Temporal and spatial combining for 5G mmWave small cells

This chapter proposes the combination of temporal processing through Rake combining based on direct sequence-spread spectrum (DS-SS), and multiple antenna beamforming or antenna spatial diversity as a possible physical layer access technique for fifth generation (5G) small cell base stations (SBS) operating in the millimetre wave (mmWave) frequencies. Unlike earlier works in the literature aimed at previous generation wireless, the use of the beamforming is presented as operating in the radio frequency (RF) domain, rather than the baseband domain, to minimise power expenditure as a more suitable method for 5G small cells. Some potential limitations associated with massive multiple input-multiple output (MIMO) for small cells are discussed relating to the likely limitation on available antennas and resultant beamwidth. Rather than relying, solely, on expensive and potentially power hungry massive MIMO (which in the case of a SBS for indoor use will be limited by a physically small form factor) the use of a limited number of antennas, complimented with Rake combining, or antenna diversity is given consideration for short distance indoor communications for both the SBS) and user equipment (UE). The proposal’s aim is twofold: to solve eroded path loss due to the effective antenna aperture reduction and to satisfy sensitivity to blockages and multipath dispersion in indoor, small coverage area base stations. Two candidate architectures are proposed. With higher data rates, more rigorous analysis of circuit power and its effect on energy efficiency (EE) is provided. A detailed investigation is provided into the likely design and signal processing requirements. Finally, the proposed architectures are compared to current fourth generation long term evolution (LTE) MIMO technologies for their anticipated power consumption and EE

Space time transceiver design over multipath fading channels

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Blind reception of multicarrier DS-CDMA using antenna arrays

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Multi-carrier CDMA using convolutional coding and interference cancellation

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Channel estimation and signal enhancement for DS-CDMA systems

This dissertation focuses on topics of Bayesian-based multiuser detection, space-time (S-T) transceiver design, and S-T channel parameter estimation for direct-sequence code-division multiple-access (DS-CDMA) systems. Using the Bayesian framework, various linear and simplified nonlinear multiuser detectors are proposed, and their performances are analyzed. The simplified non-linear Bayesian solutions can bridge the performance gap between sub-optimal linear multiuser detectors and the optimum multiuser detector. To further improve the system capacity and performance, S-T transceiver design approaches with complexity constraint are investigated. Novel S-T receivers of low-complexity that jointly use the temporal code-signature and the spatial signature are proposed. Our solutions, which lead to generalized near-far resistant S-T RAKE receivers, achieve better interference suppression than the existing S-T RAKE receivers. From transmitter side, we also proposed a transmit diversity (TD) technique in combination with differential detection for the DS-CDMA systems. It is shown that the proposed S-T TD scheme in combination with minimum variance distortionless response transceiver (STTD+MVDR) is near-far resistant and outperforms the conventional STTD and matched filter based (STTD+MF) transceiver scheme. Obtaining channel state information (CSI) is instrumental to optimum S-T transceiver design in wireless systems. Another major focus of this dissertation is to estimate the S-T channel parameters. We proposed an asymptotic, joint maximum likelihood (ML) method of estimating multipath channel parameters for DS-CDMA systems. An iterative estimator is proposed to further simplify the computation. Analytical and simulation results show that the iterative estimation scheme is near-far resistant for both time delays and DOAs. And it reaches the corresponding CRBs after a few iterations

