Scale-invariant subspace detectors based on first- and second-order statistical models

The problem is to detect a multi-dimensional source transmitting an unknown sequence of complex-valued symbols to a multi-sensor array. In some cases the channel subspace is known, and in others only its dimension is known. Should the unknown transmissions be treated as unknowns in a first-order statistical model, or should they be assigned a prior distribution that is then used to marginalize a first-order model for a second-order statistical model? This question motivates the derivation of subspace detectors for cases where the subspace is known, and for cases where only the dimension of the subspace is known. For three of these four models the GLR detectors are known, and they have been reported in the literature. But the GLR detector for the case of a known subspace and a second-order model for the measurements is derived for the first time in this paper. When the subspace is known, second-order generalized likelihood ratio (GLR) tests outperform first-order GLR tests when the spread of subspace eigenvalues is large, while first-order GLR tests outperform second-order GLR tests when the spread is small. When only the dimension of the subspace is known, second-order GLR tests outperform first-order GLR tests, regardless of the spread of signal subspace eigenvalues. For a dimension-1 source, first-order and second-order statistical models lead to equivalent GLR tests. This is a new finding.The work by I. Santamaria was supported by the Ministerio de Ciencia e Innovación of Spain, and AEI/FEDER funds of the E.U., under Grants TEC2016-75067-C4-4-R (CARMEN) and PID2019-104958RB-C43 (ADELE). The work by Louis Scharf is supported by the Air Force Office of Scientific Research under contract FA9550-18-1-0087, and by the National Science Foundation (NSF) under contract CCF-1712788. The work of David Ramírez was supported by the Ministerio de Ciencia, Innovación y Universidades under grant TEC2017-92552-EXP (aMBITION), by the Ministerio de Ciencia, Innovación y Universidades, jointly with the European Commission (ERDF), under Grant TEC2017-86921-C2-2-R (CAIMAN), and by The Comunidad de Madrid under grant Y2018/TCS-4705 (PRACTICO-CM)

One-Bit Spectrum Sensing for Cognitive Radio

Spectrum sensing in cognitive radio necessitates effective monitoring of wide bandwidths, which requires high-rate sampling. Traditional spectrum sensing methods employing high-precision analog-to-digital converters (ADCs) result in increased power consumption and expensive hardware costs. In this paper, we explore blind spectrum sensing utilizing one-bit ADCs. We derive a closed-form detector based on Rao's test and demonstrate its equivalence with the second-order eigenvalue-moment-ratio test. Furthermore, a near-exact distribution based on the moment-based method, and an approximate distribution in the low signal-to-noise ratio (SNR) regime with the use of the central limit theorem, are obtained. Theoretical analysis is then performed and our results show that the performance loss of the proposed detector is approximately $2$ dB ($\pi/2$) compared to detectors employing $\infty$-bit ADCs when SNR is low. This loss can be compensated for by using approximately $2.47$ ($\pi^2/4$) times more samples. In addition, we unveil that the efficiency of incoherent accumulation in one-bit detection is the square root of that of coherent accumulation. Simulation results corroborate the correctness of our theoretical calculations

I/Q Imbalance in Multiantenna Systems: Modeling, Analysis and RF-Aware Digital Beamforming

