Mikhail Gerasimenko

Understanding the IoT connectivity landscape: a contemporary M2M radio technology roadmap

This article addresses the market-changing phenomenon of the Internet of Things (IoT), which relies on the underlying paradigm of machine-to-machine (M2M) communications to integrate a plethora of various sensors, actuators, and smart meters across a wide spectrum of businesses. Today the M2M landscape features an extreme diversity of available connectivity solutions which, due to the enormous economic promise of the IoT, need to be harmonized across multiple industries. To this end, we comprehensively review the most prominent existing and novel M2M radio technologies, as well as share our first-hand real-world deployment experiences, with the goal to provide a unified insight into enabling M2M architectures, unique technology features, expected performance, and related standardization developments. We pay particular attention to the cellular M2M sector employing 3GPP LTE technology. This work is a systematic recollection of our many recent research, industrial, entrepreneurial, and standardization efforts within the contemporary M2M ecosystem.

Efficient small data access for machine-type communications in LTE

In this paper, we address the emerging concept of Machine-Type Communications (MTC), where unattended wireless devices send their data over the Long Term Evolution (LTE) cellular network. In particular, we emphasize that future MTC deployments are expected to feature a very large number of devices, whereas the data from a particular device may be infrequent and small. Currently, LTE is not optimized for such traffic and its data transmission schemes are not MTC-specific. To improve the efficiency of small data access, we propose a novel contention-based LTE transmission (COBALT) mechanism and evaluate its performance with both analysis and protocol-level simulations. When compared against existing alternatives, our data access scheme is demonstrated to improve network resource consumption, device energy efficiency, and mean data access delay. We conclude that COBALT has the potential for supporting massive MTC deployments based on the future releases of the LTE technology.

Intelligent access network selection in converged multi-radio heterogeneous networks

Heterogeneous multi-radio networks are emerging network architectures that comprise hierarchical deployments of increasingly smaller cells. In these deployments, each user device may employ multiple radio access technologies to communicate with network infrastructure. With the growing numbers of such multi-radio consumer devices, mobile network operators seek to leverage spectrum across diverse radio technologies, thus boosting capacity and enhancing quality of service. In this article, we review major challenges in delivering uniform connectivity and service experience to converged multiradio heterogeneous deployments. We envision that multiple radios and associated device/infrastructure intelligence for their efficient use will become a fundamental characteristic of future 5G technologies, where the distributed unlicensed-band network (e.g., WiFi) may take advantage of the centralized control function residing in the cellular network (e.g., 3GPP LTE). Illustrating several available architectural choices for integrating WiFi and LTE networks, we specifically focus on interworking within the radio access network and detail feasible options for intelligent access network selection. Both network- and user-centric approaches are considered, wherein the control rests with the network or the user. In particular, our system-level simulation results indicate that load-aware usercentric schemes, which augment SNR measurements with additional information about network loading, could improve the performance of conventional WiFi-preferred solutions based on minimum SNR threshold. Comparison with more advanced network-controlled schemes has also been completed to confirm attractive practical benefits of distributed user-centric algorithms. Building on extensive system-wide simulation data, we also propose novel analytical space-time methodology for assisted network selection capturing user traffic dynamics together with spatial randomness of multi-radio heterogeneous networks.

Impact of machine‐type communications on energy and delay performance of random access channel in LTE‐advanced

Machine-type communications (MTC) are a rapidly growing technology, which is expected to generate significant revenues to mobile network operators. In particular, smart grid is predicted to become one of the key MTC use cases that involves unattended meters autonomously reporting information to a grid infrastructure. With this research, we consider a typical smart metering MTC application scenario in the context of 3GPP LTE-advanced wireless cellular system featuring a large number of devices connecting to the network near-simultaneously. The resulting overload of the random access channel requires a novel evaluation methodology based on comprehensive analysis and simulations. In this paper, we target to complement a validated evaluation framework fully compatible with the 3GPP test cases with a thorough analysis of random access channel performance in overloaded MTC scenarios. We also look at the regular MTC operation, when the devices are sending their data after initial network entry has been performed. By including energy consumption into our methodology together with the conventional performance metrics, we aim at providing a complete and unified insight into MTC device operation, including its energy efficiency. Copyright

Multi-radio heterogeneous networks: Architectures and performance

It is well-known that next generation (5G) wireless networks will need to provide orders of magnitude more capacity to address the predicted growth in mobile traffic demand, as well as support reliable connectivity for billions of diverse devices, which comprise the “Internet of Things.” While the industry is focused on a concerted effort to improve capacity of cellular networks, operators are increasingly using WiFi technology over un-licensed spectrum to relieve congestion in their networks. This paper argues that the trend towards integrated use of multiple radio access technologies (RATs) and networks, such as WiFi, will be essential for addressing the challenges faced by future 5G networks. In particular we expect that joint use of multiple RATs can yield beyond additive gains in user connectivity experience by exploiting the rich multi-dimensional diversity (e.g., spatial, temporal, frequency, load, etc.) available across multiple radio networks. We investigate such benefits through a case study on integrating WiFi with 3GPP heterogeneous networks. Our results show that intelligent integration of WiFi/3GPP radio networks can yield an additional 2-3x gains in system capacity and user quality of service, beyond what is achievable from independent use of both networks.

