Olga Galinina

Cellular traffic offloading onto network-assisted device-to-device connections

While operators have finally started to deploy fourth generation broadband technology, many believe it will still be insufficient to meet the anticipated demand in mobile traffic over the coming years. Generally, the natural way to cope with traffic acceleration is to reduce cell size, and this can be done in many ways. The most obvious method is via picocells, but this requires additional CAPEX and OPEX investment to install and manage these new base stations. Another approach, which avoids this additional CAPEX/OPEX, involves offloading cellular traffic onto direct D2D connections whenever the users involved are in proximity. Given that most client devices are capable of establishing concurrent cellular and WiFi connections today, we expect the majority of immediate gains from this approach to come from the use of the unlicensed bands.

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.

5G Multi-RAT LTE-WiFi Ultra-Dense Small Cells: Performance Dynamics, Architecture, and Trends

The ongoing densification of small cells yields an unprecedented paradigm shift in user experience and network design. The most notable change comes from cellular rates being comparable to next-generation WiFi systems. Cellular-to-WiFi offloading, the standard modus operandi of recent years, is therefore shifting towards a true integration of both technology families. Users in future 5G systems will thus likely be able to use 3GPP, IEEE, and other technologies simultaneously, so as to maximize their quality of experience. To advance this high-level vision, we perform a novel performance analysis specifically taking the system-level dynamics into account and thus giving a true account on the uplink performance gains of an integrated multi radio access technology (RAT) solution versus legacy approaches. Further, we advocate for an enabling architecture that embodies the tight interaction between the different RATs, as we lay out a standardization roadmap able to materialize the envisaged design. 3GPP-compliant simulations have also been carried out to corroborate the rigorous mathematical analysis and the superiority of the proposed approach.

Analyzing Assisted Offloading of Cellular User Sessions onto D2D Links in Unlicensed Bands

For the past years, the analysts have been predicting a tremendous and continuous increase in mobile traffic, causing much of industry and academia to seek out any and all methods to increase wireless network capacity. In this paper, we investigate one such method, cellular data offloading onto direct connections between proximate user devices, which has been shown to provide significant wireless capacity gains. To do so, we formulate a new system model that couples a cellular network in licensed bands and a device-to-device (D2D) network in unlicensed bands. We propose that devices be continually associated with the cellular base station and use this connectivity to help manage their direct connections in unlicensed spectrum. In particular, we demonstrate that assisted offloading of cellular user sessions onto the D2D links improves the degree of spatial reuse and reduces the impact of interference. In this study, a session is a real-time flow of data from one user to another, which adheres to a Poisson point process (PPP). By contrast to a throughput- or capacity-centric system view, the application of PPP enables formulations where entire user sessions, rather than singular data packets, are arriving at random and leaving the system after being served. The proposed methodology is flexible enough to accommodate practical offloading scenarios, network selection algorithms, quality of service measures, and advanced wireless technologies. In this study, we are primarily interested in evaluating the data session blocking probability in dynamically loaded cellular and D2D networks, but given the importance of energy efficiency for mobile devices, we are also interested in characterizing the energy expenditure of a typical data session in these different networks. First with our advanced analytical methodology and then with our detailed system-level simulator, we evaluate the performance of network-assisted data session offloading from cellular to D2D connections under a variety of conditions. This analysis represents a useful tool in the development of practical offloading schemes and ongoing standardization efforts.

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

Energy-Efficient Client Relay Scheme for Machine-to-Machine Communication

In this paper, we consider a wireless cellular network capable of supporting Machine-to-Machine (M2M) applications. According to the recent IEEE 802.16p proposals, a wireless M2M device may act as an aggregation point and communicate data packets on behalf of the other M2M devices, which may lack a cellular interface or have a poor communication link to the network. We propose a client relay scheme to improve the link reliability and energy efficiency for devices with weak links. Performance of the proposed scheme is evaluated through analysis and simulation across several metrics covering client throughput, latency, and energy consumption. Our analytical approach is a novel queueing model that captures realistic traffic arrival patterns borrowed from the evaluation methodology. It is shown that the obtained analytical results demonstrate excellent agreement with simulation. We also conclude that the proposed client relay scheme may save power for devices with poor communication link.

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.