The ever-increasing demand for data-hungry wireless services and rapid proliferation of wireless devices in sub-6 GHz band have pushed the current wireless technologies to a breaking point, necessitating efficient and intelligent strategies to utilize scarce communication resources. This thesis aims at leveraging novel communication frameworks, artificial intelligence techniques, and synergies between them in bringing efficiency and intelligence to the next generation of wireless networks.In the first chapter of this thesis, we propose a novel spectrum sharing scheme to address spectrum shortage, a fundamental issue in current and future wireless networks. Our proposed scheme enables transparent spectrum utilization for a small cognitive radio network by leveraging two interference management techniques that are not reliant on inter-network coordination, fine-grained synchronization, and knowledge about other occupants of the spectrum. We further extend this idea in the second chapter of this thesis and enable concurrent device-to-device and cellular communications in cellular networks where the base station and wireless devices exploit interference management techniques to avoid causing interference to each other, making concurrent spectrum utilization possible for both cellular and device-to-device communications. In the third chapter, to enhance spectral efficiency, connectivity, and throughput of Wireless Local Area Networks (WLAN), we propose a non-orthogonal multiplexing scheme (NOMA). In our proposed scheme, the access point (AP) is equipped with a novel precoder design and user grouping which are tailored based on the requirements of power-domain NOMA. Also, a novel successive interference cancellation technique is designed for users which does not require channel estimation to decode the signals and is more resilient to interference compared to the existing techniques. The second part of this thesis focuses on taking advantage of artificial intelligence for solving communication and networking challenges and also taking advantage of novel communication frameworks to let future wireless networks indulge intelligence-oriented networking and resource management. In the fourth chapter, we propose a new solution to solve a long-standing issue ahead of multi-user multiple-input multiple-output (MU-MIMO) communications in WLANs, which is the large sounding overhead for acquiring the channel state information (CSI). Our learning-based solution includes an automated mechanism that enables access points to collect, clear, and balance dataset, and also deep neural networks to compress CSI and reduce the airtime overhead for channel acquisition. However, with provisioning concurrent MU-MIMO and orthogonal frequency division multiple access (OFDMA) in the new generation of WLANs, not only the sounding overhead problem becomes more acute, but it also marries with a complex resource allocation problem which makes designing a practical enabler of MU-MIMO-OFDMA transmissions necessary for WLANs. In the fifth chapter of this thesis, we propose DeepMux, which comprises a deep-learning-based channel sounding and a deep-learning-based resource allocation both of which reside in access points and impose no computational/communication burden on users, enabling efficient downlink MU-MIMO-OFDMA transmissions in WLANs. We finally design a communication framework for accelerating federated learning in future intelligent transportation systems, where heterogeneous capabilities and mobility of users along with limited available bandwidth for communications are huge obstacles toward making the network intelligent in a distributed manner. With the aid of a deadline-driven scheduler and asynchronous uplink multi-user MIMO, our proposed solution reduces data loss at vehicles in a dynamic vehicular environment, making a concrete step toward the practical adoption of federated learning in future transportation systems.
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