Low order channel estimation for CDMA systems

New approaches and algorithms are developed for the identification and estimation of low order models that represent multipath channel effects in Code Division Multiple Access (CDMA) communication systems. Based on these parsimonious channel models, low complexity receivers such as RAKE receivers are considered to exploit these propagation effects and enhance the system performance. We consider the scenario where multipath is frequency selective slowly fading and where the channel components including delays and attenuation coefficients are assumed to be constant over one or few signalling intervals. We model the channel as a long FIR-like filter (or a tapped delay line filter) with the number of taps related to the ratio between the channel delay-spread and the chip duration. Due to the high data rate of new CDMA systems, the channel length in terms of the chip duration will be very large. With classical channel estimation techniques this will result in poor estimates of many of the channel parameters where most of them are zero leading to a reduction in the system performance. Unlike classical techniques which estimate directly the channel response given the number of taps or given an estimate of the channel length, the proposed techniques in this work will firstly identify the significant multipath parameters using model selection techniques, then estimate these identified parameters. Statistical tests are proposed to determine whether or not each individual parameter is significant. A low complexity RAKE receiver is then considered based on estimates of these identified parameters only. The level of significance with which we will make this assertion will be controlled based on statistical tests such as multiple hypothesis tests. Frequency and time domain based approaches and model selection techniques are proposed to achieve the above proposed objectives.The frequency domain approach for parsimonious channel estimation results in an efficient implementation of RAKE receivers in DS-CDMA systems. In this approach, we consider a training based strategy and estimate the channel delays and attenuation using the averaged periodogram and modified time delay estimation techniques. We then use model selection techniques such as the sphericity test and multiple hypotheses tests based on F-Statistics to identify the model order and select the significant channel paths. Simulations show that for a pre-defined level of significance, the proposed technique correctly identifies the significant channel parameters and the parsimonious RAKE receiver shows improved statistical as well as computational performance over classical methods. The time domain approach is based on the Bootstrap which is appropriate for the case when the distribution of the test statistics required by the multiple hypothesis tests is unknown. In this approach we also use short training data and model the channel response as an FIR filter with unknown length. Model parameters are then estimated using low complexity algorithms in the time domain. Based on these estimates, bootstrap based multiple hypotheses tests are applied to identify the non-zero coefficients of the FIR filter. Simulation results demonstrate the power of this technique for RAKE receivers in unknown noise environments. Finally we propose adaptive blind channel estimation algorithms for CDMA systems. Using only the spreading code of the user of interest and the received data sequence, four different adaptive blind estimation algorithms are proposed to estimate the impulse response of frequency selective and frequency non-selective fading channels. Also the idea is based on minimum variance receiver techniques. Tracking of a frequency selective varying fading channel is also considered.A blind based hierarchical MDL model selection method is also proposed to select non-zero parameters of the channel response. Simulation results show that the proposed algorithms perform better than previously proposed algorithms. They have lower complexity and have a faster convergence rate. The proposed algorithms can also be applied to the design of adaptive blind channel estimation based RAKE receivers

Temporal and spatial combining for 5G mmWave small cells

This chapter proposes the combination of temporal processing through Rake combining based on direct sequence-spread spectrum (DS-SS), and multiple antenna beamforming or antenna spatial diversity as a possible physical layer access technique for fifth generation (5G) small cell base stations (SBS) operating in the millimetre wave (mmWave) frequencies. Unlike earlier works in the literature aimed at previous generation wireless, the use of the beamforming is presented as operating in the radio frequency (RF) domain, rather than the baseband domain, to minimise power expenditure as a more suitable method for 5G small cells. Some potential limitations associated with massive multiple input-multiple output (MIMO) for small cells are discussed relating to the likely limitation on available antennas and resultant beamwidth. Rather than relying, solely, on expensive and potentially power hungry massive MIMO (which in the case of a SBS for indoor use will be limited by a physically small form factor) the use of a limited number of antennas, complimented with Rake combining, or antenna diversity is given consideration for short distance indoor communications for both the SBS) and user equipment (UE). The proposal’s aim is twofold: to solve eroded path loss due to the effective antenna aperture reduction and to satisfy sensitivity to blockages and multipath dispersion in indoor, small coverage area base stations. Two candidate architectures are proposed. With higher data rates, more rigorous analysis of circuit power and its effect on energy efficiency (EE) is provided. A detailed investigation is provided into the likely design and signal processing requirements. Finally, the proposed architectures are compared to current fourth generation long term evolution (LTE) MIMO technologies for their anticipated power consumption and EE