Theory that Matters! Problem-based learning towards 5G Communication System and Standards

Theory that Matters! Problem-based learning towards 5G Communication System and Standard

Network-coded cooperation and multi-connectivity for massive content delivery

[EN] Massive content delivery is in the spotlight of the research community as both data traffic and the number of connected mobile devices are increasing at an incredibly fast pace. The enhanced mobile broadband (eMBB) is one of the main use cases for the fifth generation of mobile networks (5G), which focuses on transmitting greater amounts of data at higher data rates than in the previous generations, but also on increasing the area capacity (given in bits per second per square meter) and reliability. However, the broadcast and multicast implementation in 5G and presents several drawbacks such as unexpected disconnections and the lack of device-specific QoS guarantees. As a result, whenever the exact same content is to be delivered to numerous mobile devices simultaneously, this content must be replicated. Hence, the same number of parallel unicast sessions as users are needed. Therefore, novel systems that provide efficient massive content delivery and reduced energy consumption are needed. In this paper, we present a network-coded cooperation (NCC) protocol for efficient massive content delivery and the analytical model that describes its behavior. The NCC protocol combines the benefits of cooperative architectures known as mobile clouds (MCs) with Random Linear Network Coding (RLNC). Our results show the benefits of our NCC protocol when compared to the establishment of numerous parallel unicast sessions are threefold: offload data traffic from the cellular link, reduce the energy consumption at the cooperating users, and provide throughput gains when the cellular bandwidth is insufficient.This work was supported in part by the European Union's H2020 Research and Innovation Program under Grant H2020-MCSA-ITN-2016-SECRET 722424. The work of Vicent Pla and Jorge Martinez-Bauset was supported under Grant PGC2018-094151-B-I00 and Grant RED2018-102585-T (MCIU/AEI/FEDER,UE)Leyva-Mayorga, I.; Torre, R.; Pla, V.; Pandi, S.; Nguyen, GT.; Martínez Bauset, J.; Fitzek, FHP. (2020). Network-coded cooperation and multi-connectivity for massive content delivery. IEEE Access. 8:15656-15672. https://doi.org/10.1109/ACCESS.2020.29672781565615672

Hardware Acceleration for RLNC: A Case Study Based on the Xtensa Processor with the Tensilica Instruction-Set Extension

Random linear network coding (RLNC) can greatly aid data transmission in lossy wireless networks. However, RLNC requires computationally complex matrix multiplications and inversions in finite fields (Galois fields). These computations are highly demanding for energy-constrained mobile devices. The presented case study evaluates hardware acceleration strategies for RLNC in the context of the Tensilica Xtensa LX5 processor with the tensilica instruction set extension (TIE). More specifically, we develop TIEs for multiply-accumulate (MAC) operations for accelerating matrix multiplications in Galois fields, single instruction multiple data (SIMD) instructions operating on consecutive memory locations, as well as the flexible-length instruction extension (FLIX). We evaluate the number of clock cycles required for RLNC encoding and decoding without and with the MAC, SIMD, and FLIX acceleration strategies. We also evaluate the RLNC encoding and decoding throughput and energy consumption for a range of RLNC generation and code word sizes. We find that for GF ( 2 8 ) and GF ( 2 16 ) RLNC encoding, the SIMD and FLIX acceleration strategies achieve speedups of approximately four hundred fold compared to a benchmark C code implementation without TIE. We also find that the unicore Xtensa LX5 with SIMD has seven to thirty times higher RLNC encoding and decoding throughput than the state-of-the-art ODROID XU3 system-on-a-chip (SoC) operating with a single core; the Xtensa LX5 with FLIX, in turn, increases the throughput by roughly 25% compared to utilizing only SIMD. Furthermore, the Xtensa LX5 with FLIX consumes roughly three orders of magnitude less energy than the ODROID XU3 SoC