MIMO processing techniques for heterogeneous cellular systems

Abstract

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The expected massive proliferation of wireless systems point out that in the year 2020 there will be the need to support up to one thousand times more tra c than today. Besides, ten times more users will have to be managed and there will be a need to enable Gigabit per second peak speeds. The traditional methods have a limited capability to cope with the requirements of system capacity, spectral and energy e ciency among others. One of the foreseen key enabling technology for the evolution of mobile technologies towards 5G is cell densi cation. Network densi cation through use of small cells (SCs) has been considered in 3GPP which made small cells an integral part of LTE Advanced by developing the concept of heterogeneous networks (HetNets). The reduced area covered by each small cell implies the use of a large number of them, which leads to the concept of ultra-dense small cell networks. However, this leads to multiple tiers of cells with high levels of interference that need to be coordinated, requiring advanced signal processing: Interference alignment (IA) precoding and iterative equalization (namely the ones based on Iterative-Block Decision Feedback Equalization (IB-DFE) concept) are seen as e cient techniques to deal with this high interference level faced by HetNets. In this dissertation we consider several cells with heterogeneous characteristics, macro and small, working under the same spectrum. These cells serve a set of users, which are split between the two cell types. Under a frequency reuse of one, the users allocated to a given cell generate harmful interference on the other. To tackle that problem we apply IA at both macro and small cell users, but this requires the availability of Channel State Information (CSI) at the transmitters. The more realistic case of imperfect CSI is considered, but this leads to imperfectly interference aligned and therefore to performance degradation. Iterative space-frequency equalization is performed at the receiver side through IB-DFE technique to cope with this residual interference. The results demonstrate that iterative equalization is robust to imperfect CSI and removes e ciently the interference generated by the poorly aligned interference. A performance close to Matched Filter Bound (MFB) is achieved with a very few number of iterations.