Analysis of the LTE Access Reservation Protocol for Real-Time Traffic

LTE is increasingly seen as a system for serving real-time Machine-to-Machine (M2M) communication needs. The asynchronous M2M user access in LTE is obtained through a two-phase access reservation protocol (contention and data phase). Existing analysis related to these protocols is based on the following assumptions: (1) there are sufficient resources in the data phase for all detected contention tokens, and (2) the base station is able to detect collisions, i.e., tokens activated by multiple users. These assumptions are not always applicable to LTE - specifically, (1) due to the variable amount of available data resources caused by variable load, and (2) detection of collisions in contention phase may not be possible. All of this affects transmission of real-time M2M traffic, where data packets have to be sent within a deadline and may have only one contention opportunity. We analyze the features of the two-phase LTE reservation protocol and derive its throughput, i.e., the number of successful transmissions in the data phase, when assumptions (1) and (2) do not hold.Comment: 4 Pages, 4 Figures, Accepted in IEEE Communication Letters on the 20th of May 201

Efficient Random Access Channel Evaluation and Load Estimation in LTE-A with Massive MTC

© 2019 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works."[EN] The deployment of machine-type communications (MTC) together with cellular networks has a great potential to create the ubiquitous Internet-of-Things environment. Nevertheless, the simultaneous activation of a large number of MTC devices (named UEs herein) is a situation difficult to manage at the evolved Node B (eNB). The knowledge of the joint probability distribution function (PDF) of the number of successful and collided access requests within a random access opportunity (RAO) is a crucial piece of information for contriving congestion control schemes. A closed-form expression and an efficient recursion to obtain this joint PDF are derived in this paper. Furthermore, we exploit this PDF to design estimators of the number of contending UEs in an RAO. Our numerical results validate the effectiveness of our recursive formulation and show that its computational cost is considerably lower than that of other related approaches. In addition, our estimators can be used by the eNBs to implement highly efficient congestion control methods.This work was supported in part by the Ministry of Economy and Competitiveness of Spain under Grants TIN2013-47272-C2-1-R and TEC2015-71932-REDT. The work of L. Tello-Oquendo was supported in part by the Universitat Politecnica de Valencia under the Programa de Ayudas de Investigacion y Desarrollo (PAID). The work of I. Leyva-Mayorga was supported in part by the CONACYT-Gobierno del Estado de Mexico under Grant 383936. The review of this paper was coordinated by Dr. Y. Ji.Tello-Oquendo, L.; Pla, V.; Leyva-Mayorga, I.; Martínez Bauset, J.; Casares-Giner, V.; Guijarro, L. (2019). Efficient Random Access Channel Evaluation and Load Estimation in LTE-A with Massive MTC. IEEE Transactions on Vehicular Technology. 68(2):1998-2002. https://doi.org/10.1109/TVT.2018.2885333S1998200268

Preamble Transmission Prediction for mMTC Bursty Traffic : A Machine Learning based Approach

Author's accepted manuscript.© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.acceptedVersio

Optimal Channel-Switching Strategies in Multi-channel Wireless Networks.

The dual nature of scarcity and under-utilization of spectrum resources, as well as recent advances in software-defined radio, led to extensive study on the design of transceivers that are capable of opportunistic channel access. By allowing users to dynamically select which channel(s) to use for transmission, the overall throughput performance and the spectrum utilization of the system can in general be improved, compared to one with a single channel or more static channel allocations. The reason for such improvement lies in the exploitation of the underlying temporal, spatial, spectral and congestion diversity. In this dissertation, we focus on the channel-switching/hopping decision of a (group of) legitimate user(s) in a multi-channel wireless communication system, and study three closely related problems: 1) a jamming defense problem against a no-regret learning attacker, 2) a jamming defense problem with minimax (worst-case) optimal channel-switching strategies, and 3) the throughput optimal strategies for a group of competing users in IEEE 802.11-like medium access schemes. For the first problem we study the interaction between a user and an attacker from a learning perspective, where an online learner naturally adapts to the available information on the adversarial environment over time, and evolves its strategy with certain payoff guarantee. We show how the user can counter a strong learning attacker with knowledge on its learning rationale, and how the learning technique can itself be considered as a countermeasure with no such prior information. We further consider in the second problem the worst-case optimal strategy for the user without prior information on the attacking pattern, except that the attacker is subject to a resource constraint, which models its energy consumption and replenishment process. We provide explicit characterization for the optimal strategies and show the most damaging attacker, interestingly, behaves randomly in an i.i.d. fashion. In the last problem, we consider a group of competing users in a non-adversarial setting. We place the interaction among users in the context of IEEE 802.11-like medium access schemes, and derive decentralized channel allocation for overall throughput improvement. We show the typically rule-of-thumb load balancing principle in spectrum resource sharing can be indeed throughput optimal.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/108949/1/qingsi_1.pd

