In this paper, we propose a deep learning assisted soft-demodulator for multi-set space-time shift keying (MS-STSK) millimeter wave (mmWave) systems, where we train a neural network (NN) to provide the soft values of the MS-STSK symbol without relying on explicit channel state information (CSI). Thus, in contrast to the conventional MS-STSK soft-demodulator which relies on the knowledge of CSI at the receiver, the learning-assisted design circumvents the channel estimation while also improving the data rate by dispensing with pilot overhead. Furthermore, our proposed learning-aided soft-demodulation substantially reduces the number of cost-function evaluations at the output of the MS-STSK demodulator. We demonstrate by simulations that despite avoiding CSI-estimation and the pilot overhead, our learningassisted design performs closely to the channel-estimation aided design assuming perfect CSI for BER < 10 −4 , whilst imposing a low complexity. Furthermore, we show by simulations that upon using realistic imperfect CSI at the receiver employing conventional soft-demodulation, the learning-aided softdemodulator outperforms the conventional scheme. Additionally, we present quantitative discussions on the receiver complexity in terms of the number of computations required to produce the soft values.
In this paper, we present practical interference management schemes in heterogeneous networks (HetNets) based on interference decoding and cancellation at the receivers. The underlying idea is based on Han-Kobayashi type message splitting (MS) technique [3]. We develop relatively low-complexity precoders that facilitate interference mitigation and maximize sumthroughput in the network. System-level simulation results for a general HetNet system are presented. It is shown that the proposed MS design along with interference coordination among the cells provides up to 57% cell average throughput gain compared with the rank-1 coordinated beamforming (CBF) scheme. The design also provides substantial throughput gain in particular for Macro cells compared with rank-adaptive CBF transmission.
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