Delay Analysis of Random Scheduling and Round Robin in Small Cell Networks

Abstract

We analyze the delay performance of small cell networks operating under random scheduling (RS) and round robin (RR) protocols. Based on stochastic geometry and queuing theory, we derive accurate and tractable expressions for the distribution of mean delay, which accounts for the impact of random traffic arrivals, queuing interactions, and failed packet retransmissions. Our analysis asserts that RR outperforms RS in terms of mean delay, regardless of traffic statistic. Moreover, the gain from RR is more pronounced in the presence of heavy traffic, which confirms the importance of accounting fairness in the design of scheduling policy. We also find that constrained on the same delay outage probability, RR is able to support more user equipments (UEs) than that of RS, demonstrating it as an appropriate candidate for the traffic scheduling policy of internet-of-things (IoT) network.

Scheduling Policies for Federated Learning in Wireless Networks

“…Sample i ∈ D k uniformly at random, and update the local parameter w t k as follows Send parameter w t k to the AP 10: end for 11: The AP collects all the parameters {w t k } K k=1 , and updates w t+1 = 1 n K k=1 n k w t k 12: end for 13: Output: w T resource-limited radio channels to the appropriate UEs. In the following, we denote by G = K/N the ratio of the number of UEs to the number of subchannels 3 and consider three practical policies as our scheduling criteria [28], [29]: (a) Random Scheduling (RS): In each communication round, the AP uniformly selects the N associated UEs at random for parameter update, each selected UE is assigned a dedicated subchannel to transmit the trained parameter.…”

Optimizing Information Freshness in Wireless Networks: A Stochastic Geometry Approach

“…Remark 1: From an engineering point of view, our system model is motivated by the emerging interest in applications like Device-to-Device (D2D) networking, mobile crowd sourcing, and Internet-of-Things (IoT), which do not require a centralized infrastructure, e.g., bases stations or access points, to conduct communications. Note that the framework can be extended to consider other channel models such as the multiple access channels (multiple transmitters and one receiver) [24] or broadcast channels (one transmitter and multiple receivers) [27], or even scenarios with multi-hop transmissions [33].…”

Locally Adaptive Scheduling Policy for Optimizing Information Freshness in Wireless Networks

“…On the one hand, with packet arrivals following the Bernoulli distribution, which is independent with the departure process, we have E[M t ] = 1/ξ. On the other hand, the average sojourn time of a Geo/Geo/1 queue can be calculated as [14] E…”

“…The power of stochastic geometry has made it a disruptive tool for performance evaluation among various wireless applications, including ad-hoc and cellular networks [7], D2D communications [8], MIMO [9], and mmWave systems [10]. While such model has been conventionally relying on the full buffer assumption, i.e., every link always has a packet to transmit, a line of recent works managed to bring in queueing theory and relax this constraint [11]- [14], allowing one to give a complete treatment for the behavior of wireless links from both spatial and temporal perspectives. As a result, the model is further employed to design scheduling policies [12], [14], study the scaling property in IoT networks [11], and analyze the delay performance in cellular network [13].…”

“…While such model has been conventionally relying on the full buffer assumption, i.e., every link always has a packet to transmit, a line of recent works managed to bring in queueing theory and relax this constraint [11]- [14], allowing one to give a complete treatment for the behavior of wireless links from both spatial and temporal perspectives. As a result, the model is further employed to design scheduling policies [12], [14], study the scaling property in IoT networks [11], and analyze the delay performance in cellular network [13]. In this paper, we leverage a spatiotemporal model as in [15] for the design of a transmission protocol that optimizes information freshness in wireless networks.…”