Stacked intelligent metasurfaces (SIMs) have recently emerged as an effective solution for next-generation wireless networks. A SIM comprises multiple metasurface layers that enable signal processing directly in the wave domain. Moreover, recent advances in flexible metamaterials have highlighted the potential of flexible intelligent metasurfaces (FIMs), which can be physically morphed to enhance communication performance. In this paper, we propose a stacked flexible intelligent metasurface (SFIM)-based communication system for the first time, where each metasurface layer is deformable to improve the system's performance. We first present the system model, including the transmit and receive signal models as well as the channel model, and then formulate an optimization problem to maximize the system sum rate under constraints on the transmit power budget, morphing distance, and the unit-modulus condition of the meta-atom responses. To solve this problem, we develop an alternating optimization framework based on the gradient projection method. Simulation results demonstrate that the proposed SFIM-based system achieves significant performance gains compared to its rigid SIM counterpart.

Integrated sensing and communication (ISAC) has emerged as a key paradigm for next-generation wireless systems, which allows wireless resources to be used for data transmission and target sensing simultaneously. In this paper, multi-user collaborative target detection in the uplink ISAC system is investigated. To incorporate the target sensing functionality, the system relies on the reuse of uplink signals from the communication users. Specifically, we analyze an uplink multi-user single-input multiple-output (MU-SIMO) communication system with bistatic sensing. Using the channel statistics, we formulate the problem of joint optimal pilot and data power allocation to maximize the uplink ergodic sum rate while meeting communication and sensing quality-of-service (QoS) requirements. To address this non-convex problem, we propose an alternating optimization (AO)-based iterative framework, where the joint power allocation problem is decomposed into two sub-problems. Specifically, the pilot power allocation is optimized using a penalty dual decomposition (PDD)-based gradient ascent algorithm, while the data power allocation is solved via successive convex approximation (SCA). Once the long-term power allocation is determined, the base station (BS) estimates the instantaneous channels using a minimum mean-squared error (MMSE) estimator. Subsequently, based on the estimated instantaneous channel state information (CSI), the receive beamforming for communication users is optimized via another SCA-based method to maximize the sum rate. Meanwhile, the optimal receive beamforming for the target is obtained in closed-form through eigenvalue decomposition (EVD). We provide comprehensive simulation results to analyze the performance of the proposed iterative algorithm and to demonstrate its dependence on different design parameters. Our results also confirm the superiority of the proposed resource allocation approach over conventional benchmark schemes.

Integrated sensing and communication (ISAC) has emerged as a promising technology for sixth-generation (6G) communication networks. At the same time, ensuring the privacy of targets in ISAC is important in contexts where a malicious sensor is present. In this paper, we investigate a reconfigurable intelligent surface (RIS)-assisted ISAC system designed to protect a sensing region against an adversarial detector (AD), where the base station (BS) has imperfect knowledge of the AD’s location. The RIS consists of both reflecting and absorptive elements (the latter serving as sensing elements), which can be adaptively reconfigured to meet system requirements. Specifically, the system is designed to maximize the jamming power from the BS to the AD by jointly optimizing the transmit beamformer at the BS, the RIS phase-shift matrix, the receive beamformer at the RIS, and the allocation between reflecting and absorptive elements at the RIS while ensuring a minimum sensing signal-to-interference-plus-noise ratio (SINR) at sample points within the sensing region, as well as a minimum communication SINR for each user. To address this challenging optimization problem, we propose an alternating optimization framework combined with a successive convex approximation method tailored for each subproblem. Our results show that the proposed system model offers significant protection of the sensing area compared to the case where the target privacy is not considered. Simulations also confirm that the proposed adaptive RIS partitioning outperforms the fixed RIS partitioning approach.

