In this paper, a receiver algorithm consisting of differential transmission and a two-stage detection for a single-input multiple-output (SIMO) phase-noise channels is studied. Specifically, the phases of the QAM modulated data symbols are manipulated before transmission in order to make them more immune to the random rotational effects of phase noise. At the receiver, a two-stage detector is implemented, which first detects the amplitude of the transmitted symbols from a nonlinear combination of the received signal amplitudes. Then in the second stage, the detector performs phase detection. The studied signaling method does not require transmission of any known symbols that act as pilots. Furthermore, no phase noise estimator (or a tracker) is needed at the receiver to compensate the effect of phase noise. This considerably reduces the complexity of the receiver structure. Moreover, it is observed that the studied algorithm can be used for the setups where a common local oscillator or separate independent oscillators drive the radio-frequency circuitries connected to each antenna. Due to the differential encoding/decoding of the phase, weighted averaging can be employed at a multi- antenna receiver, allowing for phase noise suppression to leverage the large number of antennas. Hence, we observe that the performance improves by increasing the number of antennas, especially in the separate oscillator case. Further increasing the number of receive antennas results in a performance error floor, which is a function of the quality of the oscillator at the transmitter.

This paper presents channel measurements and preliminary analyses from three 2x2 microwave Line-of-Sight Multi-Input Multi-Output (LOS-MIMO) outdoor test links, operating on 8 GHz and 32 GHz. The main focus is to study channel conditions and the effects of rain and clear-air fading. First, we address the diverse performance measures used in the trials. In particular we introduce the MIMO phase difference as an additional channel metric to the received power. The MIMO phase difference is mainly determined by antenna positions, ground effects and atmospheric variations. The relation between antenna positions and channel conditions was addressed in a number of earlier works, while the atmospheric impact and the resulting link availability are rarely studied. Second, we present the preliminary results from the long-term measurement campaign. The long-term statistics of channel condition from the three links show stable performance. Analysis reveals that fluctuation of the phase difference and the channel condition number is likely due to abnormal atmospheric propagation which can be modelled using a two-ray reflection model. Meanwhile, rain attenuation is strongly correlated between MIMO sub-channels and its contribution to the long-term statistics of power loss is very close to the Single-Input Single-Output (SISO) case.

In this paper, the capacity of the additive white Gaussian noise (AWGN) channel, affected by time-varying Wiener phase noise is investigated. Tight upper and lower bounds on the capacity of this channel are developed. The upper bound is obtained by using the duality approach, and by considering a specific distribution over the output of the channel. In order to lower-bound the capacity, first a family of capacity-achieving input distributions is found by solving a functional optimization of the channel mutual information. Then, lower bounds on the capacity are obtained by drawing samples from the proposed distributions through Monte Carlo simulations. The proposed capacity-achieving input distributions are circularly symmetric, non-Gaussian, and the input amplitudes are correlated over time. The evaluated capacity bounds are tight for a wide range of signal-to-noise-ratio (SNR) values, and thus they can be used to quantify the capacity. Specifically, the bounds follow the well-known AWGN capacity curve at low SNR, while at high SNR, they coincide with the high-SNR capacity result available in the literature for the phase-noise channel.

In this paper, we study the problem of oscillator phase noise (PN) estimation in coordinated multi-point (CoMP) transmission systems. Specifically, we investigate the effect of phase synchronization between coordinated base stations (BSs) on PN estimation at the user receiver. In this respect, the Bayesian Cramér-Rao bound for PN estimation is derived which is a function of the level of phase synchronization between the coordinated BSs. Results show that quality of BS synchronization has a significant effect on the PN estimation.

In this paper, we address the problem of optimizing signal constellations for strong phase noise. The problem is investigated by considering three optimization formulations, which provide an analytical framework for constellation design. In the first formulation, we seek to design constellations that minimize the symbol error probability (SEP) for an approximate ML detector in the presence of phase noise. In the second formulation, we optimize constellations in terms of mutual information (MI) for the effective discrete channel consisting of phase noise, additive white Gaussian noise, and the approximate ML detector. To this end, we derive the MI of this discrete channel. Finally, we optimize constellations in terms of the MI for the phase noise channel. We give two analytical characterizations of the MI of this channel, which are shown to be accurate for a wide range of signal-to-noise ratios and phase noise variances. For each formulation, we present a detailed analysis of the optimal constellations and their performance in the presence of strong phase noise. We show that the optimal constellations significantly outperform conventional constellations and those proposed in the literature in terms of SEP, error floors, and MI.

In this paper, we address the classical problem of maximum-likelihood (ML) detection of data in the presence of random phase noise. We consider a system, where the random phase noise affecting the received signal is first compensated by a tracker/estimator. Then the phase error and its statistics are used for deriving the ML detector. Specifically, we derive an ML detector based on a Gaussian assumption for the phase error probability density function (PDF). Further without making any assumptions on the phase error PDF, we show that the actual ML detector can be reformulated as a weighted sum of central moments of the phase error PDF. We present a simple approximation of this new ML rule assuming that the phase error distribution is unknown. The ML detectors derived are also the aposteriori probabilities of the transmitted symbols, and are referred to as soft metrics. Then, using the detector developed based on Gaussian phase error assumption, we derive the symbol error probability (SEP) performance and error floor analytically for arbitrary constellations. Finally we compare SEP performance of the various detectors/metrics in this work and those from literature for different signal constellations, phase noise scenarios and SNR values.

