In this study, we propose two novel approaches to mitigate cross-link interference (CLI) in a network-controlled repeater (NCR)-assisted integrated sensing and communication (ISAC) network. These approaches include base station (BS) sensing signal transmission (BSST) and an NCR sensing signal transmission (NSST). We implement a power constraint algorithm at the BS to optimize the system performance while ensuring communication reliability and meeting sensing service requirements. Additionally, we investigate the user mobility under the CLI scenarios with different NCR deployments. Specifically, we analyze the NCR connectivity to single and multiple BSs in order to evaluate the received signal reference power and signal-to-interference-plus-noise ratio-based handover (HO) strategies, focusing on radio link failure (RLF), and handover probability. The results show that deploying an NCR-assisted single BS achieves sum rate gains of up to 58.3% and increases in the probability of target detection by 60% compared to networks without assistance. Furthermore, a reduction of RLF by 49.1% is observed in the ISAC networks. These results highlight the effectiveness of NCRs in mitigating CLI and improving the system performance under diverse mobility conditions

As social networks are constantly changing and evolving, methods to analyze dynamic social networks are becoming more important in understanding social trends. However, due to the restrictions imposed by the social network service providers, the resources available to fetch the entire contents of a social network are typically very limited. As a result, analysis of dynamic social network data requires maintaining an approximate copy of the social network for each time period, locally. In this paper, we study the problem of dynamic network and text fetching with limited probing capacities, for identifying and maintaining influential users as the social network evolves. We propose an algorithm to probe the relationships (required for global influence computation) as well as posts (required for topic-based influence computation) of a limited number of users during each probing period, based on the influence trends and activities of the users. We infer the current network based on the newly probed user data and the last known version of the network maintained locally. Additionally, we propose to use link prediction methods to further increase the accuracy of our network inference. We employ PageRank as the metric for influence computation. We illustrate how the proposed solution maintains accurate PageRank scores for computing global influence, and topic-sensitive weighted PageRank scores for topic-based influence. The latter relies on a topic-based network constructed via weights determined by semantic analysis of posts and their sharing statistics. We evaluate the effectiveness of our algorithms by comparing them with the true influence scores of the full and up-to-date version of the network, using data from the micro-blogging service Twitter. Results show that our techniques significantly outperform baseline methods (80 percent higher accuracy for network fetching and 77 percent for text fetching) and are superior to state-of-the-art techniques from the literature (21 percent higher accuracy).

We are witnessing an enormous growth in the volume of data generated by various online services. An important portion of this data contains geographic references, since many of these services are location-enhanced and thus produce spatio-temporal records of their usage. We postulate that the spatio-temporal usage records belonging to the same real-world entity can be matched across records from different location-enhanced services. Linking spatio-temporal records enables data analysts and service providers to obtain information that they cannot derive by analyzing only one set of usage records. In this paper, we develop a new linkage model that can be used to match entities from two sets of spatio-temporal usage records belonging to two different location-enhanced services. This linkage model is based on the concept of k-l diversity-that we developed to capture both spatial and temporal aspects of the linkage. To realize this linkage model in practice, we develop a scalable linking algorithm called ST-Link, which makes use of effective spatial and temporal filtering mechanisms that significantly reduce the search space for matching users. Furthermore, ST-Link utilizes sequential scan procedures to avoid random disk access and thus scales to large datasets. We evaluated our work with respect to accuracy and performance using several datasets. Experiments show that ST-Link is effective in practice for performing spatio-temporal linkage and can scale to large datasets.

Telecom Networks produce huge amount of daily alarm logs. These alarms usually arrive from different regions and network equipments of mobile operators at different times. In a typical network operator, Network Operations Centers (NOCs) constantly monitor those alarms in a central location and try to fix issues raised by intelligent warning systems by performing a trouble ticketing based management system. In order to automate rule findings, different sequential rule mining algorithms can be exploited. However, the number of sequential rules and alarm correlations that can be generated by using these algorithms can overwhelm the NOC administrators since some of those rules are neither utilized nor reduced appropriately by the non-customized sequential rule mining algorithms. Therefore, additional efficient and intelligent rule identification techniques need to be developed depending on the characteristic of the data. In this paper, two new metrics that is inspired from document classification approaches are proposed in order to increase the accuracy of the sequential alarm rules. This approach utilizes new definition of identifying transactions as alarm features and clustering the alarms by their occurrences in built transactions. Experimental evaluations demonstrate that up to 61% accuracy improvements can be achieved through utilizing the proposed appropriate metrics compared to a sequential rule mining algorithm.

