Effective Energy Efficiency and Statistical QoS Provisioning under Markovian Arrivals and Finite Blocklength Regime
In this paper we evaluate the Effective Energy Efficiency (EEE) and propose delay-outage aware resource allocation strategies for energy-limited IoT (Internet of Things) devices under the finite blocklength (FBL) regime. The EEE is a cross-layer model measured by the ratio of Effective Capacity to the total consumed power. To maximize the EEE there is a need to optimize transmission parameters such as transmission power and rate efficiently. Whereas it is quite complex to study the impact of transmission power or rate alone the complexity is aggravated by the simultaneous consideration of both variables. Hence we formulate power allocation (PA) and rate allocation (RA) optimization problems individually and jointly to maximize EEE. Furthermore we investigate the performance of the EEE under constant and random arrivals where statistical QoS constraints are imposed on buffer overflow probability. Using effective bandwidth and effective capacity theories we determine the arrival rate and the required service rate that satisfy the QoS constraints. After that we compare the performance of different iterative algorithms such as Dinkelbach’s and Cross Entropy which guarantee the convergence for the optimal solution. By numerical analysis the influence of source characteristics fixed transmission rate error probability coding blocklength and QoS constraints on the throughput are identified. Our analysis reveals that the joint PA and RA is the optimal resources allocation strategy for maximizing the EEE in the presence of constant and random data arrivals. Finally the results illustrate that the modified Dinkelbach’s algorithm has high performance and low complexity compared to others.