Efficient TTI for 3G Multimedia Applications

Time transmission interval (TTI) or outer block interleaving is an important task for the implementation of UMTS turbo coding in flat Rayleigh fading environment. An efficient TTI choice can save computational complexity. However, different multimedia scenarios are investigated using the maximum UMTS frame length, and simulation results are presented for the four possible outer block interleaver configurations in the case of flat Rayleigh fading channel. It is shown that different operating environments require an appropriate TTI in terms of bit error rate (BER) performance for the following data rates: 28.8 kbps, 64 kbps, 144 kbps, 384 kbps, and 2 Mbps

3GPP 5G V2X Scenarios: Performance of QoS Parameters Using Turbo Codes

Cellular vehicle-to-everything (C-V2X) communication has recently gained attention in industry and academia. Different implementation scenarios have been derived by the 3rd Generation Partnership Project (3GPP) 5th Generation (5G) Vehicle-to-Everything (V2X) standard, Release 16. Quality of service (QoS) is important to achieve reliable communication and parameters which have to be considered are reliability, end-to-end latency, data rate, communication range, throughput and vehicle density for an urban area. However, it would be desirable to design a dynamic selecting system (with emphasis on channel coding parameters selection) so that all QoS parameters are satisfied. Having this idea in mind, in this work we examine nine V2X implementation scenarios using Long Term Evolution (LTE) turbo coding with a geometry−based efficient propagation model for vehicle-to-vehicle communication (GEMV), where we consider the above QoS parameters for SOVA, log-MAP and max-log-MAP decoding algorithms. Our study is suitable for 3GPP cooperative sensing, for the eight scenarios considering medium and large signal-noise-ratio (SNR) values. The proposed model is sustainable despite a doubled data rate, which results in a minimal bit error rate (BER) performance loss up to 1.85 dB. In this case tripling the data rate results in a further 1 dB loss. Moreover, a small loss up to 0.4 dB is seen for a vehicle speed increase from 60 km/h to 100 km/h. Finally, increasing vehicle density has no effect on the implemented 3GPP scenario considering end-to-end latency, irrespectively from the decoding algorithm

3GPP 5G V2X Scenarios: Performance of QoS Parameters Using Turbo Codes

Cellular vehicle-to-everything (C-V2X) communication has recently gained attention in industry and academia. Different implementation scenarios have been derived by the 3rd Generation Partnership Project (3GPP) 5th Generation (5G) Vehicle-to-Everything (V2X) standard, Release 16. Quality of service (QoS) is important to achieve reliable communication and parameters which have to be considered are reliability, end-to-end latency, data rate, communication range, throughput and vehicle density for an urban area. However, it would be desirable to design a dynamic selecting system (with emphasis on channel coding parameters selection) so that all QoS parameters are satisfied. Having this idea in mind, in this work we examine nine V2X implementation scenarios using Long Term Evolution (LTE) turbo coding with a geometry−based efficient propagation model for vehicle-to-vehicle communication (GEMV), where we consider the above QoS parameters for SOVA, log-MAP and max-log-MAP decoding algorithms. Our study is suitable for 3GPP cooperative sensing, for the eight scenarios considering medium and large signal-noise-ratio (SNR) values. The proposed model is sustainable despite a doubled data rate, which results in a minimal bit error rate (BER) performance loss up to 1.85 dB. In this case tripling the data rate results in a further 1 dB loss. Moreover, a small loss up to 0.4 dB is seen for a vehicle speed increase from 60 km/h to 100 km/h. Finally, increasing vehicle density has no effect on the implemented 3GPP scenario considering end-to-end latency, irrespectively from the decoding algorithm

Reconfigurable Turbo Decoding for 3G Applications.

NoSoftware radio and reconfigurable systems represent reconfigurable functionalities of the radio interface. Considering turbo decoding function in battery-powered devices like 3GPP mobile terminals, it would be desirable to choose the optimum decoding algorithm: SOVA in terms of latency, and log-MAP in terms of performance. In this paper it is shown that the two algorithms share common operations, making feasible a reconfigurable SOVA/log-MAP turbo decoder with increased efficiency. Moreover, an improvement in the performance of the reconfigurable architecture is also possible at minimum cost, by scaling the extrinsic information with a common factor. The implementation of the improved reconfigurable decoder within the 3GPP standard is also discussed, considering different scenarios. In each scenario various frame lengths are evaluated, while the four possible service classes are applied. In the case of AWGN channels, the optimum algorithm is proposed according to the desired quality of service of each class, which is determined from latency and performance constraints. Our analysis shows the potential utility of the reconfigurable decoder, since there is an optimum algorithm for most scenarios

Sensor Topology Optimization in Dense IoT Environments by Applying Neural Network Configuration

In dense IoT deployments of wireless sensor networks (WSNs), sensor placement, coverage, connectivity, and energy constraints determine the overall network lifetime. In large-size WSNs, it is difficult to maintain a trade-off among these conflicting constraints and, thus, scaling is difficult. In the related research literature, various solutions are proposed that attempt to address near-optimal behavior in polynomial time, the majority of which relies on heuristics. In this paper, we formulate a topology control and lifetime extension problem regarding sensor placement, under coverage and energy constraints, and solve it by applying and testing several neural network configurations. To do so, the neural network dynamically proposes and handles sensor placement coordinates in a 2D plane, having the ultimate goal to extend network lifetime. Simulation results show that our proposed algorithm improves network lifetime, while maintaining communication and energy constraints, for medium- and large-scale deployments