Advanced Synthetic Aperture Radar Based on Digital Beamforming and Waveform Diversity

This paper introduces innovative SAR system concepts for the acquisition of high resolution radar images with wide swath coverage from spaceborne platforms. The new concepts rely on the combination of advanced multi-channel SAR front-end architectures with novel operational modes. The architectures differ regarding their implementation complexity and it is shown that even a low number of channels is already well suited to significantly improve the imaging performance and to overcome fundamental limitations inherent to classical SAR systems. The more advanced concepts employ a multidimensional encoding of the transmitted waveforms to further improve the performance and to enable a new class of hybrid SAR imaging modes that are well suited to satisfy hitherto incompatible user requirements for frequent monitoring and detailed mapping. Implementation specific issues will be discussed and examples demonstrate the potential of the new techniques for different remote sensing applications

Energy efficient design of an adaptive switching algorithm for the iterative-MIMO receiver

An efficient design dedicated for iterative-multiple-input multiple-output (MIMO) receiver systems is now imperative in our world since data demands are increasing tremendously in wireless networks. This puts a massive burden on the signal processing power especially in small receiver systems where power sources are often shared or limited. This thesis proposes an attractive solution to both the wireless signal processing and the architectural implementation design sides of the problem. A novel algorithm, dubbed the Adaptive Switching Algorithm, is proven to not only save more than a third of the energy consumption in the algorithmic design, but is also able to achieve an energy reduction of more than 50% in terms of processing power when the design is mapped onto state-of-the-art programmable hardware. Simulations are based in MatlabTM using the Monte Carlo approach, where multiple additive white Gaussian noise (AWGN) and Rayleigh fading channels for both fast and slow fading environments were investigated. The software selects the appropriate detection algorithm depending on the current channel conditions. The design for the hardware is based on the latest field programmable gate arrays (FPGA) hardware from Xilinx R , specifically the Virtex-5 and Virtex-7 chipsets. They were chosen during the experimental phase to verify the results in order to examine trends for energy consumption in the proposed algorithm design. Savings come from dynamic allocation of the hardware resources by implementing power minimization techniques depending on the processing requirements of the system. Having demonstrated the feasibility of the algorithm in controlled environments, realistic channel conditions were simulated using spatially correlated MIMO channels to test the algorithm’s readiness for real-world deployment. The proposed algorithm is placed in both the MIMO detector and the iterative-decoder blocks of the receiver. When the final full receiver design setup is implemented, it shows that the key to energy saving lies in the fact that both software and hardware components of the Adaptive Switching Algorithm adopt adaptivity in the respective designs. The detector saves energy by selecting suitable detection schemes while the decoder provides adaptivity by limiting the number of decoding iterations, both of which are updated in real-time. The overall receiver can achieve more than 70% energy savings in comparison to state-of-the-art iterative-MIMO receivers and thus it can be concluded that this level of ‘intelligence’ is an important direction towards a more efficient iterative-MIMO receiver designs in the future

Symbol-Level Multiuser MISO Precoding for Multi-level Adaptive Modulation

Symbol-level precoding is a new paradigm for multiuser downlink systems which aims at creating constructive interference among the transmitted data streams. This can be enabled by designing the precoded signal of the multiantenna transmitter on a symbol level, taking into account both channel state information and data symbols. Previous literature has studied this paradigm for MPSK modulations by addressing various performance metrics, such as power minimization and maximization of the minimum rate. In this paper, we extend this to generic multi-level modulations i.e. MQAM and APSK by establishing connection to PHY layer multicasting with phase constraints. Furthermore, we address adaptive modulation schemes which are crucial in enabling the throughput scaling of symbol-level precoded systems. In this direction, we design signal processing algorithms for minimizing the required power under per-user SINR or goodput constraints. Extensive numerical results show that the proposed algorithm provides considerable power and energy efficiency gains, while adapting the employed modulation scheme to match the requested data rate

Adaptive Transmission Schemes for Spectrum Sharing Systems: Trade-offs and Performance Analysis

Cognitive radio (CR) represents a key solution to the existing spectrum scarcity problem. Under the scenario of CR, spectrum sharing systems allow the coexistence of primary users (PUs) and secondary users (SUs) in the same spectrum as long as the interference from the secondary to the primary link stays below a given threshold. In this thesis, we propose a number of adaptive transmission schemes aiming at improving the performance of the secondary link in these systems while satisfying the interference constraint set by the primary receiver (PR). In the proposed techniques, the secondary transmitter (ST) adapts its transmission settings based on the availability of the channel state information (CSI) of the secondary and the interference links. In this context, these schemes offer different performance tradeoffs in terms of spectral efficiency, energy efficiency, and overall complexity. In the first proposed scheme, power adaptation (PA) and adaptive modulation (AM) are jointly used with switched transmit diversity in order to increase the capacity of the secondary link while minimizing the average number of antenna switching. Then, the concept of minimum-selection maximum ratio transmission (MS-MRT) is proposed as an adaptive variation of maximum ratio transmission (MRT) in a spectrum sharing scenario in order to maximize the capacity of the secondary link while minimizing the average number of transmit antennas. In order to achieve this performance, MS-MRT assumes that the secondary's CSI (SCSI) is perfectly known at the ST, which makes this scheme challenging from a practical point of view. To overcome this challenge, another transmission technique based on orthogonal space time bloc codes (OSTBCs) with transmit antenna selection (TAS) is proposed. This scheme uses the full-rate full-diversity Alamouti scheme in an underlay CR scenario in order to maximize the secondary's transmission rate. While the solutions discussed above offer a considerable improvement in the performance of spectrum sharing systems, they generally experience a high overall system complexity and are not optimized to meet the tradeoff between spectral efficiency and energy efficiency. In order to address this issue, we consider using spatial modulation (SM) in order to come with a spectrum sharing system optimized in terms spectral efficiency and energy efficiency. Indeed, SM can be seen as one of the emerging and promising new technologies optimizing the communication system while reducing the energy consumption thanks to the use of a single radio frequency (RF) chain for transmission. In this context, we propose the adaptive spatial modulation (ASM) scheme using AM in order to improve the spectral efficiency of SM. We also extend ASM to spectrum sharing systems by proposing a number of ASM-CR schemes aiming at improving the performance of these systems in terms of spectral efficiency and energy efficiency. While the use of a single RF-chain improves the energy efficiency of the above schemes, the RF-chain switching process between different transmissions comes with additional complexity and implementation issues. To resolve these issues, we use the concept of reconfigurable antennas (RAs) in order to improve the performance of space shift keying (SSK). In this context, employing RAs with SSK instead of conventional antennas allows for implementing only one RF chain and selecting different antenna-states for transmission without the need for RF switching. Moreover, the reconfigurable properties of RAs can be used as additional degrees of freedom in order to enhance the performance of SSK in terms of throughput, system complexity, and error performance. These RAs-based schemes are also extended to spectrum sharing systems in order to improve the capacity of the secondary link while reducing the energy consumption and the implementation complexity of the SU. In summary, we propose in this thesis several adaptive transmission schemes for spectrum sharing systems. The performance of each of these schemes is confirmed via Monte-Carlo simulations and analytical results and is shown to offer different tradeoffs in terms of spectral efficiency, energy efficiency, reliability, and implementation complexity. In this context, these proposed schemes offer different solutions in order to improve the performance of underlay cognitive radio systems