Wireless communications has experienced an unprecedented increase in data rates, numbers of active devices and selection of applications during recent years. However, this is expected to be just a start for future developments where a wireless connection is seen as a fundamental resource for almost any electrical device, no matter where and when it is operating. Since current radio technologies cannot provide such services with reasonable costs or even at all, a multitude of technological developments will be needed. One of the most important subjects, in addition to higher bandwidths and flexible network functionalities, is the exploitation of multiple antennas in base stations (BSs) as well as in user equipment (UEs). That kind of multiantenna communications can boost the capacity of an individual UE-BS link through spatial antenna multiplexing and increase the quality as well as robustness of the link via antenna diversity. Multiantenna technologies provide improvements also on the network level through spatial UE multiplexing and sophisticated interference management. Additionally, multiple antennas can provide savings in terms of the dissipated power since transmission and reception can be steered more efficiently in space, and thus power leakage to other directions is decreased. However, several issues need to be considered in order to get multiantenna technologies widely spread. First, antennas and the associated transceiver chains are required to be simple and implementable with low costs. Second, size of the antennas and transceivers need to be minimized. Finally, power consumption of the system must be kept under control. The importance of these requirements is even emphasized when considering massive multiple-input multiple-output (MIMO) systems consisting of devices equipped with tens or even hundreds of antennas.In this thesis, we consider multiantenna devices where the associated transceiver chains are implemented in such a way that the requirements above can be met. In particular, we focus on the direct-conversion transceiver principle which is seen as a promising radio architecture for multiantenna systems due to its low costs, small size, low power consumption and good flexibility. Whereas these aspects are very promising, direct-conversion transceivers have also some disadvantages and are vulnerable to certain imperfections in the analog radio frequency (RF) electronics in particular. Since the effects of these imperfections usually get even worse when optimizing costs of the devices, the scope of the thesis is on the effects and mitigation of one of the most severe RF imperfection, namely in-phase/quadrature (I/Q) imbalance.Contributions of the thesis can be split into two main themes. First of them is multiantenna narrowband beamforming under transmitter (TX) and receiver (RX) I/Q imbalances. We start by creating a model for the signals at the TX and RX, both under I/Q imbalances. Based on these models we derive analytical expressions for the antenna array radiation patterns and notice that I/Q imbalance distorts not only the signals but also the radiation characteristics of the array. After that, stemming from the nature of the distortion, we utilize widely-linear (WL) processing, where the signals and their complex conjugates are processed jointly, for the beamforming task under I/Q imbalance. Such WL processing with different kind of statistical and adaptive beamforming algorithms is finally shown to provide a flexible operation as well as distortion-free signals and radiation patterns when being under various I/Q imbalance schemes.The second theme extends the work to wideband systems utilizing orthogonal frequency-division multiplexing (OFDM)-based waveforms. The focus is on uplink communications and BS RX processing in a multiuser MIMO (MU-MIMO) scheme where spatial UE multiplexing is applied and further UE multiplexing takes place in frequency domain through the orthogonal frequency-division multiple access (OFDMA) principle. Moreover, we include the effects of external co-channel interference into our analysis in order to model the challenges in heterogeneous networks. We formulate a flexible signal model for a generic uplink scheme where I/Q imbalance occurs on both TX and RX sides. Based on the model, we analyze the signal distortion in frequency domain and develop augmented RX processing methods which process signals at mirror subcarrier pairs jointly. Additionally, the proposed augmented methods are numerically shown to outperform corresponding per-subcarrier method in terms of the instantaneous signal-to-interference-and-noise ratio (SINR). Finally, we address some practical aspects and conclude that the augmented processing principle is a promising tool for RX processing in multiantenna wideband systems under I/Q imbalance.The thesis provides important insight for development of future radio networks. In particular, the results can be used as such for implementing digital signal processing (DSP)-based RF impairment mitigation in real world transceivers. Moreover, the results can be used as a starting point for future research concerning, e.g., joint effects of multiple RF impairments and their mitigation in multiantenna systems. Overall, this thesis and the associated publications can help the communications society to reach the ambitious aim of flexible, low-cost and high performance radio networks in the future

Bayesian approach for the spectrum sensing mimo-cognitive radio network with presence of the uncertainty

A cognitive radio technique has the ability to learn. This system not only can observe the surrounding environment, adapt to environmental conditions, but also efficiently use the radio spectrum. This technique allows the secondary users (SUs) to employ the primary users (PUs) spectrum during the band is not being utilized by the user. Cognitive radio has three main steps: sensing of the spectrum, deciding and acting. In the spectrum sensing technique, the channel occupancy is determined with a spectrum sensing approach to detect unused spectrum. In the decision process, sensing results are evaluated and the decision process is then obtained based on these results. In the final process which is called the acting process, the scholar determines how to adjust the parameters of transmission to achieve great performance for the cognitive radio network

Digital Front-End Signal Processing with Widely-Linear Signal Models in Radio Devices

