5G Communications

5G Communications

The demand for video services in mobile networks is rapidly increasing. It is expected that video will account for more than 69% of mobile data traffic by 2018. For this reason group-oriented and on demand services will play a key role in the future wireless systems, posing new challenges in the design of techniques to improve the throughput and the delays to provide those services. Video traffic need to be carefully treated considering the time sensitiveness of many multimedia applications (e.g., chat or live streaming). LTE allow increasing bitrates, dedicated multicast channel for video downlink, cooperative communications, carrier aggregation, and other enabling technologies, making it a valid candidate for video demands. It is expected that in these new scenarios, the broadband technologies should frequently either switch to multicast mode or converge with broadcasting technologies in order to satisfy the expected challenging spectral efficiency required due to the video and associated data bitrate requirements of this new applications.  The MCLab investigates these issues in the following areas: Multimedia Broadcast and Multicast services in LTE netorks, multicast grouping algorithms, network balancing.

  • MBMS

MClab researchers are comparing the benefits and disadvantages of different approaches proposed for managing multicast services. 3rd Generation Partnership Project (3GPP) introduced evolved Multimedia Broadcast and Multicast services (MBMS), to allow LTE to provide data transmissions from a single source to multiple devices. MBMS utilizes a common channel to send the same data to multiple receivers, thereby minimizing the utilization of network resources. Amongst all the options possible in LTE, the division of potential receivers in different groups with different available SNR values is a well-accepted technique for enabling a more efficient exploitation of the spectral resources. This technique is known as multicast grouping (MG). Different MG algorithms are being studied considering their performance in real urban and suburban scenarios for pedestrian and vehicular users.

  • Network Balancing

MClab is developing a novel algorithm that performs a real time load balancing over heterogeneous wireless networks using a dynamic MEW function based on the user’s characteristics. MEW introduces the Signal-to-Interference-plus-Noise Ratio (SINR) and monetary cost to select the candidate network. The presence of heterogeneity of available technologies makes critical the selection of the best candidate network ensuring the higher QoS, and avoiding network saturation that would significantly decrease an acceptable user experience. One of the possible solutions is performing the traffic balancing across heterogeneous networks, making sure that the available networks for a given user at a given time are not overloaded. Currently, most of the load balancing algorithms carry out the network selection by combining the power of the received signal, the throughput, the packet delay, the monetary cost for the user, and the energy consumption.

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