The upcoming sixth-generation (6G) wireless standard is anticipated to support a variety of applications that will require a seamless integration of sensing and communication services in a single network infrastructure. At the same time, 6 G is also expected to support millions of low-powered Internet-of-Things (IoT) devices. Previous studies have demonstrated that the power requirements of these IoT devices can be met through wireless power transfer facilitated by intelligent metasurfaces. Therefore, in this paper, we try to formulate and answer a fundamental question: How much transmit power is required for an integrated sensing, communication, and power transfer (ISCPT) system? More specifically, we consider the problem of optimal active, passive, and receive beamforming design for a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-enabled ISCPT system with multiple sensing targets, multiple information receivers, and multiple energy receivers, to minimize the required transmit power from the base station, while guaranteeing predefined sensing, communication, and energy harvesting requirements. To tackle the challenging non-convex optimization problem, we use an alternating optimization (AO)-based approach, where the receive beamforming is obtained in closed form while the active and passive beamforming are obtained using a second-order cone program (SOCP) approach. Our numerical results show the benefit of using STAR-RIS to reduce the transmit power requirement for the ISCPT system compared to its corresponding conventional RIS (cRIS)-enabled and non-RIS ISCPT systems.

Active reconfigurable intelligent surfaces (RISs) can improve the performance of integrated sensing and communication (ISAC), and therefore enable simultaneous data transmission and target sensing. However, when the line-of-sight (LoS) link between the base station and the sensing target is blocked, the sensing signals suffer from severe path loss, resulting in an inferior sensing performance. To address this issue, this paper employs a sensor-aided active RIS to enhance ISAC system performance. The goal is to maximize the signal-to-noise ratio of the echo signal from the target at the sensor-array while meeting constraints on communication signal quality, power budgets, and RIS amplification limits. The optimization problem is challenging due to its non-convex nature and the coupling between the optimization variables. We propose a closed-form solution for receive beamforming, and a successive convex approximation based iterative method for transmit and reflection beamforming design. Simulation results demonstrate the advantage of the proposed sensor-aided active RIS-assisted system model over its non-sensor-aided counterpart.

Users in millimeter-wave (mmWave) systems often exhibit diverse channel strengths, which can negatively impact user fairness in resource allocation. Moreover, exact channel state information (CSI) may not be available at the transmitter, rendering suboptimal resource allocation. In this paper, we address these issues within the context of energy efficiency maximization in reconfigurable intelligent surface (RIS)-assisted mmWave systems. We first derive a tractable lower bound on the achievable sum rate, taking into account CSI errors. Subsequently, we formulate the optimization problem, targeting maximizing the system energy efficiency while maintaining a minimum Jain’s fairness index controlled by a tunable design parameter. The optimization problem is very challenging due to the coupling of the optimization variables in the objective function and the fairness constraint, as well as the existence of non-convex equality and fractional constraints. To solve this optimization problem, we employ the penalty dual decomposition method, together with a projected gradient ascent based alternating optimization procedure. The proposed algorithm exhibits linear time complexity with respect to the number of RIS elements. Simulation results demonstrate that the proposed algorithm can achieve an optimal energy efficiency for a prescribed Jain’s fairness index. In addition, adjusting the fairness design parameter can yield a favorable trade-off between energy efficiency and user fairness compared to methods that exclusively focus on optimizing one of these metrics.

The forthcoming sixth-generation (6G) communications standard is anticipated to provide integrated sensing and communication (ISAC) as a fundamental service. These ISAC systems present unique security challenges because of the exposure of information-bearing signals to sensing targets, enabling them to potentially eavesdrop on sensitive communication information with the assistance of sophisticated receivers. Recently, reconfigurable intelligent surfaces (RISs) have shown promising results in enhancing the physical layer security of various wireless communication systems, including ISAC. However, the performance of conventional passive RIS (pRIS)-enabled systems are often limited due to multiplicative fading, which can be alleviated using active RIS (aIRS). In this paper, we consider the problem of beampattern gain maximization in a secure pRIS/aRIS-enabled ISAC system, subject to signal-to-interference-plus-noise ratio constraints at communication receivers, and information leakage constraints at an eavesdropping target. For the challenging non-convex problem of joint beamforming design at the base station and the pRIS/aRIS, we propose a novel successive convex approximation (SCA)-based method. Unlike the conventional alternating optimization (AO)-based methods, in the proposed SCA-based approach, all of the optimization variables are updated simultaneously in each iteration. The proposed method shows significant performance superiority for pRIS-aided ISAC system compared to a benchmark scheme using penalty-based AO method. Moreover, our simulation results also confirm that aRIS-aided system has a notably higher beampattern gain at the target compared to that offered by the pRIS-aided system for the same power budget. We also present a detailed complexity analysis and proof of convergence for the proposed SCA-based method.

