Traffic‐aware overload control scheme in 5G ultra‐dense M2M networks

Abstract

Due to a huge number of M2M devices expected to access the network simultaneously, congestion and overload of networks are the core challenges to the 5G ultra‐dense M2M networks design. In this paper, an optimal traffic‐load control scheme is proposed to overcome the challenges. Since the base station lacks accurate information about the traffic‐load of network resulting in an unachievable solution to the optimal control scheme in practice, we propose two different traffic‐load estimation schemes corresponding to the two different access protocols. For the first protocol that the collision devices are not barred by an access probability, we propose a markov‐chain based estimation scheme according to the collision state of network. For the second protocol that the collision devices are barred by an access probability, we estimate the traffic‐load using the number of success devices and the average number of new active devices in each slot. Both estimation schemes ensure that the practical control schemes are very close to the theoretical optimal control scheme. In addition, we prove that the optimal performances of the two protocols are exactly the same in theory. Finally, the simulation results exhibit our conclusion and show the superior performances of our proposed scheme.

Online Supervised Learning for Traffic Load Prediction in Framed-ALOHA Networks

“…where the N max is an upper bound on the backlog. Unless one makes simplistic assumptions as in [4][5][6], problem (1) remains generally intractable even in the presence of realistic traffic models, further justifying the data-driven solutions developed in prior art and in this letter. We emphasize that we focus solely on the problem of prediction and that we leave the problem of investigating the interplay between overload control via, e.g., frame size selection and access barring, and traffic prediction to future work.…”

“…Every device has only two possible states, either inactive or active, while it has a single packet to be transmitted in the latter case. This captures sporadic traffic random access in cellular-based networks as detailed in [2,[4][5][6].…”

“…A more complex estimator can be obtained by using the ML estimatorÑ t ML of the current backlog [6]. This is given asN t+1…”

“…This Markov chain is defined by letting each device sequentially, and independently, select an RAO at each step. We refer to [6] for details on the numerical procedure.…”

Estimating the number of contending IoT devices in 5G networks: Revealing the invisible

“…Nevertheless, no traffic differentiation is considered and all IoT devices are treated identically. Using a similar approach for deducing the optimal load, the authors, in previous works, proposed to rely on the calculation of the steady state probabilities of a Markov chain to deduce an efficient control scheme. A similar method was proposed by Pang et al This method estimates the number of RA attempts and then proposed a scheme for resources' allocation.…”