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This position paper introduces a Dynamic Data Driven Open Radio Access Network System (3D-O-RAN). The key objective of 3D-O-RAN is to support congested, contested and contaminated tactical settings where multimedia sensors, application constraints and operating wireless conditions may frequently change over space, time and frequency. 3D-O-RAN is compliant with the O-RAN specification for beyond 5G cellular systems to reduce costs and guarantee interoperability among vendors. Moreover, 3D-O-RAN integrates computational, sensing, and cellular networking components in a highly-dynamic, feedback-based, data-driven control loop. Specifically, 3D-O-RAN is designed to incorporate heterogeneous data into the network control loop to achieve a system-wide optimal operating point. Moreover, 3D-O-RAN steers the multimedia sensor measurement process in real time according to the required application needs and current physical and/or environmental constraints. 3D-O-RAN uses (i) a semantic slicing engine, which takes into account the semantic of the application to optimally compress the multimedia stream without losing in classification accuracy; (ii) a dynamic data driven neural network certification system that translates mission-level constraints into technical-level constraints on neural network latency/accuracy, and occupation of hardware/software resources. Realistic use-case scenarios of 3D-O-RAN in a tactical context demonstrate system performance. 

In this paper, we introduce a neural network (NN)-based symbol detection scheme for Wi-Fi systems and its associated hardware implementation in software radios. To be specific, reservoir computing (RC), a special type of recurrent neural network (RNN), is adopted to conduct the task of symbol detection for Wi-Fi receivers. Instead of introducing extra training overhead/set to facilitate the RC-based symbol detection, a new training framework is introduced to take advantage of the signal structure in existing Wi-Fi protocols (e.g., IEEE 802.11 standards), that is, the introduced RC-based symbol detector will utilize the inherent long/short training sequences and structured pilots sent by the Wi-Fi transmitter to conduct online learning of the transmit symbols. In other words, our introduced NN-based symbol detector does not require any additional training sets compared to existing Wi-Fi systems. The introduced RC-based Wi-Fi symbol detector is implemented on the software-defined radio (SDR) platform to further provide realistic and meaningful performance comparisons against the traditional Wi-Fi receiver. Over the air, experiment results show that the introduced RC based Wi-Fi symbol detector outperforms conventional Wi-Fi symbol detection methods in various environments indicating the significance and the relevance of our work.