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1. Multiband Spectrum Sensing with Non-exponential Channel Occupancy Times

   Cheng, Hanke; Mark, Brian L.; Ephraim, Yariv; Chen, Chun-Hung (June 2021, IEEE International Conference on Communications)

   null (Ed.)

   In a wireless network with dynamic spectrum sharing, tracking temporal spectrum holes across a wide spectrum band is a challenging task. We consider a scenario in which the spectrum is divided into a large number of bands or channels, each of which has the potential to provide dynamic spectrum access opportunities. The occupancy times of each band by primary users are generally non-exponentially distributed. We develop an approach to determine and parameterize a small selected subset of the bands with good spectrum access opportunities, using limited computational resources under noisy measurements. We model the noisy measurements of the received signal in each band as a bivariate Markov modulated Gaussian process, which can be viewed as a continuous-time bivariate Markov chain observed through Gaussian noise. The underlying bivariate Markov process allows for the characterization of non-exponentially distributed state sojourn times. The proposed scheme combines an online expectation-maximization algorithm for parameter estimation with a computing budget allocation algorithm. Observation time is allocated across the bands to determine the subset of G out of G frequency bands with the largest mean idle times for dynamic spectrum access and at the same time to obtain accurate parameter estimates for this subset of bands. Our simulation results show that when channel holding times are non-exponential, the proposed scheme achieves a substantial improvement in the probability of correct selection of the best subset of bands compared to an approach based on a (univariate) Markov modulated Gaussian process model. 

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2. Scalable Dynamic Spectrum Access with IEEE 1900.5.2 Spectrum Consumption Models

   https://doi.org/10.36227/techrxiv.171216553.32959940/v1  

   Netalkar, Prasad; Bastidas, Carlos_E Caicedo; Kadota, Igor; Zussman, Gil; Seskar, Ivan; Raychaudhuri, Dipankar (April 2024, Techrxiv)

   Dynamic Spectrum Access (DSA) is a key mechanism for meeting the ever-increasing demand for emerging wireless services. DSA involves managing and assigning available spectrum resources in a way that minimizes interference and allows RF coexistence between heterogeneous devices and systems. Spectrum Consumption Models (SCMs)- defined in the IEEE 1900.5.2 standard, offer a mechanism for RF devices to: (i) declare the characteristics of their intended spectrum use and their interference protection needs; and (ii) determine compatibility (non-interference) with existing devices. In this paper, we propose a novel SCM-based Spectrum Deconfliction (SD) algorithm that dynamically configures RF operational parameters (e.g., center frequency and transmission power) of a target transmitter-receiver pair aiming to minimize interference with existing devices/systems. We also propose sequential and distributed DSA methods that use the SD algorithm for assigning spectrum in large-scale networks. To evaluate the performance of our methods in terms of computation time, spectrum assignment efficiency, and overhead, we use two custom-made simulation platforms. Finally, to experimentally demonstrate the feasibility of our methods, we build a proof-of-concept implementation in the NSF PAWR COSMOS wireless testbed. The results reveal the advantages of using SCMs and their capabilities to conduct spectrum assignments in dynamic and congested communication environments. 

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3. Wideband Temporal Spectrum Sensing Using Cepstral Features

   https://doi.org/10.1109/WoWMoM.2019.8793002  

   Cheng, Hanke; Mark, Brian L.; Ephraim, Yariv (June 2019, 2019 IEEE 20th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM))

   Spectrum sensing enables secondary users in a cognitive radio network to opportunistically access portions of the spectrum left idle by primary users. Tracking spectrum holes jointly in time and frequency over a wide spectrum band is a challenging task. In one approach to wideband temporal sensing, the spectrum band is partitioned into narrowband subchannels of fixed bandwidth, which are then characterized via hidden Markov modeling using average power or energy measurements as observation data. Adjacent, correlated subchannels are recursively aggregated into channels of variable bandwidths, corresponding to the primary user signals. Thus, wideband temporal sensing is transformed into a multiband sensing scenario by identifying the primary user channels in the spectrum band. However, future changes in the configuration of the primary user channels in the multiband setup cannot generally be detected using an energy detector front end for spectrum sensing. We propose the use of a cepstral feature vector to detect changes in the spectrum envelope of a primary user channel. Our numerical results show that the cepstrum-based spectrum envelope detector performs well under moderate to high signal-to-noise ratio conditions. 

