Resource Allocation in Multicast OFDM System with Approximate Rate Proportion

In conventional multicast scheme (CMS), the total throughput of multicast group is constrained by the user with the worst channel quality. In order to overcome this problem of limited throughput, the authors introduce a resource allocation algorithm by exploiting layered coding combined with erasure correction coding for multicast services in the downlink of orthogonal frequency-division multiple access-based multi-antenna system. To reduce the feedback overhead of uplink, the authors design a novel transmission scheme with limited feedback. Then, the joint subcarrier and power allocation problem for the data of base layer and enhancement layers are formulated, which is shown to be non-deterministic polynomial- hard. Hence, in order to reduce the computational complexity, they propose a three-phase suboptimal algorithm. The algorithm is designed to maximise the system throughput, whereas at the same time guarantee the quality of services (QoS) requirements of all multicast groups. It is composed of precoding scheme, proportional fairness subcarrier allocation algorithm and modified water-filling power allocation algorithm with QoS guarantees (MWF-Q). To further decrease the complexity of MWF-Q, a power allocation algorithm with increased fixed power allocation algorithm with QoS guarantees is introduced. Simulation results show that the proposed algorithms based on limited feedback scheme significantly outperform CMS and any other existing algorithm with full feedback. Moreover, the proposed scheme can efficiently reduce 50% of the full feedback overhead.

This paper considers the network resource allocation problem in multi-service MEC networks, and in particular considers joint spectrum and computing resource allocation in an edge network service system. The resource allocation problem under consideration is formulated as an optimization problem, which takes into account users’ minimum service requirements in terms of the service delay, transmission rate, and computation rate, with an objective to minimize the average service delay of a user’s service request. To solve the problem, we propose a deep-Q-network (DQN) based joint spectrum and computing resource allocation algorithm, which attempts to optimize the resource allocation for each service request by learning a more efficient allocation scheme so as to better adapt to dynamic traffic arrivals, diverse service requirements, and complex environmental conditions. Simulation results show that the proposed resource allocation algorithm can efficiently reduce the average service delay compared with two benchmark algorithms.

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 this paper, single- and multi- user Resource Allocation (RA) optimization problems considering transmit power and minimum rate constraint of Mobile Station (MS) for maximizing MS' energy efficiency, measured as bits-per-joule (bpj), are addressed. Assume channel state information of all MSs is known by base station. We propose uplink RA algorithms, performing subcarrier assignment and power allocation, for optimizing bpj of MS in a single-cell OFDMA-based cellular network for both single- and multi- user scenarios. In the single-user case, we propose RA algorithms, which utilize the closed-form solution derived by applying Lambert-W function and an iterative approach based on Karush---Kuhn---Tucker conditions respectively to achieve optimal bpj of MS. In the multi-user case, centralized iterative multi-user RA algorithms for maximizing sum of MS' bpj, performing joint subcarrier assignment and power allocation iteratively, are proposed by utilizing the proposed single-user RA schemes. In particular, tradeoffs between energy efficiency and spectral efficiency are fully investigated, and the influence of MS' power and minimum rate constraints on bpj performance is also studied. The effectiveness of proposed algorithms is presented by numerical experiments. Numerical results demonstrate the proposed algorithms can enhance bpj significantly with limited loss of total throughput compared to the sum-rate maximization algorithm (in Moretti et al., IEEE Trans Veh Technol 60(4):1788---1798, 2011).

This paper develops a sum-power minimized resource allocation (RA) algorithm subject to a sum-rate constraint for cooperative orthogonal frequency division modulation (OFDM) transmission with subcarrier-pair based opportunistic decode-and-forward (DF) relaying. The improved DF protocol first proposed in [1] is used with optimized subcarrier pairing. Instrumental to the RA algorithm design is appropriate definition of variables to represent source/relay power allocation, subcarrier pairing and transmission-mode selection elegantly, so that after continuous relaxation, the dual method and the Hungarian algorithm can be used to find an (at least approximately) optimum RA with polynomial complexity. Moreover, the bisection method is used to speed up the search of the optimum Lagrange multiplier for the dual method. Numerical results are shown to illustrate the power-reduction benefit of the improved DF protocol with optimized subcarrier pairing.

This paper presents an efficient uplink resource algorithm in multicarrier based cognitive radio systems. The algorithm allocates subcarriers among the different users considering their channel quality, the amount of interference that they may introduce to the primary system and the increment in the overall data rate. Afterwards, an efficient power allocation algorithm is used to distribute the per-user available power among the different subcarriers. The interference to the primary system is guaranteed to be under the prescribed interference temperature limit. Simulation results illustrate that the proposed resource allocation algorithm with low computational complexity improves the total data rate and achieves a very good performance.