Decentralised Algorithms for Wireless Networks.

Designing and managing wireless networks is challenging for many reasons. Two of the most crucial in 802.11 wireless networks are: (a) variable per-user channel quality and (b) unplanned, ad-hoc deployment of the Access Points (APs). Regarding (a), a typical consequence is the selection, for each user, of a different bit-rate, based on the channel quality. This in turn causes the so-called performance “anomaly”, where the users with lower bit-rate transmit for most of the time, causing the higher bit-rate users to receive less time for transmission (air time). Regarding (b), an important issue is managing interference. This can be mitigated by selecting different channels for neighbouring APs, but needs to be carried out in a decentralised way because often APs belong to different administrative domains, or communication between APs is unfeasible. Tools for managing unplanned deployment are also becoming important for other small cell networks, such as femtocell networks, where decentralised allocation of scrambling codes is a key task

Stochastic models for cognitive radio networks

During the last decade we have seen an explosive development of wireless technologies. Consequently the demand for electromagnetic spectrum has been growing dramatically resulting in the spectrum scarcity problem. In spite of this, spectrum utilization measurements have shown that licensed bands are vastly underutilized while unlicensed bands are too crowded. In this context, Cognitive Radio emerges as an auspicious paradigm in order to solve those problems. Even more, this concept is envisaged as one of the main components of future wireless technologies, such as the fifth-generation of mobile networks. In this regard, this thesis is founded on cognitive radio networks. We start considering a paid spectrum sharing approach where secondary users (SUs) pay to primary ones for the spectrum utilization. In particular, the first part of the thesis bears on the design and analysis of an optimal SU admission control policy, i.e. that maximizes the long-run profit of the primary service provider. We model the optimal revenue problem as a Markov Decision Process and we use dynamic programming (and other techniques such as sample-path analysis) to characterize properties of the optimal admission control policy. We introduce different changes to one of the best known dynamic programming algorithms incorporating the knowledge of the characterization. In particular, those proposals accelerate the rate of convergence of the algorithm when is applied in the considered context. We complement the analysis of the paid spectrum sharing approach using fluid approximations. That is to say, we obtain a description of the asymptotic behavior of the Markov process as the solution of an ordinary differential equation system. By means of the fluid approximation of the problem, we propose a methodology to estimate the optimal admission control boundary of the maximization profit problem mentioned before. In addition, we use the deterministic model in order to propose some tools and criteria that can be used to improve the mean spectrum utilization with the commitment of providing to secondary users certain quality of service levels. In wireless networks, a cognitive user can take advantage of either the time, the frequency, or the space. In the first part of the thesis we have been concentrated on timefrequency holes, in the second part we address the complete problem incorporating the space variable. In particular, we first introduce a probabilistic model based on a stochastic geometry approach. We focus our study in two of the main performance metrics: medium access probability and coverage probability. Finally, in the last part of the thesis we propose a novel methodology based on configuration models for random graphs. With our proposal, we show that it is possible to calculate an analytic approximation of the medium access probability (both for PUs and, most importantly, SUs) in an arbitrary large heterogeneous random network. This performance metric gives an idea of the possibilities offered by cognitive radio to improve the spectrum utilization. The introduced robust method, as well as all the results of the thesis, are evaluated by several simulations for different network topologies, including real scenarios of primary network deployments. Keywords: Markov decision process, fluid limit, stochastic geometry, random graphs,dynamic spectrum assignment, cognitive radi