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Distributed Precoding For Decentralized Estimation In Coexisting IoT Networks
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  • Navneet Garg ,
  • Tharmalingam Ratnarajah ,
  • Venkata Mani Vakamulla ,
  • Mathini Sellathurai
Navneet Garg
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Tharmalingam Ratnarajah
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Venkata Mani Vakamulla
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Mathini Sellathurai
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Abstract

With the increasing number of applications of internet-of-things (IoT) devices, co-channel interference is unavoidable among coexisting wireless networks, where multiple IoT devices transmit their observations to their respective des tinations (access points (APs)). In this scenario, we present joint precoding and power allocation solution to minimize the mean squared error (MSE), while satisfying the power constraint at each IoT device. In this regard, first, the necessary conditions for the feasibility for the joint problem is obtained for successful interference mitigation. Subsequently, an MSE minimization based iterative algorithm is derived and analyzed for convergence. Analysis  shows that the MSE performance or the signal-to-noise-plus-interference-ratio (SINR) at the APs is limited by the observation signal-to-noise-ratio (SNR). It is also inferred that to avoid the MSE saturation at AP, the transmit power at  IoT devices should be scaled proportional to and must be less than the observation SNR. We compare the performance of our solution with two classical methods, namely, the minimum variance distortionless precoding (MVDP) and interference alignment (IA) methods, which are modified for the given coexisting scenario. Simulations verify the above results, and show that MSE based algorithm converges globally with robustness to initializations and provides the better precoders and power allocation as compared to MVDP’s and IA’s.