Aperiodic Array Synthesis for Telecommunications
Arrays of antennas offer important advantages over single-element antennas and are thus a key part of most advanced communication systems. The majority of current arrays are based on a classical regular layout, which offer simple design criteria despite some limitations. Aperiodic arrays can reduce the number of elements and improve the performance, however their design is far more challenging. This thesis focuses on the synthesis of aperiodic arrays, advancing the state-of-the-art of phased arrays and pioneering the application to Multiple-Input Multiple-Output (MIMO) systems.
In satellite communications (SATCOM), aperiodic sparse arrays have the potential for drastically reducing the costs of massive antennas. Most available synthesis methods are however either intractable, suboptimal or limited for such demanding scenarios. We propose a deterministic and efficient approach based on Compressive Sensing, capable of accounting for electromagnetic phenomena and complex specifications. Some of the key contributions include the extension to the design of multi-beam, modular, multi-element, reconfigurable and isophoric architectures.
The same approach is successfully applied to the design of compact arrays for Point-to-Point (PtP) backhauling. The aperiodicity is used here instead to reduce the side lobes and meet the target radiation envelope with high aperture efficiency. A dense, column arranged, slotted waveguide isophoric array has been successfully designed, manufactured and measured.
Line-of-Sight (LoS) MIMO can multiply the data rates of classical Single-Input Single-Output (SISO) backhauling systems, however it suffers from limited installation flexibility. We demonstrate how aperiodic arrays and their switched extensions can instead overcome this shortcoming. Since a small number of antennas are typically used, an exhaustive search is adopted for the synthesis.
Massive Multi-User (MU) MIMO is envisioned as a key technology for future 5G systems. Despite the prevailing understanding, we show how the MIMO performance is affected by the array layout. To exploit this, we propose a new hybrid statistical-density tapered synthesis approach. Results show a significant improvement in minimum power budget, capacity and amplifier efficiency, especially for massive and/or crowded systems
maximally sparse array
Room EB, floor 4, Hörsalsvägen 11, Chalmers University of Technology
Opponent: Prof. Andrea Massa, Department of Information Engineering and Computer Science, University of Trento, Italy