in OFDMA systems. Bit-based allocation considers the allocation through bit-based connectivity. Resource wastage is prevented by greedy reassignment in low traffic regime. Packet-based allocation adopts cμ-rule and allocates the servers through multiple-packet rates. The allocation is ordered from the server with lowest connectivity to the server with highest connectivity. Simulation results show that bit-based allocation outperforms packet-based allocation in queue length minimization for small number of users. With increasing number of users, the performance difference becomes smaller and at the certain limit, the packet-based allocation performs better. Furthermore, the packet-based allocation might be better in practical realization due to the lower complexity.

Demand on multimedia content by consumers' handheld devices over wireless channels is on the increase. In view of the accelerated trend towards consumption of high quality video, power utilization by mobile devices is expected to excessively increase. Hence, it becomes equally important to advance more efficient power minimization techniques, in light of the short battery life in portable devices. However, power minimization algorithms that adopt consumers' perceptual quality of video have not received adequate research. This paper proposes a joint optimization of energy and quality requirements in a multiuser orthogonal frequencydivision multiplexing environment. A multi-objective optimization problem is formulated with the aim to identify bitrate allocations among users such that total power is minimized, and average quality is maximized. For this, a content-aware and energy-efficient resource allocation scheme (CaERAS) is proposed based on genetic and greedy algorithms. Simulation results show that CaERAS, as a lowcomplexity scheme, outperforms comparable methods in terms of efficiency and selectivity of suboptimal solutions. It is shown to acquire a suboptimal solution in as low as 0.0025 of the search space in previous methods. Also, a significant average saving of 85.66% in required energy is observed in broadcast transmission as opposed to unicast transmission.

A new concept-traffic diversity is proposed for resource allocation of orthogonal frequency division multiple access system. With traffic diversity, the transmitter designates different bit error rate (BER) level for each kind of traffic. The transmission data rate corresponds to the BER level. At the receiver, this separated traffic, which belong to the same user, will be aggregated. Simulation results show that traffic diversity can increase the throughput by 27%.

It is proposed that resource allocation algorithm considers rate proportion among the multicast groups in multicast orthogonal frequency division multiplexing (OFDM) downlink systems in this paper. However, it is minor significance to enforce the rate proportion strictly in actual mobile communication system. To solve this problem, the resource allocation algorithm is proposed to satisfy the rate proportion approximately in this text, including sub carrier allocation algorithm and power allocation algorithm. The former is achieved by modifying the existing unicast sub carrier assignment algorithm, the latter is Water-filling algorithm, which is capable of maximize the capacity when sub carrier assignment is determined. Simulation results show that compared to conventional algorithms, the proposed resource allocation algorithm has a good performance in capacity as well as rate proportion, which is a realistic method to solve the resource allocation in actual mobile communication system. In addition, we also discussed the capacity has a close relation with the number of multicast group.

The progressive adaptability of today’s mobile communication services and the prompt progressions in the mobile phones industry have steered to a remarkable development in the wireless market capacity. Notwithstanding the incessant advancement in the radio access technologies to manage with that growth. However, many remaining challenges, i.e., inefficient allocation and management of wireless network resources in Heterogeneous Wireless Networks (HWNs) when using the modern cellular mobile communications radio access technologies such as Wireless Local Area Network (WLAN)/Wi-Fi, Long-TERA Evolution (LTE) and Fifth Generation (5G)) still continue and needs to be addressed. This problem emanates from the enormous number of users sharing these resources, random distribution of wireless channels, scarceness of the wireless spectral resources, and the dynamic behavior of generated traffic. Therefore, this paper solves the identified problem by designing an Efficient Resource Allocation (ERA) algorithm to address the inefficient allocation of available resources versus quality of service (QoS) challenges. The obtained simulation results demonstrate the effectiveness of the proposed model to meet the objectives subject to limitations and constraints compared to other published works. On average, the proposed ERA algorithm, Joint Power Bandwidth Allocation (JPBA) algorithm, and Dynamic Bandwidth Allocation (DBA) algorithm have shown 98.9%, 85%, 81% throughput performance, respectively at a time interval of 100s.

Multiple-input multiple-output (MIMO) wireless communication systems in combination with orthogonal frequency division multiple access and space division multiple access are flexible, spectrally efficient and of high capacity. These systems can allocate frequency, time, and space resources adaptively among mobile stations. Optimal resource allocation (RA) for maximizing the sum rate is usually too complex for practical applications, hence suboptimal algorithms are required. The performance of RA algorithms are degraded due to imperfections in channel state information at transmitter (CSIT). Thus, techniques that make the RA algorithm, robust against CSIT errors are favorable. In this paper, two novel suboptimal grouping algorithms are introduced that are shown to achieve better sum rate-complexity and fairness-complexity performances. A novel index is proposed to measure the efficiency of RA algorithms, which takes into account sum rate and complexity simultaneously. Moreover, the effect of erroneous CSIT on sum rate is investigated and it is shown that performance of the proposed RA algorithms with erroneous CSIT are superior to the existing schemes. Finally, fairness performance of the proposed RA algorithms are evaluated, which are shown to be the same if not superior to other existing algorithms.

In wireless communication system, the available radio resource is limited and scarce, and hence, how to use the limited resource to satisfy various service quality requirements and realize the fairness between different users is a key problem. This paper gives an overview for the existing resource allocation algorithms in OFDM systems from the aspects of single-user and multi-user, respectively. Moreover, existing methods to solve the resource allocation problem are classified, and future development is analyzed as well.