Blind CSI acquisition for multi-antenna interference mitigation in 5G networks

Esswie, Ali Abdulmawgood Ali Ali (2017) Blind CSI acquisition for multi-antenna interference mitigation in 5G networks. Masters thesis, Memorial University of Newfoundland.

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Abstract

Future wireless communication networks are required to satisfy the increasing demands of traffic and capacity. The upcoming fifth generation (5G) of the cellular technology is expected to meet 1000 times the capacity that of the current fourth generation (4G). These tight specifications introduce a new set of research challenges. However, interference has always been the bottleneck in cellular communications. Thus, towards the vision of the 5G, massive multi-input multi-output (mMIMO) and interference alignment (IA) are key transmission technologies to fulfil the future requirements, by controlling the residual interference. By equipping the base-station (BS) with a large number of transmit antennas, e.g, tens of hundreds of antennas, a mMIMO system can theoretically achieve significant capacity with limited interference, where many user equipment (UEs) can be served simultaneously at the same time and frequency resources. A mMIMO offers great spatial degrees of freedom (DoFs), which boost the total network capacity without increasing transmission power or bandwidth. However, the majority of the recent mMIMO investigations are based on theoretical channels with independent and identically distributed (i.i.d) Gaussian distribution, which facilitates the computation of closed-form rate expressions. Nonetheless, practical channels are not spatially uncorrelated, where the BS receives different power ratios across different spatial directions between the same transmitting and receiving antennas. Thus, it is important to understand the behavior of such new technology with practical channel modeling. Alternatively, IA is known to break the bottleneck between the capacity of the network and the overall spectral efficiency (SE), where a performance degradation is observed at a certain level of connected user capacity, due to the overwhelming inter-user interference. Theoretically, IA guarantees a linear relationship between half of the overall network SE and the online capacity by aligning interference from all transmitters inside one spatial signal subspace, leaving the other subspace for desired transmission. However, IA has tight feasibility conditions in practice including high precision channel state information at transmitter (CSIT), which leads to severe feedback overhead. In this thesis, high-precision blind CSIT algorithms are developed under different transmission technologies. We first consider the CSIT acquisition problem in MIMO IA systems. Proposed spatial channel estimation for MIMO-IA systems (SCEIA) shows great offered spatial degrees of freedom which contributes to approaching the performance of the perfect-CSIT case, without the requirements of channel quantization or user feedback overhead. In massive MIMO setups, proposed CSIT strategy offered scalable performance with the number of the transmit antennas. The effect of the non-stationary channel characteristics, which appears with very large antenna arrays, is minimized due to the effective scanning precision of the proposed strategy. Finally, we extend the system model to the full dimensional space, where users are distributed across the two dimensions of the cell space (azimuthal/elevation). Proposed directional spatial channel estimation (D-SCE) scans the 3D cell space and effectively attains additional CSIT and beamforming gains. In all cases, a list of comparisons with state-of-the-art schemes from academia and industry is performed to show the performance improvement of the proposed CSIT strategies.

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