In this work, the problem of receiver design for phase noise estimation and data detection in the presence of oscillator phase noise in a point-to-point multiple-input multiple-output (MIMO) system is addressed. First, we discuss some interesting and challenging aspects in receiver design for MIMO systems in the presence of Wiener phase noise. Then, using the variational Bayesian (VB) framework, a joint iterative phase noise estimator and symbol detector are developed based on inverse Gibbs or variational free energy maximization. Further, the symbol error probability (SEP) of the newly proposed iterative scheme is compared with the optimal maximum likelihood (ML) detector with perfect phase information for 16-phase shift keying (PSK) and 16-quadrature amplitude modulation (QAM) schemes.

Previous results in the literature have shown that derivation of the optimum maximum-likelihood (ML) receiver for symbol-by-symbol (SBS) detection of an uncoded data sequence based on all received signals in the presence of random phase noise is an intractable problem. This is because it involves the computation of the conditional probability distribution function (PDF) of the phase noise process. In this paper, we seek to minimize symbol error probability (SEP), which is achieved by SBS detection of the sequence based on all received signals. We show that the ML detector for this problem can be formulated as a weighted sum of central moments of the conditional PDF of phase noise. Given that the central moments of the conditional PDF of phase noise can be estimated, this new optimal structure is tractable with respect to the previously known optimal ML receiver. Furthermore, based on the new receiver structure, we propose a simple approximate method for SBS detection and investigate its scope and applicability. Simulation results demonstrate that SEP performance close to optimality can be obtained through the proposed method for a wide range of phase noise variances and low signal-to-noise ratio (SNR).

In this work, we propose a cognitive interference management scheme for heterogeneous cellular wireless networks (LTE-A and WiMAX) with macrocells and femtocells (that operate in a closed-access mode). The scheme presented allocates resources (in time and frequency) and transmission opportunities to the macro/femtocells in the network by considering their potential to cause interference at each others' associated user equipments (UEs). We study the efficacy of the proposed scheme using a LTE-A system level simulator and compare with frequency reuse techniques and no interference management scenarios. We observe that our scheme significantly enhances average cell-edge UE throughput (5\%-tile throughput) with some degradation in overall sum throughput. Also we observe that the scheme decreases the probability of cell-edge users in the system experiencing degraded SINR.

We analyze the characteristics and performance of a wireless ad hoc network where nodes are connected via random channels and information is transported in the network in a cooperative multihop fashion using amplify and forward relay strategy. We characterize the network by studying important parameters such as: (1) SNR degradation with hop, (2) outage probability, (3) maximum permissible number of hops, and (4) maximum permissible number of simultaneous transmissions. We then devise a method for node selection and transmission of information across the network over disjoint routes between source and destination nodes by employing standard constructs from graph theory. Based on the above results, we evaluate the throughput achievable in the network and its asymptotic scaling as function of the number of nodes in the network for a given channel distribution.

Consider a scenario where a subset of sensors has been selected for target tracking in an energy-constrained wireless sensor network. They communicate their quantized state measurements over noisy Rayleigh fading channels to a fusion center, where centralized estimation of target state is performed. In this context, we consider the problem of determining optimum quantization bits and transmission power per bit for all sensors such that both resource utilization and overall estimation error at the fusion center are minimized. Unlike prior efforts, we incorporate the operating state (characterized by the amount of residual battery power) of the sensors in the optimization framework. Analytical solutions for optimum power and number of quantization bits are derived and validated with numerical examples through simulations. We study the effect of channel quality, local measurement noise, and operating states of the sensors on their optimum choice for quantization bits and transmit power per bit.

In this work, we present novel methods for detecting the presence of motion artifact in photoplethysmographic (PPG) measurements based on higher order statistical information present in the data. We analyze both clean and corrupt PPG data in the time and frequency domains. In the time domain, skew and kurtosis measures of the signal are used as distinguishing metrics between clean and motion- corrupted data. In the frequency domain, the presence of random components due to motion artifact is analyzed using a frequency domain kurtosis measure. Additionally, bispectral analysis of PPG data indicates the presence of strong quadratic phase coupling (QPC) and more specifically self coupling in the case of clean PPG data. Though quadratic phase coupling is found in data corrupted by motion artifact, the self coupling feature is absent. A Neyman-Pearson (NP) detection rule is formulated for each of the measures. Additionally, treating each of the measures as observations from independent sensors, the Varshney-Chair rule is used to fuse individual decisions to form a global system decision.

Corruption of photopleythsmograms by motion artifacts has been a serious obstacle to the reliable use of pulse oximeters for real-time, continuous state-of-health monitoring. In this work, we propose a motion artifact reduction methodology that is effective even in the case of severe subject movement. The methodology involves an enhanced preprocessing unit consisting of a motion detection unit (MDU), period estimation unit, and a Fourier series reconstruction unit. The MDU aids in identifying clean data frames versus those corrupted with motion artifacts. The period detection unit is used to determine the fundamental frequency of a corrupt frame. The Fourier series reconstruction unit reconstructs the final preprocessed signal. The reconstruction process primarily utilizes the spectrum variability of the pulse waveform. Preprocessed data are then fed to a magnitude- based frequency domain independent component analysis (FD-ICA) unit. This helps reduce motion artifacts present at the frequency components chosen for reconstruction. Experimental results are presented to demonstrate the effectiveness of the proposed motion artifact reduction method. The efficacy of the technique is compared with time domain ICA and complex frequency domain ICA methods.