On the Monotonicity of the Generalized Marcum and Nuttall Q-Functions

Monotonicity criteria are established for the generalized Marcum Q-function, \emph{Q}_{M}, the standard Nuttall Q-function, \emph{Q}_{M,N}, and the normalized Nuttall Q-function, $\mathcal{Q}_{M,N}$, with respect to their real order indices M,N. Besides, closed-form expressions are derived for the computation of the standard and normalized Nuttall Q-functions for the case when M,N are odd multiples of 0.5 and $M\geq N$. By exploiting these results, novel upper and lower bounds for \emph{Q}_{M,N} and $\mathcal{Q}_{M,N}$ are proposed. Furthermore, specific tight upper and lower bounds for \emph{Q}_{M}, previously reported in the literature, are extended for real values of M. The offered theoretical results can be efficiently applied in the study of digital communications over fading channels, in the information-theoretic analysis of multiple-input multiple-output systems and in the description of stochastic processes in probability theory, among others.Comment: Published in IEEE Transactions on Information Theory, August 2009. Only slight formatting modification

Best Arm Identification Based Beam Acquisition in Stationary and Abruptly Changing Environments

We study the initial beam acquisition problem in millimeter wave (mm-wave) networks from the perspective of best arm identification in multi-armed bandits (MABs). For the stationary environment, we propose a novel algorithm called concurrent beam exploration, CBE, in which multiple beams are grouped based on the beam indices and are simultaneously activated to detect the presence of the user. The best beam is then identified using a Hamming decoding strategy. For the case of orthogonal and highly directional thin beams, we characterize the performance of CBE in terms of the probability of missed detection and false alarm in a beam group (BG). Leveraging this, we derive the probability of beam selection error and prove that CBE outperforms the state-of-the-art strategies in this metric. Then, for the abruptly changing environments, e.g., in the case of moving blockages, we characterize the performance of the classical sequential halving (SH) algorithm. In particular, we derive the conditions on the distribution of the change for which the beam selection error is exponentially bounded. In case the change is restricted to a subset of the beams, we devise a strategy called K-sequential halving and exhaustive search, K-SHES, that leads to an improved bound for the beam selection error as compared to SH. This policy is particularly useful when a near-optimal beam becomes optimal during the beam-selection procedure due to abruptly changing channel conditions. Finally, we demonstrate the efficacy of the proposed scheme by employing it in a tandem beam refinement and data transmission scheme

Predictor Antenna Systems: Exploiting Channel State Information for Vehicle Communications

Vehicle communication is one of the most important use cases in the fifth generation of wireless networks (5G). The growing demand for quality of service (QoS) characterized by performance metrics, such as spectrum efficiency, peak data rate, and outage probability, is mainly limited by inaccurate prediction/estimation of channel state information (CSI) of the rapidly changing environment around moving vehicles. One way to increase the prediction horizon of CSI in order to improve the QoS is deploying predictor antennas (PAs). A PA system consists of two sets of antennas typically mounted on the roof of a vehicle, where the PAs positioned at the front of the vehicle are used to predict the CSI observed by the receive antennas (RAs) that are aligned behind the PAs. In realistic PA systems, however, the actual benefit is affected by a variety of factors, including spatial mismatch, antenna utilization, temporal correlation of scattering environment, and CSI estimation error. This thesis investigates different resource allocation schemes for the PA systems under practical constraints.Comment: Licentiate thesis, Chalmers University of Technolog

Predictor Antenna Systems: Exploiting Channel State Information for Vehicle Communications

Vehicle communication is one of the most important use cases in the fifth generation of wireless networks (5G).\ua0 The growing demand for quality of service (QoS) characterized by performance metrics, such as spectrum efficiency, peak data rate, and outage probability, is mainly limited by inaccurate prediction/estimation of channel state information (CSI) of the rapidly changing environment around moving vehicles. One way to increase the prediction horizon of CSI in order to improve the QoS is deploying predictor antennas (PAs).\ua0 A PA system consists of two sets of antennas typically mounted on the roof of a vehicle, where the PAs positioned at the front of the vehicle are used to predict the CSI observed by the receive antennas (RAs) that are aligned behind the PAs. In realistic PA systems, however, the actual benefit is affected by a variety of factors, including spatial mismatch, antenna utilization, temporal correlation of scattering environment, and CSI estimation error. This thesis investigates different resource allocation schemes for the PA systems under practical constraints, with main contributions summarized as follows.First, in Paper A, we study the PA system in the presence of the so-called spatial mismatch problem, i.e., when the channel observed by the PA is not exactly the same as the one experienced by the RA. We derive closed-form expressions for the throughput-optimized rate adaptation, and evaluate the system performance in various temporally-correlated conditions for the scattering environment. Our results indicate that PA-assisted adaptive rate adaptation leads to a considerable performance improvement, compared to the cases with no rate adaptation. Then, to simplify e.g., various integral calculations as well as different operations such as parameter optimization, in Paper B, we propose a semi-linear approximation of the Marcum Q-function, and apply the proposed approximation to the evaluation of the PA system. We also perform deep analysis of the effect of various parameters such as antenna separation as well as CSI estimation error. As we show, our proposed approximation scheme enables us to analyze PA systems with high accuracy.The second part of the thesis focuses on improving the spectral efficiency of the PA system by involving the PA into data transmission. In Paper C, we analyze the outage-limited performance of PA systems using hybrid automatic repeat request (HARQ). With our proposed approach, the PA is used not only for improving the CSI in the retransmissions to the RA, but also for data transmission in the initial round.\ua0 As we show in the analytical and the simulation results, the combination of PA and HARQ protocols makes it possible to improve the spectral efficiency and adapt transmission parameters to mitigate the effect of spatial mismatch

