Millimeter wave (mmWave) spectrum promises unprecedented data rates in 5G New Radio (NR). However, mmWave links are susceptible to severe path and propagation losses. Implementation of directional antennas that can manifest multiple beams becomes necessary at 5G base stations (gNBs) and User Equipments (UEs). Random access procedure (RAP), which is the first stage to establish connections to gNB, in its current state would be inadequate for such systems. To support RAP, 5G NR introduced mapping of different subsets of preambles in Physical Random Access Channel (PRACH) to different beams. We explore changes in RAP due to preamble-beam mapping and present a novel online algorithm to dynamically distribute preambles amongst beams to achieve fair access opportunities. The proposed algorithm can also eliminate overload problem of the beams with large UE population especially for high arrival rates. We extend the proposed algorithm to be applicable for RAP with access class barring mechanism which enables UEs to transmit a preamble with an optimal probability and, therefore, reduce access congestion. Through extensive simulations, we validate the efficiency of our proposed algorithms in providing fair channel access opportunities, improving throughput (or stability region), and reducing access delay when the system has high rate of UE arrivals and has unbalanced UE arrivals over different beams.

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.

Emerging mmWave enabled 5G wireless communications, offer tremendous increase in data demands and better quality expectations. However, high mmWave frequencies call for directional beamforming to overcome propagation limitations and enhance spatial capabilities. The establishment of new directional paradigm challenges the current Discontinuous Reception (DRX) mechanism for power saving in the User Equipment (UE). In this article, we introduce new Directional-Discontinuous Reception (DDRX) for directional air interface expected in mmWave enabled 5G communications. DDRX mechanism emphasizes the importance of beam searching for alignment of directional beams between UE and 5G base station (gNB), after every sleep cycle. Beam searching, though inevitable, reduces the effective sleep time. We propose three new DDRX mechanisms: Integrated DDRX (I-DDRX), Standalone DDRX (S-DDRX), and Cooperative DDRX (C-DDRX) to limit the impediments on power saving. Probabilistic estimation of UE's power saving and delay is performed for all the three different DDRX mechanisms using semi-Markov process. The power saving achieved in IDDRX is 12.1 percent higher than S-DDRX. The power saving of C-DDRX is 6.1 percent higher than S-DDRX. Analytical results of DDRX proposals are validated by simulation studies, performed over real wireless trace.

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.