• Murat Temiz

Student thesis: Phd


During the last decade, the number of connected devices to mobile communication networks and their data-rate demands have enormously increased. As a consequence, efficient and intelligent utilization of frequency resources is required to satisfy these demands for future communication networks due to the limited available frequency spectrum. Meanwhile, growing demand for radar sensing applications has been recently observed especially in vehicular networks where sensing and communications are two paramount technologies to empower safe, efficient, and smart vehicular systems. Radar sensing also necessitates wideband frequency resources to accurately estimate the target parameters such as range, velocity and angle. Massive multiple-input-multiple-output (MIMO) is the most promising solution to efficiently utilize available frequency spectrum since it enables the base station (BS) or access point (AP) to simultaneously communicate with multiple users (UEs) in the same time-frequency resources. Furthermore, dual-functional radar and communication (RadCom) platforms can also enhance the energy and spectral efficiencies by utilising the same hardware and frequency resources for radar sensing and communications. Motivated by the aforementioned advantages of massive MIMO communication and RadCom systems, this thesis proposes techniques and algorithms to enhance the spectral and energy efficiencies of massive MIMO networks and enable joint radar sensing and communications. Firstly, we investigated the impact of the array geometry on the performance of massive MIMO networks and proposed an antenna array geometry which reduces the channel correlation among UEs and enhances the spectral efficiency of the network. The performance of the proposed array geometry is examined by simulations and measurements in a prototyped indoor massive MIMO network. Moreover, based on the measurement data, a practical power control algorithm for uplink massive MIMO networks is proposed. Secondly, we examined low-resolution analogue-to-digital converters (ADCs) in massive MIMO networks in order to improve the energy efficiency of the network and revealed the optimum modulation schemes for each ADC resolution. Furthermore, the trade-off between energy and spectral efficiencies with regards to ADC resolutions is investigated, and employing a root-raised-cosine filter is studied to mitigate the impact of the coarse quantization. Thirdly, we introduced techniques and algorithms to enable joint massive MIMO communication and OFDM radar sensing by exploiting the interference between radar and communication subsystems. For joint downlink communications and radar sensing, a novel RadCom architecture with an interference utilization precoder is proposed, and its communication capacity and radar detection accuracy are mathematically analysed and verified via simulations. This novel RadCom architecture is shown to provide a substantial capacity improvement while enabling simultaneous radar sensing. Furthermore, the proposed RadCom precoder has been optimized via convex optimization to improve the energy and spectral efficiencies of the network. The capacity of the optimized precoders are analysed and compared to the other techniques. Lastly, an uplink RadCom architecture, which utilizes successive interference cancellation (SIC) and self-interference (SI) cancellation techniques, is proposed and comprehensively analysed. Mathematical analysis and simulation results show that the proposed uplink RadCom architecture can efficiently sense the environment while communicating with multiple uplink UEs.
Date of Award1 Aug 2021
Original languageEnglish
Awarding Institution
  • The University of Manchester
SupervisorLaith Danoon (Supervisor) & Emad Alsusa (Supervisor)


  • Massive MIMO Communication
  • OFDM Radar Sensing
  • Antenna Arrays
  • Microwave Antennas
  • MIMO Radar
  • Signal Processing

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