In this paper, we investigate a fifth generation cellular vehicle-to-everything based platoon system and propose low-complexity dynamic manager selection and resource allocation algorithms in the finite block length regime, to meet the ultra reliable and low latency communication requirements of intra-platoon safety-related data transmission. A closed-form expression for the platoon manager index is derived, based on which a low-complexity dynamic platoon manager selection algorithm is proposed, enabling a significant communication performance enhancement over the conventional head vehicle platoon manager algorithm. This is also the first work to investigate the impact of finite block length on platoon communication. The optimal finite block length coding rate under automatic repeat request retransmission is obtained. A subchannel multiplexing criterion is proposed and closed-form expressions for the optimal transmission powers of the platoon manager and its subchannel multiplexing partner are derived. Based on the theoretical findings, a joint resource allocation and coding rate optimization algorithm is proposed, achieving a near-optimal performance in terms of groupcast latency with a significant complexity reduction over exhaustive search.

Orthogonal frequency division multiplexing (OFDM) is an optimal technique for high-speed transmission. In this paper, a multicell multiuser OFDM system with adaptive subcarrier allocation and adaptive modulation is considered. We propose an adaptive subcarrier, bit and power allocation algorithm to maximum the total throughput of the multicell system in the presence of cochannel interference (CCI), frequency selective Rayleigh fading and additive white Gaussian noise (AWGN). Its performance in a multicell system with different number of users is investigated through simulation. It is shown from the simulation that this adaptive allocation algorithm enhances the throughput of OFDM systems as compared to equal power allocation and is suitable for multiuser system.

Heterogeneous network (HetNet) deployment is considered a de facto solution for meeting the ever increasing mobile traffic demand. However, excessive power usage in such networks is a critical issue, particularly for mobile operators. Characterizing the fundamental energy efficiency (EE) performance of HetNets is therefore important for the design of green wireless systems. In this paper, we address the EE optimization problem for downlink two-tier HetNets comprised of a single macro-cell and multiple pico-cells. Considering a heterogeneous real-time and non-real-time traffic, transmit beamforming design and power allocation policies are jointly considered in order to optimize the system energy efficiency. The EE resource allocation problem under consideration is a mixed combinatorial and non-convex optimization problem, which is extremely difficult to solve. In order to reduce the computational complexity, we decompose the original problem with multiple inequality constraints into multiple optimization problems with single inequality constraint. For the latter problem, a two-layer resource allocation algorithm is proposed based on the quasiconcavity property of EE. Simulation results confirm the theoretical findings and demonstrate that the proposed resource allocation algorithm can efficiently approach the optimal EE.

With the rapid advancement of wireless network technologies and the constant increase in the number of mobile devices, enormous amount of data is generated from a set of user equipments (UEs), such as mobile phones and wearable devices. How to power these devices wirelessly becomes a challenging issue. Meanwhile, data and energy integrated communication networks (DEINs) are emerging as a new type of wireless networks emphasizing on the cooperation between wireless information and energy transfer. This means that the DEINs can be a solution to tackle with powering UEs wirelessly and further achieving a higher data rate. However, there are no researches that have been studied about how to obtain the optimal scheme in DEINs under the consideration of data traffic. Hence, we propose a fair energy-efficient resource allocation algorithm for DEINs in this paper. Our objective is to optimize the energy efficiency with the consideration of providing fair queuing for data traffic by minimizing the queue length difference among the UEs' buffers. Besides, when designing the downlink energy beamforming and the downlink-uplink power-and-time allocation scheme, power sensitivity of conversion circuits and system stability are taken into account. Simulation results illustrate that the proposed resource allocation algorithm does not only improve the fairness among UEs by queue balancing, but also achieve close-to-optimal system energy efficiency by the beamforming design and resource allocation.

With the rapid increasing of maritime activities, maritime wireless networks (MWNs) with high reliability, high energy efficiency, and low delay are required. However, the centralized networking with fixed resource scheduling is not suitable for MWNs due to the special environment. In this paper, we introduce the collaborative relay communication in distributed MWNs to improve the link reliability, and propose an orthogonal time-frequency resource block reservation based multiple access (RRMA) scheme for both one-hop direct link and two-hop collaborative relay link to reduce the interference. To further improve the network performance, we formulate an energy efficiency (EE) maximization resource allocation problem and solve it by an iterative algorithm based on the Dinkelbach method. Finally, numerical results are provided to investigate the proposed RRMA scheme and resource allocation algorithm, showing that the low outage probability and transmission delay can be attained by the proposed RRMA scheme. Moreover, the proposed resource allocation algorithm is capable of achieving high EE in distributed MWNs.