Cooperative Radio Resource Management in Heterogeneous Cloud Radio Access Networks

Responding to the unprecedented challenges imposed by the 5G communications ecosystem, emerging heterogeneous network architectures allow for improved integration between multiple radio access technologies. When combined with advanced cloud infrastructures, they bring to life a novel paradigm of heterogeneous cloud radio access network (H-CRAN). The novel H-CRAN architecture opens door to improved network-wide management, including coordinated cross-cell radio resource allocation. In this paper, emphasizing the lack of theoretical performance analysis, we specifically address the problem of cooperative radio resource management in H-CRAN by providing a comprehensive mathematical methodology for its real-time performance optimization. Our approach enables flexible balance between throughput and fairness metrics, as may be desired by the network operator, and demonstrates attractive benefits when compared against the state-of-the-art multiradio resource allocation strategies. The resulting algorithms are suitable for efficient online implementation, which principal feasibility is confirmed by our proof-of-concept prototype.

Capturing Spatial Randomness of Heterogeneous Cellular/WLAN Deployments With Dynamic Traffic

As fourth generation communications technology is already being deployed, research efforts are now being shifted to what comes beyond state-of-the-art wireless systems. Driven by the anticipated acceleration in mobile traffic demand, the wireless industry is specifically focused on improving capacity and coverage of current networks through aggressive reuse of the cellular spectrum. Together with deploying an increasingly dense overlay tier of smaller cells, mobile network operators are beginning to rely on unlicensed-band WLAN technologies to leverage additional spectrum and relieve congestion on their networks. Consequently, the emerging vision of heterogeneous networks exploits the potential of a diverse range of devices requiring connectivity at different scales to augment available system capacity and improve the user connectivity experience. In this paper, we seek to meet this important trend with our novel integrated methodology for assisted (managed) radio network selection capturing spatial randomness of converged cellular/WLAN deployments together with dynamic uplink traffic from their users. To this end, we employ tools coming from stochastic geometry to characterize performance of macro and pico cellular networks, as well as WLAN, mindful of user experience and targeting intelligent network selection/assignment. We complement our analysis with system-level simulations providing deeper insights into the behavior of future heterogeneous deployments.

Energy and delay analysis of LTE-Advanced RACH performance under MTC overload

Machine-Type Communications (MTC) is a rapidly growing technology, which is expected to generate significant revenues to mobile network operators. In particular, smart grid is predicted to become one of the key MTC use cases that involves meters autonomously reporting information to a grid infrastructure. With this research, we consider a typical smart metering MTC application scenario in the context of 3GPP LTE-Advanced wireless cellular system featuring a large number of devices connecting to the network near-simultaneously. The resulting overload of the random access channel (RACH) requires a novel evaluation methodology based on comprehensive analysis and simulations. In this paper, we target to complement a validated evaluation methodology fully compatible with the 3GPP test cases with a thorough analysis of RACH performance in overloaded MTC scenarios. By including energy consumption into our framework together with the conventional performance metrics, we aim at providing a complete and unified insight into MTC device operation.

Characterizing performance of load-aware network selection in multi-radio (WiFi/LTE) heterogeneous networks

In this paper, we consider the problem of network selection between different radio access technologies (RATs) deployed as part of an operator managed multi-RAT heterogeneous network. An urban deployment scenario is studied where WiFi small cells are overlaid on top of the 3GPP LTE network. We assume limited cooperation across the multi-RAT network and emphasize user-centric network selection algorithms to minimize feedback overhead and to better account for user preferences. Specifically, we investigate schemes that rely on network loading information with suitable adaptation of hysteresis mechanisms and compare them with WiFi-preferred schemes that only account for signal strength measurements. We also benchmark the performance of load-aware schemes against conventional cell-range extension methods that use network-wide optimization to offload users to small cells. The results of our system-level performance evaluation show that load-aware user-centric schemes can provide improved performance compared to the WiFi-preferred schemes and may even outperform network-based cell-range extension schemes under some conditions.

Analyzing Impacts of Coexistence between M2M and H2H Communication on 3GPP LTE System

In this paper, we consider 3GPP LTE cellular system where machine-to-machine (M2M) devices and human-to-human (H2H) users transmit their data into the network. By contrast to previous studies which primarily focused on M2M overload protection and respective control mechanisms, this work concentrates on system operation when M2M and H2H data flows coexist in the network. In particular, we propose an integrated simulation-analytical framework to evaluate relevant performance characteristics (data transmission delays, blocking probabilities, etc.) with both Markov process based analysis and system-level simulations. Our results indicate that the proposed methodology demonstrates acceptable levels of convergence between analytical and simulations components, as well as becomes useful to characterize impacts of M2M/H2H coexistence on radio resource allocation in 3GPP LTE across a number of important M2M-centric scenarios.