Effective Capacity Analysis over Generalized Composite Fading Channels

A performance analysis of the effective capacity in two recently proposed generalized composite fading channels, namely \kappa - \mu /inverse gamma and \eta - \mu /inverse gamma composite fading channels, is conducted. To this end, accurate analytic expressions for the effective capacity are derived along with simple tight bound representations. Additionally, simple approximate expressions at the high average signal-to-noise ratio regime are also provided. The effective capacity is then analyzed for different delay constraint, multipath fading and shadowing conditions. The numerical results show that the achievable spectral efficiency lessens as the multipath fading and shadowing parameters decrease (i.e., severe multipath fading and heavy shadowing become prevalent) or the delay constraint increases. The accuracy and tightness of the proposed bounds is demonstrated and approximate representations are also provided to verify their usefulness. Furthermore, our numerical results are validated through a careful comparison with the simulated results.publishedVersionPeer reviewe

Effective Capacity Maximization With Statistical Delay and Effective Energy Efficiency Requirements

This paper presents the three-fold energy, rate and delay tradeoff in mobile multimedia fading channels. In particular, we propose a rate-efficient power allocation strategy for delay-outage limited applications with constraints on energy-per-bit consumption of the system. For this purpose, at a target delay-outage probability, the link-layer energy efficiency, referred to as effective-EE, is measured by the ratio of effective capacity (EC) and the total expenditure power, including the transmission power and the circuit power. At first, the maximum effective-EE of the channel at a target delay-outage probability is found. Then, the optimal power allocation strategy is obtained to maximize EC subject to an effective-EE constraint with the limit set at a certain ratio of the maximum achievable effective-EE of the channel. We then investigate the effect of the circuit power level on the maximum EC. Further, to set a guideline on how to choose the effective-EE limit, we obtain the transmit power level at which the rate of increasing EC (as a function of transmit power) matches a scaled rate of losing effective-EE. Analytical results show that a considerable EC-gain can be achieved with a small sacrifice in effective-EE from its maximum value. This gain increases considerably as the delay constraint becomes tight

Energy Efficiency in the Low-SNR Regime under Queueing Constraints and Channel Uncertainty

Energy efficiency of fixed-rate transmissions is studied in the presence of queueing constraints and channel uncertainty. It is assumed that neither the transmitter nor the receiver has channel side information prior to transmission. The channel coefficients are estimated at the receiver via minimum mean-square-error (MMSE) estimation with the aid of training symbols. It is further assumed that the system operates under statistical queueing constraints in the form of limitations on buffer violation probabilities. The optimal fraction of power allocated to training is identified. Spectral efficiency–bit energy tradeoff is analyzed in the low-power and wideband regimes by employing the effective capacity formulation. In particular, it is shown that the bit energy increases without bound in the low-power regime as the average power vanishes. A similar conclusion is reached in the wideband regime if the number of non-interacting subchannels grow without bound with increasing bandwidth. On the other hand, it is proven that if the number of resolvable independent paths and hence the number of non-interacting subchannels remain bounded as the available bandwidth increases, the bit energy diminishes to its minimum value in the wideband regime. For this case, expressions for the minimum bit energy and wideband slope are derived. Overall, energy costs of channel uncertainty and queueing constraints are identified, and the impact of multipath richness and sparsity is determined