In this letter, we investigate the adaptive resource allocation with Lyapunov optimization for time-varying wireless networks. The transmit power minimization is characterized by a stochastic optimization model. A dynamic resource allocation (DRA) algorithm is developed to accommodate the wireless network dynamics, i.e., time-varying wireless channels and as well the queuing dynamics. We investigate the tracking error between the DRA algorithm output and the target optimal resource allocation solution. Based on these results, we further develop an adaptive-compensation resource allocation (ACRA) algorithm, which iterates only once when the network state changes for saving the huge iteration overheads. Finally, we determine a sufficient condition that the ACRA algorithm asymptotically tracks the moving equilibrium point with no tracking errors. Simulation results validate the theoretical analysis of our proposed scheme.

Researchers are yet to entirely mapped out the difficulty in allocating optimal resources to mobile Worldwide Interoperability for Microwave Access (WiMAX) subscribers. This research presents an optimal scheduling algorithm for WiMAX resource allocation based on an Particle Swarm Optimization (PSO). In this work, sub-group creation is used to offer a PSO-based technique for allocating subcarriers and Orthogonal Frequency Division Multiplexing (OFDM) symbols to mobile WiMAX customers. The WiMAX network environment is organized into seven layers, with seven different modulation and coding algorithms proposed for sending packets to subscribers within each layer. By adopting an improved PSO-based WiMAX resource allocation method, an enhanced model for throughput maximization and channel data rate was implemented. The Aggregate Data Rate (ADR) and Channel Data Rate (CDR) for each scenario were obtained by simulating several scenarios of WiMAX multicast service to mobile users. Based on the performance evaluation of the enhanced algorithm for ADR and CDR, the results for the various layers and uniform distribution of users over the full layers were 350Mbps, 525Mbps, 700Mbps, 1050Mbps, 1050Mbps, 1400Mbps, 1575Mbps, and 1398Mbps. 6.98Mbps, 10.48Mbps, 13.97Mbps, 20.95Mbps, 20.95Mbps, 27.94Mbps, 31.5Mbps, and 28Mbps were also achieved for CDR. The significance of optimal resource allocation is to achieved a maximum ADR and CDR. The results showed a fair distribution of resources within the coverage area of the network.

We present in this paper a novel proactive interference avoidance scheme for mobile-to-mobile (M2M) communication as an underlay in Long-Term Evolution (LTE) downlink networks. The proposed scheme introduces an intelligent user-selective resource allocation algorithm that aims to proactively avoid interference between M2M pairs and LTE regular users while achieving a satisfactory link budget for both M2M and LTE regular links. The proposed resource allocation algorithm is based on a novel low-complexity interference estimation approach that requires only channel quality indicator (CQI) information (CSI). Since the CQI reporting mechanism has been already implemented in current LTE systems, the proposed algorithm is indeed practical and standard compatible, compared with the existing works that require perfect knowledge of the channel state information (CSI). In this research, achievable data throughput with LTE-compliant adaptive modulation and coding has been adopted as the optimization measurement, rather than the information-theoretic capacity. Therefore, the performance of the proposed scheme is practically realizable in LTE communication systems. Since the performance of the proposed scheme highly relies on the accuracy of CQI feedback, we also carry out performance analysis to evaluate the impact of CQI estimation error and delay. The simulation results show that the total achievable data throughput for LTE networks is dramatically enhanced by M2M communication with this proposed interference avoidance resource allocation scheme.

Multi-User Orthogonal Frequency Division Multiplexing (MU-OFDM) is an efficient technique for achieving high downlink capacity in high-speed communication systems. A key issue in MU-OFDM is the allocation of the OFDM subcarriers and power to users sharing the channel. In this paper a proportional rate-adaptive resource allocation algorithm for MU-OFDM is presented. Subcarrier and power allocation are carried out sequentially to reduce the complexity. The low complexity proportional subcarriers allocation is followed by Greedy Power Allocation (GPA) to solve the rate-adaptive resource allocation problem with proportional rate constraints for MU-OFDM systems. It improves the work of Wong et al. in this area by introducing an optimal GPA that achieves approximate rate proportionality, while maximizing the total sum-rate capacity of MU-OFDM. It is shown through simulation that the proposed GPA algorithm performs better than the algorithm of Wong et al., by achieving higher total capacities with the same computational complexity, especially, at larger number of users and roughly satisfying user rate proportionality.