Energy Efficient OFDMA Networks Maintaining Statistical QoS Guarantees for Delay-Sensitive Traffic

Abstract

An energy-efficient design is proposed under specific statistical quality-of-service (QoS) guarantees for delay-sensitive traffic in the downlink orthogonal frequency-division multiple-access networks. This design is based on Wu's effective capacity (EC) concept, which characterizes the maximum throughput of a system subject to statistical delay-QoS requirements at the data-link layer. In the particular context considered, our main contributions consist of quantifying the effective energy-efficiency (EEE)versus-EC tradeoff and characterizing the delay-sensitive traffic as a function of the QoS-exponent θ, which expresses the exponential decay rate of the delay-QoS violation probabilities. Upon exploiting the properties of fractional programming, the originally quasi-concave EEE optimization problem having a fractional form is transformed into a subtractive optimization problem by applying Dinkelbach's method. As a result, an iterative inner-outer loop-based resource allocation algorithm is conceived for efficiently solving the transformed EEE optimization problem. Our simulation results demonstrate that the proposed scheme converges within a few Dinkelbach algorithm's iterations to the desired solution accuracy. Furthermore, the impact of the circuitry power, the QoS-exponent, and the power amplifier inefficiency is characterized numerically. These results reveal that the optimally allocated power maximizing the EEE decays exponentially with respect to both the circuitry power and the QoS-exponent, while decaying linearly with respect to the power amplifier inefficiency.INDEX TERMS 5G, effective energy-efficiency (EEE), statistical quality-of-service (QoS), delay-sensitive traffic, Dinkelbach's method, effective capacity, orthogonal frequency-division multiple-access (OFDMA).

Energy-Spectral-Efficiency Analysis and Optimization of Heterogeneous Cellular Networks: A Large-Scale User-Behavior Perspective

“…A number of existing GR techniques focused on the energyefficient physical layer transceiver design and radio resource allocation optimization [8]- [12], which however did not take into account the network's traffic characteristics. Diverse BS energy saving methods relying on dynamic operations and on BS deployment strategies were investigated in [5]- [7], [15]- [17].…”

“…where 0 < ε out < 1 is the outage probability threshold in tier mac and η ESE is given by (12). First, we explain why only the macro-tier outage constraint has to be considered.…”

“…Thus, the explosively increasing mobile traffic can still be seamlessly conveyed, whilst satisfying high quality-of-service (QoS) requirements. On the other hand, driven both by economical and environmental concerns, network designers have to pay increasing attention to power-efficient 'green radio' (GR) techniques [3]- [12] in order to curb the rapidly escalating energy consumption of wireless communication networks. Both the 3GPP Long Term Evolution (LTE) and LTE-Advanced standards have specified energy efficiency (EE) as an important system performance indicator in addition to the enhanced QoS guarantees and to the dramatic boost of service data rates [13].…”

Resource and energy efficient device to device communications in downlink cellular system

“…In order to obtain the optimal ω k,n and p k,n for this nonconvex problem, exhaustive search with very high complexity is required. By relaxing the integer variables, ω k,n ∈ {0, 1} into continuous variables [10], ω k,n ∈ [0, 1], problem (13) can be written as…”

Joint Energy-Spectral-Efficiency Optimization of CoMP and BS Deployment in Dense Large-Scale Cellular Networks

“…An energy-efficient carrier aggregation scheme is considered in [17], while large-scale MIMO based systems are discussed in [18]. Similarly, the designs of [19]- [21] are either for singlecell or fixed networks. However, these existing algorithms are only based on local system performance metrics, but ignore large-scale mobile-traffic variations, such as the temporal and geographic fluctuations of the users' behaviors.…”