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

Abstract

Despite the advent of fifth-generation mobile networks in the next years, the Long-Term Evolution system still have an important role in the mobile communication scenario. Moreover, the development of solutions that fulfill the demand of new multimedia applications as well as provide an efficient use of the energy resources is one of the drivers of future networks. Therefore, in this article, we deal with the total energy efficiency maximization problem subject to minimum satisfaction constraints per service and quality of service in the uplink of Long-Term Evolution using single-carrier frequency-division multiple access. Although the formulated problem is a nonlinear combinatorial optimization problem, we obtain the optimal solution through algebraic manipulations and by using an iterative method. Motivated by the high computational complexity to obtain the optimal solution, we propose a heuristic solution with lower computational complexity. Realistic system-level simulations indicate that the proposed solution presents good performance regarding both outage rate and energy efficiency.Trans Emerging Tel Tech. 2019;30:e3674.wileyonlinelibrary.com/journal/ett

A Semi-Distributed Approach for Uplink Max-Min Energy Efficiency Optimization with Minimum User Satisfaction and Adjacency Constraints

“…In addition, we employ the Minimum Mean Square Error (MMSE) equalizer to combat Inter Symbol Interference (ISI). Thus, the effective SNR perceived by the receiver when data is transmitted by UE on the RBs that belong to assignment pattern , MMSE , , is [8], [9] MMSE ,…”

“…As in [8], [9], we consider summations, multiplications and comparisons as the most relevant and time-consuming operations. Therefore, the centralized optimal solutions have exponential worst-case computational complexity given by O ( 2+ ), see [8] and [9] for more details. Regarding the proposed semi-distributed solution, the most computational complex operations consist in finding the UE in line 3 and the assignment pattern in line 6.…”

“…We define per-service satisfaction as a minimum percentage (or number) of user equipments (UEs) of each service that should have their QoS fulfilled. The per-service satisfaction concept was considered in [8] and [9]. In [8] the constrained power minimization (CPM) problem was considered to minimize the total power consumption while in [9] the constrained energy efficiency maximization (CEEM) problem was studied in order to maximize the overall EE.…”

“…The per-service satisfaction concept was considered in [8] and [9]. In [8] the constrained power minimization (CPM) problem was considered to minimize the total power consumption while in [9] the constrained energy efficiency maximization (CEEM) problem was studied in order to maximize the overall EE. However, works [8] and [9] only considered centralized solutions.…”

“…In [8] the constrained power minimization (CPM) problem was considered to minimize the total power consumption while in [9] the constrained energy efficiency maximization (CEEM) problem was studied in order to maximize the overall EE. However, works [8] and [9] only considered centralized solutions. Indeed, centralized solutions require global channel state information (CSI) availability at a central controlling unit, which may be difficult to acquire in modern networks due to the amount of involved network nodes.…”