25 research outputs found

    Efficient Resource Allocation and Spectrum Utilisation in Licensed Shared Access Systems

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    Artificial Intelligence-aided OFDM Receiver: Design and Experimental Results

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    Orthogonal frequency division multiplexing (OFDM) is one of the key technologies that are widely applied in current communication systems. Recently, artificial intelligence (AI)-aided OFDM receivers have been brought to the forefront to break the bottleneck of the traditional OFDM systems. In this paper, we investigate two AI-aided OFDM receivers, data-driven fully connected-deep neural network (FC-DNN) receiver and model-driven ComNet receiver, respectively. We first study their performance under different channel models through simulation and then establish a real-time video transmission system using a 5G rapid prototyping (RaPro) system for over-the-air (OTA) test. To address the performance gap between the simulation and the OTA test caused by the discrepancy between the channel model for offline training and real environments, we develop a novel online training strategy, called SwitchNet receiver. The SwitchNet receiver is with a flexible and extendable architecture and can adapts to real channel by training one parameter online. The OTA test verifies its feasibility and robustness to real environments and indicates its potential for future communications systems. At the end of this paper, we discuss some challenges to inspire future research.Comment: 29 pages, 13 figures, submitted to IEEE Journal on Selected Areas in Communication

    Distributed MIMO for wireless sensor networks

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    Over the past decade, wireless sensor networks have gained more research attention for their potential applications in healthcare, defense, environmental monitoring, etc. Due to the strict energy limitation in the sensor node, techniques used for energy saving are necessary for this kind of network. MIMO technology is proven to be an effective method of increasing the channel capacity and supporting higher data rate under a fixed power budget and bit-error-rate requirement. So, wireless sensor networks and MIMO technology are combined and investigated in this thesis. The key contributions of this thesis are detailed below. Firstly, the extended total energy consumption equations for different transmission modes in cluster-based wireless sensor networks are derived. The transmitting energy consumption and the circuit energy consumption are taken into account in both intra-cluster and inter-cluster phases respectively. Secondly, a resource allocation framework is proposed for cluster-based cooperative MIMO on consideration of circuit energy. By introducing two adjusting parameters for the transmitting energy and the time slot allocation between intra-cluster and inter-cluster phases, this framework is designed to achieve the maximum data throughput of the whole system whilst maintaining the capacity and outage probability requirement in these two phases respectively. Thirdly, on comparison of various transmission modes in wireless sensor networks, a relatively energy-efficient mode switching framework is proposed for both single-hop and multi-hop transmissions. Based on the destination and the neighboring nodes’ path-loss, the source node can decide which transmission mode, SISO or cooperative MISO, single-hop or multi-hop, should be chosen. Conditions for each mode switching are investigated. The possible existing area of the cooperative nodes and the relaying nodes can be obtained from this framework

    Intelligent Sensing and Learning for Advanced MIMO Communication Systems

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