Internet of Things (IoT) imbued with several heterogeneous services and applications is integral to the ultimate realization of connected living. However, massive connectivity would pose huge challenges not only due to immense scalability but also due to diverse characteristics of periodicity, delay-criticality, and quality-of-service (QoS) requirement. One important trend of this diversity is that the connected devices would manifest different priorities. Random access procedure (RAP) is the first step by which most devices establish connection to the base station. A major transformation is urgently needed in RAP for cellular IoT as the volume of connected devices would far exceeds human oriented connections of the legacy networks. To prioritize the RAP for IoT devices, we propose novel online control algorithm that enables the most likely successful preamble transmission for the devices that manifest higher priority requirements. In the proposed algorithm, the number of active devices in each priority is recursively estimated based on Bayesian rule and then the preambles to each priority are accordingly allocated. We extend our proposal by adopting access class baring (ACB) to optimize the algorithm and subsequently, enhance it further by incorporating access delay requirements. Extensive simulations show the effectiveness of proposed algorithms over multiple priorities and confirm that the proposed algorithms are able to resolve congestions for massive activation of IoT devices.

Internet of Things (IoT) is the ongoing revolution that offers a truly connected society by integrating several heterogeneous services and applications. A major transformation is urgently needed when the volume of connected devices far exceeds people oriented connections. Due to diversity in applications and requirements, one important trend is that the connected devices could manifest different priorities. With the variety of requirements accordingly, in terms of latency, payload size, number of connections, update frequency, reliability, the Random access process (RAP) as the first step to establish connection between most devices and the base station would also require modification in cellular IoT. In order to prioritize the IoT devices in RAP, we propose a novel online control algorithm with dynamic preamble distribution over multiple priorities. The proposed algorithm recursively estimates the number of active devices in each priority based on Bayesian rule and controls the number of preambles for each priority accordingly. Subsequently, we extend our proposal to incorporate access class baring (ACB) to optimize the algorithm. Extensive simulations show the effectiveness of proposed algorithm over multiple priorities.

This paper considers non-orthogonal multiple access (NOMA) systems in which users randomly transmit packets in each slot while the transmit power is adjusted so that the receive power of each packet at the base station can be one of two predetermined values. While the NOMA random access system can support simultaneous transmissions from up to two users with different target receive powers, it complicates the access protocol design as each user not only has to optimize the transmission probability but also has to suitably choose the target receive power. By considering the fact that users may have different capability of choosing the target receive power due to their diverse locations in a cell, we propose an online distributed channel access algorithm which enables the users to adaptively choose the target receive power and control the transmission probability over time through observing channel outcomes such as idle, success and collision. Numerical results demonstrate that the proposed algorithm can optimize the system performance in terms of throughput and access fairness.

The evolution towards 5G network emphasizes on massive machine type communication (mMTC) as an important work direction. Massive connectivity in Machine-Type Communications (MTC) poses huge challenge for the efficient management of random access mechanism, originally designed and optimized for limited number of broadband connections in legacy networks. When large number of MTC devices, with bursty traffic characteristics, randomly attempt to access the network, the probability of collision preambles drastically increases. Thus, with increased scalability of MTC devices the performance of LTE/LTE-A random access mechanism degrades sharply. Our proposed analysis provides optimal access class barring to maximize the number of average access success preambles. We also evaluate the upper bound performance to estimate the average access delay. The proposed optimal random access procedure facilitates for abstractions in simulations to better serve efficient network design that supports scalability of new MTC devices.

In unsaturated slotted carrier sense multiple access (CSMA) systems, the queue states of the stations (STNs) in uplink vary over time in general. This phenomenon is the main difficulty that complicates the efficient system operation. In this paper, we propose an estimation algorithm to estimate the backlog size, i.e., the number of STNs who have packets to transmit in each slot. Then, based on the estimation, the transmission probability is controlled to maximize the system throughput. One key feature of the proposed algorithm is that it only applies the information about the idle and busy channel slots to estimate the backlog size. Through extensive computer simulations, we show that the proposed algorithm achieves near- optimal system performance in term of mean and variance of queuing delay.