• Thursday, 15 June, 9:00-10:30, Room Library Auditorium
  • Session Chair: Babak Hossein Khalaj (Sharif University of Technology, Iran)



09:00 Vehicle Clustering for Improving Enhanced LTE-V2X Network Performance

Petri Luoto (University of Oulu, Finland); Mehdi Bennis (Centre of Wireless Communications, University of Oulu, Finland); Pekka Pirinen (University of Oulu, Finland); Sumudu Samarakoon (Centre for Wireless Communications, University of Oulu, Finland); Kari Horneman (Nokia & Bell Labs, Finland); Matti Latva-aho (UoOulu, Finland)

Vehicle-to-Everything (V2X) communication holds the promise for improving road safety and reducing road accidents by enabling reliable and low latency services for vehicles. Vehicles are among the fastest growing type of connected devices. Therefore, there is a need for V2X communication, i.e., passing of information from Vehicle-to-Vehicle (V2V) or Vehicle-to-Infrastructure (V2I) and vice versa. In this paper, we focus on both V2I and V2V communication in a multi-lane freeway scenario, where coverage is provided by the Long Term Evolution Advanced (LTE-A) road side unit (RSU) network. Here, we propose a mechanism to offload vehicles with low signal-to-interference-plus-noise ratio (SINR) to be served by other vehicles, which have much higher quality link to the RSU. Furthermore, we analyze the improvements in the probabilities of achieving target throughputs and the performance is assessed through extensive system-level simulations. Results show that the proposed solution offloads low quality V2I links to stronger V2V links, and further increases successful transmission probability from 93% to 99.4%.


09:18 On the Performance of Ultra-Reliable Decode and Forward Relaying Under the Finite Blocklength

Parisa Nouri and Hirley Alves (University of Oulu, Finland); Matti Latva-aho (UoOulu, Finland)

In this paper, we examine the performance of the decode-and-forward (DF) relaying protocols with finite blocklength (FB). We provide the overall outage probability of three distinct DF relaying protocols, where the channels are assumed to be quasi static Rayleigh fading. More importantly, we derive the closed form expressions of the outage probability in the three relaying scenarios. We illustrate protocols where the cooperative communications outperform the direct transmission (DT). In addition, we compare the operating efficiency of the cooperative schemes in the ultra-reliable (UR) region.


09:36 Coordinated Multi-Cell Resource Allocation for 5G Ultra-Reliable Low Latency Communications

Vesa Hytönen (Magister Solutions Ltd., Finland); Zexian Li (Nokia Bell Labs, Finland); Beatriz Soret (Nokia Bell Labs, Denmark); Vuokko Nurmela (Nokia Bell Labs, Finland)

The coming 5G cellular communication system is envisioned to support a wide range of new use cases on top of regular cellular mobile broadband services. One of the 5G usage scenarios is ultra-reliable low-latency communications (URLLC). It has been predicted that URLLC will play an essential role to enable wireless communication for emerging new services and applications such as factory automation, remote manipulation, autonomous driving and tactile Internet, to name a few. The two key performance metrics related to URLLC are latency and reliability. In this paper three coordinated multi-cell resource allocation methods for 5G URLLC are presented in a typical indoor environment. From the simulation results, it can be observed that effectively handling inter-cell interference can lead to significant performance improvement in terms of reliability without bringing any degradation according to latency performance.


09:54 Comparison of Different Beamtraining Strategies from a Rate-Positioning Trade-Off Perspective

Jani Saloranta (University of Oulu & Centre for Wireless Communications, Finland); Giuseppe Destino (University of Oulu, Finland); Henk Wymeersch (Chalmers University of Technology, Sweden)

In next generation of mobile networks, the 5G, millimeter-wave communication is considered one of the key technologies. It allows high-data rate as well as the utilization of large antennas for massive MIMO and beamforming. However, it is mandatory that transmitter and receiver perform a training of their beams in order to gain all the benefits of a large array gain. In this paper, we study the impact of the beamtraining overhead on the data rate when an exhaustive or hierarchical strategy is used. Also, we show that the beamtraining phase can be used for positioning and, in this regard, we study the trade-off between positioning and data-rate.


10:12 Lousy Processing Increases Energy Efficiency in Massive MIMO Systems

Sara Gunnarsson and Micaela Bortas (Lund University, Sweden); Yanxiang Huang (IMEC & KU Leuven, Belgium); Cheng-Ming Chen (KU Leuven, Belgium); Liesbet Van der Perre (KUL, Belgium); Ove Edfors (Lund University, Sweden)

Massive MIMO (MaMIMO) is a key technology for 5G wireless communication, enabling large increase in both spectral and energy efficiency at the same time. Before it can be deployed, it is important to find efficient implementation strategies. Because of the many antennas, an essential part of decreasing complexity, and further improving energy efficiency, is optimization of the digital signal processing (DSP) in the per-antenna functions. Assuming an orthogonal frequency-division multiplexing (OFDM) based MaMIMO system, this paper explores coarse quantization in the per-antenna digital transmit filters and inverse fast Fourier transforms (IFFTs) and evaluates it in terms of performance and complexity savings. Results show that DSP complexity can be greatly reduced per-antenna, and therefore significant power savings can be achieved, with limited performance degradation. More specifically, when going towards MaMIMO and therefore increasing the number of antennas from 8 to 64, it is possible to reduce the complexity in each transmit filter by 55%. Also, when using 6 bits to represent the input signal and 6 bits for the filter coefficients, this results in an SNR degradation of less than 0.5 dB compared to floating-point performance. Consequently, we conclude that the overall system energy greatly benefits from lousy per-antenna processing.