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4. mRSC: Multidimensional Robust Synthetic Control

   https://doi.org/10.1145/3309697.3331507  

   Amjad, Muhammad Jehangir; Misra, Vishal; Shah, Devavrat; Shen, Dennis (July 2019, ACM SIGMETRICS)

   We propose an algorithm to impute and forecast a time series by transforming the observed time series into a matrix, utilizing matrix estimation to recover missing values and de-noise observed entries, and performing linear regression to make predictions. At the core of our analysis is a representation result, which states that for a large class of models, the transformed time series matrix is (approximately) low-rank. In effect, this generalizes the widely used Singular Spectrum Analysis (SSA) in the time series literature, and allows us to establish a rigorous link between time series analysis and matrix estimation. The key to establishing this link is constructing a Page matrix with non-overlapping entries rather than a Hankel matrix as is commonly done in the literature (e.g., SSA). This particular matrix structure allows us to provide finite sample analysis for imputation and prediction, and prove the asymptotic consistency of our method. Another salient feature of our algorithm is that it is model agnostic with respect to both the underlying time dynamics and the noise distribution in the observations. The noise agnostic property of our approach allows us to recover the latent states when only given access to noisy and partial observations a la a Hidden Markov Model; e.g., recovering the time-varying parameter of a Poisson process without knowing that the underlying process is Poisson. Furthermore, since our forecasting algorithm requires regression with noisy features, our approach suggests a matrix estimation based method-coupled with a novel, non-standard matrix estimation error metric-to solve the error-in-variable regression problem, which could be of interest in its own right. Through synthetic and real-world datasets, we demonstrate that our algorithm outperforms standard software packages (including R libraries) in the presence of missing data as well as high levels of noise. 

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5. Model Agnostic Time Series Analysis via Matrix Estimation

   Agarwal, Anish; Amjad, Muhammad Jehangir; Shah, Devavrat; Shen, Dennis (December 2018, ACM SIGMETRICS performance evaluation review)

   We propose an algorithm to impute and forecast a time series by transforming the observed time series into a matrix, utilizing matrix estimation to recover missing values and de-noise observed entries, and performing linear regression to make predictions. At the core of our analysis is a representation result, which states that for a large class of models, the transformed time series matrix is (approximately) low-rank. In effect, this generalizes the widely used Singular Spectrum Analysis (SSA) in the time series literature, and allows us to establish a rigorous link between time series analysis and matrix estimation. The key to establishing this link is constructing a Page matrix with non-overlapping entries rather than a Hankel matrix as is commonly done in the literature (e.g., SSA). This particular matrix structure allows us to provide finite sample analysis for imputation and prediction, and prove the asymptotic consistency of our method. Another salient feature of our algorithm is that it is model agnostic with respect to both the underlying time dynamics and the noise distribution in the observations. The noise agnostic property of our approach allows us to recover the latent states when only given access to noisy and partial observations a la a Hidden Markov Model; e.g., recovering the time-varying parameter of a Poisson process without knowing that the underlying process is Poisson. Furthermore, since our forecasting algorithm requires regression with noisy features, our approach suggests a matrix estimation based method—coupled with a novel, non-standard matrix estimation error metric—to solve the error-in-variable regression problem, which could be of interest in its own right. Through synthetic and real-world datasets, we demonstrate that our algorithm outperforms standard software packages (including R libraries) in the presence of missing data as well as high levels of noise. 

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