Towards Software-Defined Massive MIMO for 5G&amp;B Spectral-Efficient Networks

Lin, Shih‐Chun; Narasimhan, Harini

doi:10.1109/icc.2018.8422150

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…Applying the orthogonality of the MMSE estimate, the channel vector can be further decomposed as h i =ĥ i +h i , whereh i ∼ CN (0, A i − Φ i ) is the uncorrelated (and also statistically independent) estimation error [24,22].…”

Section: Minimum Mean-square Error (Mmse) Channel Estimationmentioning

confidence: 99%

Software-Defined architecture for QoS-Aware IoT deployments in 5G systems

et al. 2019

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Internet of Things (IoT), a ubiquitous network of interconnected objects, harvests information from the environments, interacts with the physical world, and uses the existing Internet infrastructure to provide services for information transfer and emerging applications. However, the scalability and Internet access fundamentally challenge the realization of a wide range of IoT applications. Based on recent developments of 5G system architecture, namely Sof-tAir, this paper introduces a new software-defined platform that enables dynamic and flexible infrastructure for 5G IoT communication. A corresponding sum-rate analysis is also carried out via an optimization approach for efficient data transmissions. First, the SoftAir decouples control plane and data plane for a software-defined wireless architecture and enables effective coordination among remote radio heads (RRHs), equipped with millimeter-wave (mmWave) frontend, for IoT access. Next, by introducing an innovative design of softwaredefined gateways (SD-GWs) as local IoT controllers in SoftAir, the wide diversity of IoT applications and the heterogeneity of IoT devices are easily accommodated. These SD-GWs aggregate the traffic from heterogeneous IoT devices and

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Section: Minimum Mean-square Error (Mmse) Channel Estimationmentioning

confidence: 99%

Software-Defined architecture for QoS-Aware IoT deployments in 5G systems

et al. 2019

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“…However, the Gbps rate is still an order of magnitude below that which is needed to serve popular streaming use-cases. To satisfy the urgent need of multimedia applications, there are two obvious ways of further improving the data rate: increasing the bandwidth used for communication, and improving spectral efficiency at which frequency bands are used [5], [6]. In light of data rate improvements, researchers have started to focus on the utilization of terahertz (THz)-bands for next-generation communications [7].…”

Section: Introductionmentioning

confidence: 99%

TULVCAN: Terahertz Ultra-broadband Learning Vehicular Channel-Aware Networking

Chen

Lin

Blasch

2021

Preprint

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Due to spectrum scarcity and increasing wireless capacity demands, terahertz (THz) communications at 0.1-10THz and the corresponding spectrum characterization have emerged to meet diverse service requirements in future 5G and 6G wireless systems. However, conventional compressed sensing techniques to reconstruct the original wideband spectrum with under-sampled measurements become inefficient as local spectral correlation is deliberately omitted. Recent works extend communication methods with deep learning-based algorithms but lack strong ties to THz channel properties. This paper introduces novel THz channel-aware spectrum learning solutions that fully disclose the uniqueness of THz channels when performing such ultrabroadband sensing in vehicular environments. Specifically, a joint design of spectrum compression and reconstruction is proposed through a structured sensing matrix and two-phase reconstruction based on high spreading loss and molecular absorption at THz frequencies. An end-to-end learning framework, namely compression and reconstruction network (CRNet), is further developed with the mean-square-error loss function to improve sensing accuracy while significantly reducing computational complexity. Numerical results show that the CRNet solutions outperform the latest generative adversarial network (GAN) realization with a much higher cosine and structure similarity measures, smaller learning errors, and 56\% less required training overheads. This THz Ultra-broadband Learning Vehicular Channel-Aware Networking (TULVCAN) work successfully achieves effective THz spectrum learning and hence allows frequency-agile access.

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