In cellular and cellular-IoT systems, random access has the utmost importance as it is the gatekeeper that assures conflict-free access to radio resources. The main drawback of the legacy protocols lies at the collision resolution procedure as the base station (BS) is blind to the collision multiplicity and collided devices rely on the BSs for contention resolution. While contention resolution over the random access channel (RACH) is mature in the literature, most studies are following optimistic assumptions in which the full collision impact at the receiver is neglected, and a simplified model of partially overlapping packets is considered. This work is devoted to the investigation of a more realistic model of collision in cellular networks with the aim to investigate challenges and potential solutions in preamble detection in collision scenarios. The simulation results show that besides previously well-known challenges in dealing with collisions, the occurrence of collisions significantly increases the probability of false preamble detection.

In addition to the Mobile Broadband (MBB) services, future cellular networks will need to cope with a range of new "Internet of things" (IoT) services. LTE-Advanced and future generation cellular technologies should support both service sets within one network in order to keep the service costs and deployment expenses low. Massive IoT services require much less bandwidth than the MBB services, but the network need to serve a massive number of devices in each cell. The IoT applications put stringent demands on the service reliability and energy efficiency, and some will require very low delay. The existing random access (RA) procedures of cellular networks are not really designed for large numbers of terminals, which may result in excessive collisions, and hence, link delay and waste of precious battery energy. The present work aims at solving this problem by proposing a novel delay-estimation based random access scheme that improves the resolution mechanism of the conventional RA procedure in order to lower delay and energy consumption. The performance evaluation results show that the proposed scheme can significantly reduce the access delay in densely deployed scenarios.

The Random Access (RA) procedure in existing cellular networks is not capable of functioning properly during high access load conditions. For this purpose, overload control mechanisms are needed. Most proposed mechanisms in the literature offer a tradeoff between access rate and experienced delays. However, when the maximal tolerated delay and the energy spent on retransmissions are tightly bounded, the very high access rate, targeted for 5G systems, cannot be achieved. For these situations, we propose the Delay Estimation based RA (DERA)-scheme that has the potential to meet very stringent reliability requirements, even in high access load conditions. The present work shows that this goal can be achieved only at the cost of limited additional complexity. Furthermore, we also study the optimal switchover point at which the proposed scheme moves from low-load to the high-load phase. The derived tool can also be used along with other proposed RA overload control schemes, e.g.when to invoke access class barring. The performance evaluation results show that the novel DERA scheme can significantly improve the control channels’ resource utilization along with the success rate in dense deployment scenarios.

Successful implementation of massive machine-type communications (MTC) service over cellular networks is an important enabler of Internet of things. As the existing cellular infrastructure has been designed and optimized for moderate traffic, the introduction of MTC in cellular networks results in several key challenges, among them, temporal starvation of initial access resources is of paramount importance. Here, we propose a new traffic model for cellular networks serving machine-type subscribers. This model is subsequently used to derive closed-form expressions for the access rate performance of the system. This closed-form expression are subsequently used for RACH resources planning in the time and frequency domains. Given a target access rate requirement, numerical analysis indicates how and when the required RACH resources in the code/frequency domain, i.e. number of preambles, can be traded with the occurrence frequency of RACH slots in the time domain.

3GPP introduced Early Data Transmit (EDT) protocol in release-15 to address Internet of things (IoT) services signaling cost and connection delay due to RRC connectivity procedure. EDT enables data transmission in MSG3 wherein traditional RACH procedure, a collision is realized at MSG3 reception. Traditional preventive approach (access class barring) may control the device access rate, but that does not always adequate to solve hidden collision due to the receiver’s limitation and devices power control mechanism. The present work aims at solving the problem by allocating extra resources pool for potential collided devices. The allocated resource of the pool is optimized based on an artificial neural network (ANN) based learning algorithm. The performance evaluation result shows that the learning-based resource allocation radically increases the data transmission success rate with EDT.

This techno-economic study introduces a framework to study the deployment cost of IoT radio access network and to compare the cost and capacity of LPWAN, LPLAN, and Cellular-IoT systems. There are a plethora of Communication technologies available today. Small rollout and pilot projects are visible for most of the IoT connectivity technologies. However, The economic viability of cellular and non-cellular IoT connectivity technologies are not definite. The primary objective of this work is to analyze the cost structure of SigFox, LoRaWAN, NB-IoT, and WiFi-HaLow. In this paper, we study an urban scenario with the consideration of an Greenfield and Brownfield actor to understand the technology selection strategy of communication service prodvider's perspective. One of our findings indicates that the maintenance cost is the key cost driver for IoT radio access networks. We also point out certain bounds where a technology is potentially cost-effective.

— Pilot rollouts of Cellular-IoT (C-IoT) networks just started to take place this year. In the coming years, commercial deployment will take place. However, among the plethora of low power wide area network (LPWAN) technologies, the cost-effectiveness of C-IoT is not certain for IoT service providers, small and greenfield operators.  Today, there is no known public framework for the feasibility analysis of IoT communication technologies.

Hence, this paper first presents a generic framework to assess the viability of cellular and non-cellular LPWAN technologies. Then, we applied the framework in eight deployment scenarios to analyze the prospect of LPWAN technologies like Sigfox, LoRaWAN, NB-IoT, LTE-M, and EC-GSM. We consider the inter-technology interference impact on LoRaWAN and Sigfox scalability. Our results validate that large rollout with a single technology is not cost-efficient. Also, our analysis suggests the viability of an IoT communication Technology may not be linear to cost efficiency.

A plethora of Internet of Things (IoT) platforms are available in the market today. Most of the IoT platforms are used mainly for service prototyping. Cost-efficient service scalability on any platform is still an unresolved concern that, so far, has been addressed qualitatively. A quantitative method for IoT platform economics is missing in the literature. In this paper, we propose a generic framework to address this gap. Our proposed framework covers the dimensioning of the platform's software and hardware to envisage the design, deployment, and operation cost of platform services. Then, we use the framework to perform a quantitative study of platform rollout in three platform business contexts. Our analysis shows the applicability of different deployment and platform integration choices. Our results suggest that storage and energy are the main cost drivers for platforms' hardware scalability, where the main cost driver is the intensity of the sensors' message transmission rate. Additionally, our use-case based study suggests that platform as a service (PaaS) is only beneficial for actors who have limited scale or niche market need.