Several thousands of alarm events are arriving into the network operation center of today's mobile service providers per day. On the other hand, managing this huge amount of information is getting much more difficult as the size of the network infrastructure gets larger. The demo presented in this paper proposes a novel scalable architecture for alarm/event data analyses where alarms are captured, processed and visualized with appropriate notifications in real time. Alarm rules are pre-registered offline into the system through root cause analysis module. The proposed system includes novel methods for streaming data analytics with Complex Event Processing (CEP) using platforms such as Apache Kafka and Storm. The presented proof-of-concept demo helps data center and network operators to monitor continuous alarm streams in order to predict the upcoming alarms and potential network failures.

This paper introduces a sinusoidal modeling technique for low bit rate speech coding wherein the parameters for each sinusoidal component are sequentially extracted by a closed-loop analysis. The sinusoidal modeling of the speech linear prediction (LP) residual is performed within the general framework of matching pursuits with a dictionary of sinusoids. The frequency space of sinusoids is restricted to sets of frequency intervals or bins, which in conjunction with the closed-loop analysis allow us to map the frequencies of the sinusoids into a frequency vector that is efficiently quantized. In voiced frames, two sets of frequency vectors are generated: one of them represents harmonically related and the other one nonharmonically related components of the voiced segment. This approach eliminates the need for voicing dependent cutoff frequency that is difficult to estimate correctly and to quantize at low bit rates. In transition frames, to efficiently extract and quantize the set of frequencies needed for the sinusoidal representation of the LP residual, we introduce frequency bin vector quantization (FBVQ). FBVQ selects a vector of nonuniformly spaced frequencies from a frequency codebook in order to represent the frequency domain information in transition regions. Our use of FBVQ with closed-loop searching contribute to an improvement of speech quality in transition frames. The effectiveness of the coding scheme is enhanced by exploiting the critical band concept of auditory perception in defining the frequency bins. To demonstrate the viability and the advantages of the new models studied, we designed a 4 kbps matching pursuits sinusoidal speech coder. Subjective results indicate that the proposed coder at 4 kbps has quality exceeding the 6.3 kbps G.723.1 coder.

This paper presents a matching pursuits sinusoidal speech coder which incorporates new techniques including a novel vector quantization (VQ) technique used for the weighted quantization of spectral magnitude vector, and an interframe quantization of spectral magnitudes using an interpolation matrix that minimize the weighted interpolation error. The paper describes a novel vector quantization technique, wherein the quantized vector is obtained by applying a linear transformation selected from a first codebook to a codevector selected from a second codebook. The transformation is selected from a family of linear transformations, represented by a matrix codebook. Vectors in the second codebook are called residual codevectors. In order to avoid high complexity during the search for the best linear transformation, each linear transformation is assigned a representative vector, such that the search can be done employing the representative vectors. The VQ design algorithm is based on joint optimization of the linear transformation and the residual codebooks. The introduced techniques are general enough to be used in any sinusoidal speech coding scheme. In this work we incorporated the techniques into the matching pursuits sinusoidal model to achieve high quality speech using sinusoidal speech coder at 4 kbps. Subjective tests indicate that the proposed coding model at 4 kbps has quality comparable to that of G.729 at 8 kbps.

This paper presents a novel vector quantization (VQ) technique in which the quantized vector is formed by adding the transformed outputs of a multistage codebook rather than just adding the outputs of the stages as in regular multistage vector quantization (MSVQ). The transformations are selected from a family of linear transformations represented by a codebook of matrices. This technique can be viewed as a generalized form of MSVQ. If the transformations are constrained to be the identity transformation, this technique becomes identical to the regular MSVQ. The design algorithm is based on joint optimization of the linear transformations and the stage codebooks. It is shown that the proposed technique yields high quality spectral magnitude quantization with performance exceeding that of multistage vector quantization (MSVQ) of similar complexity and bit rate.

We introduce a general and powerful approach to sinusoidal modeling of speech wherein a closed-loop analysis-by-synthesis (AbS) technique sequentially extracts the parameters for each sinusoidal component. Low bit-rate speech coding is achieved by efficiently constraining the allowed frequencies of sinusoidal components into sets of frequency intervals or bins. In conjunction with the closed-loop analysis, the constrained frequency regions allow us to efficiently vector quantize the frequency information in each frame. In voiced frames, two sets of frequency vectors are generated: one for harmonically related components and the other for non-harmonically related components of the voiced segment. In transition frames, a vector of nonuniformly spaced frequencies is selected from a frequency codebook using frequency bin vector quantization (FBVQ) to represent the frequency domain information. The effectiveness of the coding scheme is enhanced by exploiting the critical band concept of auditory perception in defining the frequency bins. In transition segments, the sinusoidal phases are modeled and coded. Subjective tests with a partially quantized model indicate that, for a target rate of 4 kbps, the coder quality exceeds that of the G.729 standard at 8 kbps.