Effective Energy Efficiency of Ultrareliable Low-Latency Communication

Abstract

Effective Capacity defines the maximum communication rate subject to a specific delay constraint, while effective energy efficiency (EEE) indicates the ratio between effective capacity and power consumption. We analyze the EEE of ultrareliable networks operating in the finite blocklength regime. We obtain a closed form approximation for the EEE in quasi-static Nakagami-(and Rayleigh as sub-case) fading channels as a function of power, error probability, and latency. Furthermore, we characterize the QoS constrained EEE maximization problem for different power consumption models, which shows a significant difference between finite and infinite blocklength coding with respect to EEE and optimal power allocation strategy. As asserted in the literature, achieving ultra-reliability using one transmission consumes huge amount of power, which is not applicable for energy limited IoT devices. In this context, accounting for empty buffer probability in machine type communication (MTC) and extending the maximum delay tolerance jointly enhances the EEE and allows for adaptive retransmission of faulty packets. Our analysis reveals that obtaining the optimum error probability for each transmission by minimizing the non-empty buffer probability approaches EEE optimality, while being analytically tractable via Dinkelbach's algorithm. Furthermore, the results illustrate the power saving and the significant EEE gain attained by applying adaptive retransmission protocols, while sacrificing a limited increase in latency.

Effective Energy Efficiency and Statistical QoS Provisioning Under Markovian Arrivals and Finite Blocklength Regime

“…This model gives a more realistic estimation of effective capacity due to assuming the probability of emptying the buffer during transmission with constant arrival rate. Recently, in [21] authors proved that this power consumption model is valid for finite blocklength regime. The authors [28] proposed a EEE model based on empty and non-empty buffer for large packets transmission.…”

“…In the literature, there are two energy efficiency models that attracted extensive attention for cellular networks. The first is based on network capacity per unit energy consumption, and the second is effective capacity per unit energy consumption model [20], [21]. The network capacity describes a theoretical performance of upper bound throughput, which does not consider delay QoS requirements in practical applications.…”

“…The energy efficiency issue in mMTC was studied in [23], where the authors investigated an analytical framework for energy efficiency that can be defined as the ratio of achievable rate to energy consumption, that comprises radiator and static circuit powers. In [21], authors evaluated the effective energy efficiency (EEE) for delay-sensitive networks in the finite blocklength regime and propose an optimum power allocation strategy. Results confirm that Shannon's model underestimates the optimum power when compared to the exact finite blocklength model.…”

Interference-Limited Ultra-Reliable and Low-Latency Communications: Graph Neural Networks or Stochastic Geometry?

“…The authors in [9] optimized the power allocation and the delay exponent to maximize the effective energy efficiency under the delay and reliability constraints. The works in [10] and [11] considered a joint power allocation and transmission blocklength optimization to minimize the decoding error probability with maximum delay and total power constraints.…”

“…In each time slot, the i-th (i ∈ K, i = k) transmitter sends a packet with the probability of λ {τ } i M{τ} i . Finally, we count the number of transmissions that the sum of the decoding error probability and the queuing delay violation probability is larger than the reliability requirement of the k-th user, ǫ max k , to get the P k defined in (9) and evaluate the QoS violation probability in (13). The value of the loss function f loss (.)…”