Necessitated by the demand for ever higher data rates, modern communications waveforms have increasingly wider bandwidths and higher signal dynamics. Furthermore, radio devices are expected to transmit and receive a growing number of different waveforms from cellular networks, wireless local area networks, wireless personal area networks, positioning and navigation systems, as well as broadcast systems. On the other hand, commercial wireless devices are expected to be cheap, be relatively small in size, and have a long battery life. The demands for flexibility and higher data rates on one hand, and the constraints on production cost, device size, and energy efficiency on the other, pose difficult challenges on the design and implementation of future radio transceivers. Under these diametric constraints, in order to keep the overall implementation cost and size feasible, the use of simplified radio architectures and relatively low-cost radio electronics are necessary. This notion is even more relevant for multiple antenna systems, where each antenna has a dedicated radio front-end. The combination of simplified radio front-ends and low-cost electronics implies that various nonidealities in the remaining analog radio frequency (RF) modules, stemming from unavoidable physical limitations and material variations of the used electronics, are expected to play a critical role in these devices. Instead of tightening the specifications and tolerances of the analog circuits themselves, a more cost-effective solution in many cases is to compensate for these nonidealities in the digital domain. This line of research has been gaining increasing interest in the last 10-15 years, and is also the main topic area of this work. The direct-conversion radio principle is the current and future choice for building low-cost but flexible, multi-standard radio transmitters and receivers. The direct-conversion radio, while simple in structure and integrable on a single chip, suffers from several performance degrading circuit impairments, which have historically prevented its use in wideband, high-rate, and multi-user systems. In the last 15 years, with advances in integrated circuit technologies and digital signal processing, the direct-conversion principle has started gaining popularity. Still, however, much work is needed to fully realize the potential of the direct-conversion principle. This thesis deals with the analysis and digital mitigation of the implementation nonidealities of direct-conversion transmitters and receivers. The contributions can be divided into three parts. First, techniques are proposed for the joint estimation and predistortion of in-phase/quadrature-phase (I/Q) imbalance, power amplifier (PA) nonlinearity, and local oscillator (LO) leakage in wideband direct-conversion transmitters. Second, methods are developed for estimation and compensation of I/Q imbalance in wideband direct-conversion receivers, based on second-order statistics of the received communication waveforms. Third, these second-order statistics are analyzed for second-order stationary and cyclostationary signals under several other system impairments related to circuit implementation and the radio channel. This analysis brings new insights on I/Q imbalances and their compensation using the proposed algorithms. The proposed algorithms utilize complex-valued signal processing throughout, and naturally assume a widely-linear form, where both the signal and its complex-conjugate are filtered and then summed. The compensation processing is situated in the digital front-end of the transceiver, as the last step before digital-to-analog conversion in transmitters, or in receivers, as the first step after analog-to-digital conversion. The compensation techniques proposed herein have several common, unique, attributes: they are designed for the compensation of frequency-dependent impairments, which is seen critical for future wideband systems; they require no dedicated training data for learning; the estimators are computationally efficient, relying on simple signal models, gradient-like learning rules, and solving sets of linear equations; they can be applied in any transceiver type that utilizes the direct-conversion principle, whether single-user or multi-user, or single-carrier or multi-carrier; they are modulation, waveform, and standard independent; they can also be applied in multi-antenna transceivers to each antenna subsystem separately. Therefore, the proposed techniques provide practical and effective solutions to real-life circuit implementation problems of modern communications transceivers. Altogether, considering the algorithm developments with the extensive experimental results performed to verify their functionality, this thesis builds strong confidence that low-complexity digital compensation of analog circuit impairments is indeed applicable and efficient

Robust spectrum sensing techniques for cognitive radio networks

Cognitive radio is a promising technology that improves the spectral utilisation by allowing unlicensed secondary users to access underutilised frequency bands in an opportunistic manner. This task can be carried out through spectrum sensing: the secondary user monitors the presence of primary users over the radio spectrum periodically to avoid harmful interference to the licensed service. Traditional energy based sensing methods assume the value of noise power as prior knowledge. They suffer from the noise uncertainty problem as even a mild noise level mismatch will lead to significant performance loss. Hence, developing an efficient robust detection method is important. In this thesis, a novel sensing technique using the F-test is proposed. By assuming a multiple antenna assisted receiver, this detector uses the F-statistic as the test statistic which offers absolute robustness against the noise variance uncertainty. In addition, since the channel state information (CSI) is required to be known, the impact of CSI uncertainty is also discussed. Results show the F-test based sensing method performs better than the energy detector and has a constant false alarm probability, independent of the accuracy of the CSI estimate. Another main topic of this thesis is to address the sensing problem for non-Gaussian noise. Most of the current sensing techniques consider Gaussian noise as implied by the central limit theorem (CLT) and it offers mathematical tractability. However, it sometimes fails to model the noise in practical wireless communication systems, which often shows a non-Gaussian heavy-tailed behaviour. In this thesis, several sensing algorithms are proposed for non-Gaussian noise. Firstly, a non-parametric eigenvalue based detector is developed by exploiting the eigenstructure of the sample covariance matrix. This detector is blind as no information about the noise, signal and channel is required. In addition, the conventional energy detector and the aforementioned F-test based detector are generalised to non-Gaussian noise, which require the noise power and CSI to be known, respectively. A major concern of these detection methods is to control the false alarm probability. Although the test statistics are easy to evaluate, the corresponding null distributions are difficult to obtain as they depend on the noise type which may be unknown and non-Gaussian. In this thesis, we apply the powerful bootstrap technique to overcome this difficulty. The key idea is to reuse the data through resampling instead of repeating the experiment a large number of times. By using the nonparametric bootstrap approach to estimate the null distribution of the test statistic, the assumptions on the data model are minimised and no large sample assumption is invoked. In addition, for the F-statistic based method, we also propose a degrees-of-freedom modification approach for null distribution approximation. This method assumes a known noise kurtosis and yields closed form solutions. Simulation results show that in non-Gaussian noise, all the three detectors maintain the desired false alarm probability by using the proposed algorithms. The F-statistic based detector performs the best, e.g., to obtain a 90% detection probability in Laplacian noise, it provides a 2.5 dB and 4 dB signal-to-noise ratio (SNR) gain compared with the eigenvalue based detector and the energy based detector, respectively