Joint Resource Block Assignment and Power Allocation Problem for Rate Maximization With QoS Guarantees in Multiservice Wireless

Abstract

Abstract-We formulate the resource and power assignment problem of maximizing the spectral efficiency of a wireless system subject to user satisfaction constraints in the multiservice scenario. We show that although this optimization problem is nonlinear, it can be converted to an integer linear program. In this way, standard techniques can be used to obtain the optimal solution. Motivated by the high computational complexity of the optimal solution, we propose a fast suboptimal algorithm. Simulation results show that our proposal achieves near-optimal performance in low and medium loads with a much lower computational complexity compared with the algorithm used to obtain the optimal solution. Therefore, our proposed algorithm achieves a good tradeoff between performance and computational complexity. We also show that the addition of adaptive power allocation renders significant performance gains in the considered scenario.Index Terms-Multiservice, quality of service (QoS), rate maximization, resource and power assignment.

Maximizing energy efficiency in SC‐FDMA uplink with QoS guarantees and user satisfaction

“…Considering the previous example, the system operator can determine that the number of satisfied UEs in VoIP calls is greater than the number of satisfied UE that upload personal files to the server in the cloud. The per‐service minimum satisfaction concept was proposed as a system‐level metric in the work of Furuskär and used in many other works in downlink OFDMA. In the work of Lima et al two distinct RRA problems in uplink SC‐FDMA in a multiservice scenario were proposed: the constrained rate maximization (CRM) and unconstrained rate maximization problems that consist in maximizing the total data rate with and without per‐service minimum satisfaction requirements, respectively.…”

Resource Allocation for Energy Efficiency and QoS Provisioning

“…However, only frequency resource assignment is optimized while the transmit power is equally divided among frequency resources. In [13], the RRA problem of [12] is extended to a more challenging setting with the simultaneous optimization of transmit power and frequency resource assignment. Nonetheless, the proposed transmit power optimization in [13] aims at maximizing the spectral efficiency and not EE.…”

“…5, we assign some or all the remaining RBs while applying an adaptive transmit power allocation among RBs in order to satisfy minimally the system QoS requirements, i.e., using the lowest possible transmit power to meet them. Note that, parts 1 and 2 are also present as part of the suboptimal solution for the Joint RB Assignment and Power Allocation Problem (JRAPAP) for Rate Maximization with QoS constraints in our previous work [13]. Part 3 branches into two different algorithms, as it will be explained in Sections III-B1 and III-B2.…”

Improved Joint Resource and Power Allocation Algorithm with QoS Provisioning

“…Note that none of the previous works presented so far in this section have addressed multi-service scenarios with satisfaction guarantees. In [5], we extended the problem of [6] to evaluate the performance gains that can be achieved with the joint optimization of adaptive power allocation and frequency resource assignment, and a low complexity suboptimal algorithm was proposed. Although the solution from [5] achieves good performances, two limitations are present in the algorithm: 1) the use of estimated throughput to determine the priority between User Equipments (UEs); and 2) infeasible solutions are not treated.…”

“…In [5], we extended the problem of [6] to evaluate the performance gains that can be achieved with the joint optimization of adaptive power allocation and frequency resource assignment, and a low complexity suboptimal algorithm was proposed. Although the solution from [5] achieves good performances, two limitations are present in the algorithm: 1) the use of estimated throughput to determine the priority between User Equipments (UEs); and 2) infeasible solutions are not treated. Therefore, in this paper we propose two improvements in the algorithm proposed in [5], allowing us to deal with infeasible solutions and achieve a better performance.…”