During conversations, the human flow of thoughts may result in topic shifts and evolution.In open-domain dialogue systems, it is crucial to track the topics discussed and recommend relevant topics to be included in responses to have effective conversations.Furthermore, topic evolution is needed to prevent stagnation as conversation length increases.Existing open-domain dialogue systems do not pay sufficient attention to topic evolution and shifting, resulting in performance degradation due to ineffective responses as conversation length increases.To address the shortcomings of existing approaches, we propose EVOLV-CONV.EVOLVCONV conducts real-time conversation topic and user preference tracking and utilizes the tracking information to evolve and shift topics depending on conversation status.We conduct extensive experiments to validate the topic evolving and shifting capabilities of EVOLVCONV as conversation length increases.Un-referenced evaluation metric UniEval compare EVOLVCONV with the baselines.Experimental results show that EVOLV-CONV maintains a smooth conversation flow without abruptly shifting topics; the probability of topic shifting ranges between 5%-8% throughout the conversation.EVOLVCONV recommends 4.77% more novel topics than the baselines, and the topic evolution follows balanced topic groupings.Furthermore, we conduct user surveys to test the practical viability of EVOLVCONV.User survey results reveal that responses generated by EVOLVCONV are preferred 47.8% of the time compared to the baselines and comes second to real human responses.

Minqian Liu, Ying Shen, Zhiyang Xu, Yixin Cao, Eunah Cho, Vaibhav Kumar, Reza Ghanadan, Lifu Huang. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

In the rapidly advancing landscape of System-on-Chips (SoCs), achieving appropriate levels of functional safety compliance has become a fundamental concern. With SoCs serving critical roles in applications such as automotive, industrial, and consumer electronics, a unified approach to navigate different functional safety standards can help save time and speed up time to market. By reusing the fundamental principles of ISO 26262 and IEC 61508, this article presents a unified framework tailored specifically for advanced multi-domain SoCs. This framework seeks to streamline safety compliance efforts while enhancing overall system safety resilience for various applications.

Integrated sensing and communication (ISAC) is expected to be offered as a fundamental service in the upcoming sixth-generation (6G) communications standard. However, due to the exposure of information-bearing signals to the sensing targets, ISAC poses unique security challenges. In recent years, intelligent reflecting surfaces (IRSs) have emerged as a novel hardware technology capable of enhancing the physical layer security of wireless communication systems. Therefore, in this paper, we consider the problem of transmit and reflective beamforming design in a secure IRS-enabled ISAC system to maximize the beampattern gain at the target. The formulated non-convex optimization problem is challenging to solve due to the intricate coupling between the design variables. Moreover, alternating optimization (AO) based methods are inefficient in finding a solution in such scenarios, and convergence to a stationary point is not theoretically guaranteed. Therefore, we propose a novel successive convex approximation (SCA)-based second-order cone programming (SOCP) scheme in which all of the design variables are updated simultaneously in each iteration. The proposed SCA-based method significantly outperforms a penalty-based benchmark scheme previously proposed in this context. Moreover, we also present a detailed complexity analysis of the proposed scheme, and show that despite having slightly higher per-iteration complexity than the benchmark approach the average problem-solving time of the proposed method is notably lower than that of the benchmark scheme.

