Learning to Mitigate the FAR in Polar Code Blind Detection

The explosive growth of mobile multimedia services and applications are increasing the demand of access ability for the recent 5G networks. Non-orthogonal multiple access (NOMA) techniques have been recently proposed for 5G to improve access efficiency through allowing multiple users to share the same spectrum resources in a non-orthogonal way. Low Density Superposition Modulation (LDSM) is one of the NOMA techniques with potential to support high spectral efficiency and massive connectivity. In this paper, we utilize Bare-bone Particle Swarm Optimization (BBPSO) algorithm with extrinsic information transfer (EXIT) chart to design degree distribution of LDSM. An optimized design of LDSM scheme has better convergence performance compared with conventional sparse code multiple access (SCMA) and pattern division multiple access (PDMA) scheme over fading channel. Furthermore, the designed irregular LDSM can significantly improve the overall system performance and edge user performance under the nearfar effect scenario. Simulation results validate that the proposed optimized LDSM can achieve about 1.0 dB gains compared to both SCMA and PDMA schemes.

Section: Optimized Low Density Superposition Modulation Based On Bbpso-exit a Exit Chart Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimized Low Density Superposition Modulation for 5G Mobile Multimedia Wireless Networks

Jiang

2019

“…With the help of data sensing, energy harvesting, wireless communication and caching technologies, the environment data as well as the real-time characteristics associated to the transmission lines can be sensed and transmitted to the edge/cloud servers [3]- [8]. Particularly, the incoming era of 5G and Internet of Things will not only improve the traffic diversity but also effectively reduces the data transmission delay, which will significant enhance the safety monitoring performance compared to the traditional wireless sensor network [9]- [13]. For example, the multimedia data can be introduced to the transmission line monitoring with the help of 5G D2D technology.…”

Section: Introductionmentioning

confidence: 99%

Data Collection Study Based on Spatio-Temporal Correlation in Event-Driven Sensor Networks

Jiang

Pang

et al. 2019

In this paper, a spatial-temporal correlation aware data collection mechanism is proposed for a event-driven sensor network in terms of the realistic requirements such as real-time data sensing and dynamic network topology. Firstly, in order to reduce the path congestion and the data transmission delay, the perceived data states are classified based on binary representation. Secondly, a low cost manner is studied to aggregate the perceived data at the representative nodes and aggregation nodes respectively based on the spatial-temporal correlation. Furthermore, the best data collection path is obtained by carrying out a particle swarm optimization (PSO). Simulation results validate that the proposed algorithm can effectively reduce the amount of data transmissions in the network event area. Besides, the proposed mechanism also has advantages in reducing the delay and energy consumption. INDEX TERMS Event-driven sensor network, spatio-temporal correlation, data fusion, data collection.

“…With the enhancement of 5G technologies, such as network slice [3], D2D [4], [5], physical layer coding [6], [7] and other IoT technologies [8], [9], many streaming media…”

Section: Introductionmentioning

confidence: 99%

Network-Assisted Neural Adaptive Naked-Eye 3D Video Streaming Over Wireless Networks

Liu

Wang

et al. 2019

High quality transmission of Virtual Reality (VR) video depends strictly on bandwidth and delay requirements. It becomes possible with the maturity and commercialization of 5G technology. However, to transmit VR video streaming over wireless communication system, we must cope with the challenge that fluctuation exists in the wireless channel conditions. For example, available channel bandwidth, packet loss rate, and interference level may vary with time due to channel fading and user's mobility. The problem is more prominent when transmitting naked-eye 3D video which generally consists of multiple viewpoints with different resolutions. In order to optimize the quality of experience (QoE) of watching naked-eye VR video over wireless networks, this paper proposes a network-assisted neural adaptive video streaming algorithm (NAVSA). Specifically, we present a modified QoE function to describe the quality of naked-eye 3D streaming quantitatively, which not only considers the quality of naked-eye 3D video itself, but also considers the phenomenon of rebuffering and video fluctuation that occurs during video transmission. Next, with the network-assisted feedback, the physical layer information, the buffer occupancy of the video client, and the size of the next video chunk are collected to train a reinforcement learning model. Based on dynamic adaptive streaming over HTTP (DASH), the model can automatically choose appropriate viewpoints and resolutions corresponding to the current condition of the wireless networks such that the network capacity can be fully explored. To verify the performance of our proposed NAVSA, we simulate 3 naked-eye 3D video application scenarios on the NS3 platform. The results show that the performance of NAVSA is about 5∼8% better than some state-of-the-art algorithms in wireless networks.