Scheduling fast uplink grant transmissions for machine type communications (MTCs) is one of the main challenges of future wireless systems. In this paper, a novel fast uplink grant scheduling method based on the theory of multi-armed bandits (MABs) is proposed. First, a single quality-of-service metric is defined as a combination of the value of data packets, maximum tolerable access delay, and data rate. Since full knowledge of these metrics for all machine type devices (MTDs) cannot be known in advance at the base station (BS) and the set of active MTDs changes over time, the problem is modeled as a sleeping MAB with stochastic availability and a stochastic reward function. In particular, given that, at each time step, the knowledge on the set of active MTDs is probabilistic, a novel probabilistic sleeping MAB algorithm is proposed to maximize the defined metric. Analysis of the regret is presented and the effect of the prediction error of the source traffic prediction algorithm on the performance of the proposed sleeping MAB algorithm is investigated. Moreover, to enable fast uplink allocation for multiple MTDs at each time, a novel method is proposed based on the concept of best arms ordering in the MAB setting. Simulation results show that the proposed framework yields a three-fold reduction in latency compared to a maximum probability scheduling policy since it prioritizes the scheduling of MTDs that have stricter latency requirements. Moreover, by properly balancing the exploration versus exploitation tradeoff, the proposed algorithm selects the most important MTDs more often by exploitation. During exploration, the sub-optimal MTDs will be selected, which increases the fairness in the system, and, also provides a better estimate of the reward of the sub-optimal MTD.

Supporting parallel multi-priority data streams is vital for maintaining the quality of service (QoS) in vehicle-to-everything (V2X) communication. Hence, the European telecommunications standard institute (ETSI) has defined four packet types, with varying priority levels, to be used as broadcast packets in such communication. This paper studies the medium access control (MAC) layer performance of IEEE 802.11p and cellular-V2X (C-V2X) Mode 4 using discrete-time Markov chain (DTMC) based models, while considering parallel multi-priority data streams. The overall model consists of four queue models with their respective traffic generators, which are appropriately linked with the DTMCs modeling the MAC layer operations of IEEE 802.11p and C-V2X Mode 4. Closed-form solutions for the steady-state probabilities of the models are obtained, which are then utilized to derive expressions for key performance indicators at the MAC layer. Numerical results are provided to draw insights on the MAC layer performance of the two technologies. IEEE 802.11p is comparatively superior in average delay, and at maintaining fairness among multi-priority data streams, whereas C-V2X Mode 4 exhibits better collision resolution, which leads to its higher throughput. The paper also includes design insights on possible performance enhancements for future releases.

This paper presents experimental validation of a distributed optimization-based voltage control system. The dual-decomposition method is used in this paper to solve the voltage optimization problem in a fully distributed way. Device-to-device communication is implemented to enable peer-to-peer data exchange between agents of the proposed voltage control system. The paper presents the design, development and hardware setup of a laboratory-based testbed used to validate the performance of the proposed dual-decomposition-based peer-to-peer voltage control. The architecture of the setup consists of four layers: microgrid, control, communication, and monitoring. The key question motivating this research was whether distributed voltage control systems are a technically effective alternative to centralized ones. The results discussed in this paper show that distributed voltage control systems can indeed provide satisfactory regulation of the voltage profiles.

Prioritization of data streams in cellular vehicle-to-everything (C-V2X) may lead to unfavorable packet delays in low-priority streams. This paper studies allocating multiple candidate single-subframe resources (CSRs) per vehicle as a solution. It proposes a methodology to determine the number of CSRs for each vehicle based on the total number of neighboring vehicles, and to assign the multiple data streams among them for simultaneous transmission. The numerical results highlight the achievable delay gains of the proposed approach, and its negligible impact on packet collisions.

This paper investigates the possibility of delivering distinct smart grid (SG) demand response (DR) applications in a highly loaded LTE network. In a shared LTE network, the proportion of SG DR traffic is relatively low when compared to typical traffics such as voice over IP, Skype video call, FTP, Youtube video stream, and HTTP. The quality of service (QoS) requirements for the SG DR traffics have to be fulfilled by maintaining the network delays and the packet delivery ratios within certain limits, while not causing significant hindrance to the typical traffics. The Riverbed Modeler network simulations are performed using detailed physical layer propagation models, detailed LTE functionality, and a suburban topology. In the simulation scenarios, three distinct DR applications generate varying amounts of SG DR traffic to the LTE network while the LTE capacity is exceeded by the typical traffics. The results illustrate that satisfactory performance for the SG DR traffics can be maintained due to the constant traffic characteristics and relatively low traffic amount that facilitates the scheduling of channel resources. Typically, the more a DR application generates traffic the higher hindrance it causes for the typical traffics other than the voice over IP that applies the QoS class of highest priority.

Ensuring an effective coexistence of conventional broadband cellular users with machine type communications (MTCs) is challenging due to the interference from MTCs to cellular users. This interference challenge stems from the fact that the acquisition of channel state information (CSI) from machine type devices (MTD) to cellular base stations (BS) is infeasible due to the small packet nature of MTC traffic. In this paper, a novel approach based on the concept of opportunistic spatial orthogonalization (OSO) is proposed for interference management between MTC and conventional cellular communications. In particular, a cellular system is considered with a multi-antenna BS in which a receive beamformer is designed to maximize the rate of a cellular user, and, a machine type aggregator (MTA) that receives data from a large set of MTDs. The BS and MTA share the same uplink resources, and, therefore, MTD transmissions create interference on the BS. However, if there is a large number of MTDs to chose from for transmission at each given time for each beamformer, one MTD can be selected such that it causes almost no interference on the BS. A comprehensive analytical study of the characteristics of such an interference from several MTDs on the same beamformer is carried out. It is proven that, for each beamformer, an MTD exists such that the interference on the BS is negligible. To further investigate such interference, the distribution of the signal-to-interference-plus-noise ratio (SINR) of the cellular user is derived, and, subsequently, the distribution of the outage probability is presented. However, the optimal implementation of OSO requires the CSI of all the links in the BS, which is not practical for MTC. To solve this problem, an online learning method based on the concept of contextual multi-armed bandits (MAB) learning is proposed. The receive beamformer is used as the context of the contextual MAB setting and Thompson sampling: a well-known method of solving contextual MAB problems is proposed. Since the number of contexts in this setting can be unlimited, approximating the posterior distributions of Thompson sampling is required. Two function approximation methods, a) linear full posterior sampling, and, b) neural networks are proposed for optimal selection of MTD for transmission for the given beamformer. Simulation results show that is possible to implement OSO with no CSI from MTDs to the BS. Linear full posterior sampling achieves almost 90% of the optimal allocation when the CSI from all the MTDs to the BS is known.