Fully-Passive versus Semi-Passive IRS-Enabled Sensing: SNR and CRB Comparison

This paper investigates the sensing performance of two intelligent reflecting surface (IRS)-enabled non-line-of-sight (NLoS) sensing systems with fully-passive and semi-passive IRSs, respectively. In particular, we consider a fundamental setup with one base station (BS), one uniform linear array (ULA) IRS, and one point target in the NLoS region of the BS. Accordingly, we analyze the sensing signal-to-noise ratio (SNR) performance for a target detection scenario and the estimation Cram\'er-Rao bound (CRB) performance for a target's direction-of-arrival (DoA) estimation scenario, in cases where the transmit beamforming at the BS and the reflective beamforming at the IRS are jointly optimized. First, for the target detection scenario, we characterize the maximum sensing SNR when the BS-IRS channels are line-of-sight (LoS) and Rayleigh fading, respectively. It is revealed that when the number of reflecting elements $N$ equipped at the IRS becomes sufficiently large, the maximum sensing SNR increases proportionally to $N^2$ for the semi-passive-IRS sensing system, but proportionally to $N^4$ for the fully-passive-IRS counterpart. Then, for the target's DoA estimation scenario, we analyze the minimum CRB performance when the BS-IRS channel follows Rayleigh fading. Specifically, when $N$ grows, the minimum CRB decreases inversely proportionally to $N^4$ and $N^6$ for the semi-passive and fully-passive-IRS sensing systems, respectively. Finally, numerical results are presented to corroborate our analysis across various transmit and reflective beamforming design schemes under general channel setups. It is shown that the fully-passive-IRS sensing system outperforms the semi-passive counterpart when $N$ exceeds a certain threshold. This advantage is attributed to the additional reflective beamforming gain in the IRS-BS path, which efficiently compensates for the path loss for a large $N$.Comment: 13 pages,7 figure

Towards Context Information-based High-Performing Connectivity in Internet of Vehicle Communications

Internet-of-vehicles (IoV) is one of the most important use cases in the fifth generation (5G) of wireless networks and beyond. Here, IoV communications refer to two types of scenarios: serving the in-vehicle users with moving relays (MRs); and supporting vehicle-to-everything (V2X) communications for, e.g., connected vehicle functionalities. Both of them can be achieved by transceivers on top of vehicles with growing demand for quality of service (QoS), such as spectrum efficiency, peak data rate, and coverage probability. However, the performance of MRs and V2X is limited by challenges such as the inaccurate prediction/estimation of the channel state information (CSI), beamforming mismatch, and blockages. Knowing the environment and utilizing such context information to assist communication could alleviate these issues. This thesis investigates various context information-based performance enhancement schemes for IoV networks, with main contributions listed as follows.In order to mitigate the channel aging issue, i.e., the CSI becomes inaccurate soon at high speeds, the first part of the thesis focuses on one way to increase the prediction horizon of CSI in MRs: predictor antennas (PAs). A PA system is designed as a system with two sets of antennas on the roof of a vehicle, where the PAs positioned at the front of the vehicle are used to predict the CSI observed by the receive antennas (RAs) that are aligned behind the PAs. In PA systems, however, the benefit is affected by a variety of factors. For example, 1) spatial mismatch between the point where the PA estimates the channel and the point where the RA reaches several time slots later, 2) antenna utilization efficiency of the PA, 3) temporal evolution, and 4) estimation error of the PA-base station (BS) channel. First, in Paper A, we study the PA system in the presence of the spatial mismatch problem, and propose an analytical channel model which is used for rate adaptation. In paper B, we propose different approximation schemes for the analytical investigation of PA systems, and study the effect of different parameters on the network performance. Then, involving PAs into data transmission, Paper C and Paper D analyze the outage- and the delay-limited performance of PA systems using hybrid automatic repeat request (HARQ), respectively. As we show in the analytical and the simulation results in Papers C-D, the combination of PA and HARQ protocols makes it possible to improve spectral efficiency and adapt the transmission parameters to mitigate the effect of spatial mismatch. Finally, a review of PA studies in the literature, the challenges and potentials of PA as well as some to-be-solved issues are presented in Paper E.The second part of the thesis focuses on using advanced technologies to further improve the MR/IoV performance. In Paper F, a cooperative PA scheme in IoV networks is proposed to mitigate both the channel aging effect and blockage sensitivity in millimeter-wave channels by collaborative vehicles and BS handover. Then, in Paper G, we study the potentials and challenges of dynamic blockage pre-avoidance in IoV networks