On the integration of SIC and MIMO DoF for interference cancellation in wireless networks

Jalaian, Brian; Yuan, Xu; Shi, Yi; Hou, Y. Thomas; Lou, Wenjing; Midkiff, Scott F.; Dasari, Venkat R.

doi:10.1007/s11276-017-1472-7

Cited by 7 publications

(2 citation statements)

References 32 publications

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“…This diagram abstracts the underlying complexity shown in prior research (Nagaraj and Pasupathy 2017) that studied cross‐layer optimization and extended those notions to a system‐level model representing the architecture that exists in intelligent decision support systems (Liu et al 2015; Kaiwartya et al 2016; Beans 2018). Given a layered architecture such as shown in figure 2, which incorporates computational context in stochastic variables, tractable mathematical models can be developed that optimize the performance of assets and services with respect to decision objectives (Jalaeian, Zhu, Samani, and Motani 2016; Jalaian et al 2017).…”

Section: Optimization Under Uncertaintymentioning

confidence: 99%

Uncertain Context: Uncertainty Quantification in Machine Learning

2019

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Machine learning and artificial intelligence will be deeply embedded in the intelligent systems humans use to automate tasking, optimize planning, and support decision-making. However, many of these methods can be challenged by dynamic computational contexts, resulting in uncertainty in prediction errors and overall system outputs. Therefore, it will be increasingly important for uncertainties in underlying learning-related computer models to be quantified and communicated. The goal of this article is to provide an accessible overview of computational context and its relationship to uncertainty quantification for machine learning, as well as to provide general suggestions on how to implement uncertainty quantification when doing statistical learning. Specifically, we will discuss the challenge of quantifying uncertainty in predictions using popular machine learning models. We present several sources of uncertainty and their implications on statistical models and subsequent machine learning predictions.

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Section: Optimization Under Uncertaintymentioning

confidence: 99%

Uncertain Context: Uncertainty Quantification in Machine Learning

2019

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“…They studied a framework for the performance in SIC and proposed some general expressions for the successful transmission probabilities in SIC. For the Multiple-Input Multiple-Output (MIMO) degree-of-freedom model, Jalaian et al [19] eliminated loop interference through SIC technique. We find the SIC technology can make receive nodes receive more intended signals and the wireless networks work more powerfully.…”

Section: Introductionmentioning

confidence: 99%

Successive Interference Cancellation Based Throughput Optimization for Multi-Hop Wireless Rechargeable Sensor Networks

Zhang

Ding

et al. 2020

Sensors

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Wireless Sensor Networks are constrained by low channel utilization and limited battery capacity, so they are widely regarded as the mainly performance bottlenecks. In this paper, in order to improve channel utilization and prolong network lifetime, we investigate the cooperation of multi-hop Wireless Rechargeable Sensor Networks (WRSNs) with Successive Interference Cancellation (SIC) technology. In WRSNs, since the flow rate of each node is unknown, the power of the nodes is not constant. However, SIC will not work if the signal power levels at receive node cannot be sorted. To solve this issue, we first construct a minimum energy routing and unify the transmit rate to determine the transmit power. We can also obtain the time scheduling scheme after determining the routing and power. Next, we formulate an optimization problem, with the objective of maximizing the mobile charger’s vacation time over the rechargeable cycle. Finally, we provide a near-optimal solution and prove its feasible performance. Simulation results present that SIC can achieve the better upper boundary on throughput (compared to inference avoidance increasing about 180–450%) and no extra transmit and receive energy consumption in the multi-hop WRSNs.

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Frameworks of Non‐Orthogonal Multiple Access Techniques in Cognitive Radio Networks

Thakur¹,

Singh²

2021

Spectrum Sharing in Cognitive Radio Networks

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On the integration of SIC and MIMO DoF for interference cancellation in wireless networks

Cited by 7 publications

References 32 publications

Uncertain Context: Uncertainty Quantification in Machine Learning

Uncertain Context: Uncertainty Quantification in Machine Learning

Successive Interference Cancellation Based Throughput Optimization for Multi-Hop Wireless Rechargeable Sensor Networks

Frameworks of Non‐Orthogonal Multiple Access Techniques in Cognitive Radio Networks

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