Interference Networks with Random User Activity and Heterogeneous Delay Constraints

Autor: Nikbakht, Homa, Wigger, Michèle, Shamai, Shlomo, Gorce, Jean-Marie, Poor, H. Vincent
Rok vydání: 2023
Předmět:
Druh dokumentu: Working Paper
Popis: To answer the call for a new theoretical framework to simultaneously accommodate random user activity and heterogeneous delay traffic in Internet of Things (IoT) systems, in this paper we propose coding schemes and information-theoretic converse results for the transmission of heterogeneous delay traffic over interference networks with random user activity and random data arrivals. The heterogeneous traffic is composed of delay-tolerant traffic and delay-sensitive traffic where only the former can benefit from transmitter and receiver cooperation since the latter is subject to stringent decoding delays. The total number of cooperation rounds at transmitter and receiver sides is limited to $\D$ rounds. Each transmitter is active with probability $\rho \in [0,1]$. We consider two different models for the arrival of the mixed-delay traffic: in Model~$1$, each active transmitter sends a delay-tolerant message, and with probability $\rho_f \in [0,1]$ also transmits an additional delay-sensitive message; in Model~$2$, each active transmitter sends either a delay-sensitive message with probability $\rho_f$ or a delay-tolerant message with probability $1-\rho_f$. We derive inner and outer bounds on the fundamental per-user multiplexing gain (MG) region of the symmetric Wyner network as well as inner bounds on the fundamental MG region of the hexagonal model. Our inner and outer bounds are generally very close and coincide in special cases. They also show that when both transmitters and receivers can cooperate, then under Model~$1$, transmitting delay-sensitive messages hardly causes any penalty on the sum per-user MG, and under Model~$2$, operating at large delay-sensitive per-user MGs incurs no penalty on the delay-tolerant per-user MG and thus increases the sum per-user MG.
Databáze: arXiv