Reconfigurable intelligent surfaces (RISs) are considered to be a promising solution to overcome the blockage issue in the millimeter-wave (mmWave) band. Energy efficiency is an important performance metric in RIS-assisted mmWave systems with a large number of antennas. However, due to the severe path loss in mmWave systems, resource allocation algorithms tend to allocate most of the resources for the benefit of the users with higher channel gains. In this paper, we propose a lexicographic-based approach to find the optimal power allocation, RIS passive beamforming matrix, and analog precoders that maximize both energy efficiency and user fairness. We solve the corresponding multi-objective optimization problem in two stages. In the first stage, we maximize the energy efficiency, and in the second stage we maximize the fairness subject to a minimum energy efficiency constraint. We propose an alternating optimization procedure to solve the optimization problem in each stage. The optimal power allocation is found using Dinkelbach's method and convex optimization techniques in the first and second stage respectively, the RIS phase shift matrix is found using a gradient ascent algorithm, and the analog precoder is determined using beam alignment. Numerical results show that the proposed algorithm can achieve an excellent trade-off between the energy efficiency and fairness by boosting the minimum weighted rate with a minor and controllable reduction in the energy efficiency.

This paper focuses on the fundamental problem of maximizing the achievable weighted sum rate (WSR) at information receivers (IRs) in an intelligent reflecting surface (IRS) assisted simultaneous wireless information and power transfer system under a multiple-input multiple-output (SWIPT-MIMO) setting, subject to a quality-of-service (QoS) constraint at the energy receivers (ERs). Notably, due to the coupling between the transmit precoding matrix and the passive beamforming vector in the QoS constraint, the formulated non-convex optimization problem is challenging to solve. We first decouple the design variables in the constraints following a penalty dual decomposition method, and then apply an alternating gradient projection algorithm to achieve a stationary solution to the reformulated optimization problem. The proposed algorithm nearly doubles the WSR compared to that achieved by a block-coordinate descent (BCD) based benchmark scheme. At the same time, the complexity of the proposed scheme grows linearly with the number of IRS elements while that of the benchmark scheme is proportional to the cube of the number of IRS elements.

In safety-critical applications, e.g., automotive, how to avoid or alleviate risk due to hazards caused by silicon/IC malfunction is a key field, aka functional safety (FuSa). While there have been several standards established for FuSa, e.g., ISO 26262, there are still many technical challenges in this field, for example, tradeoff between cost and effectiveness, new safety mechanism for transient, intermittent or degrading faults. In this session we invited the industry experts to present the latest developments in these domains and share their insights.

Intelligent reflecting surfaces (IRSs) have shown huge advantages in many potential use cases and thus have been considered a promising candidate for next-generation wireless systems. In this paper, we consider an IRS-assisted multigroup multicast (IRS-MGMC) system in a multiple-input single-output (MISO) scenario, for which the related existing literature is rather limited. In particular, we aim to jointly design the transmit beamformers and IRS phase shifts to maximize the sum rate of the system under consideration. In order to obtain a numerically efficient solution to the formulated non-convex optimization problem, we propose an alternating projected gradient (APG) method where each iteration admits a closed-form and is shown to be superior to a known solution that is derived from the majorization-minimization (MM) method in terms of both achievable sum rate and required complexity, i.e., run time. In particular, we show that the complexity of the proposed APG method grows linearly with the number of IRS tiles, while that of the known solution in comparison grows with the third power of the number of IRS tiles. The numerical results reported in this paper extend our understanding on the achievable rates of large-scale IRS-assisted multigroup multicast systems.

Security and energy efficiency have become crucial features in the modern-era wireless communication. In this paper, we consider an energy-efficient design for intelligent reflecting surface (IRS)-assisted multiple-input multiple-output multiple-eavesdropper (MIMOME) wiretap channels (WTC). Our objective is to jointly optimize the transmit covariance matrix and the IRS phase-shifts to maximize the secrecy energy efficiency (SEE) of the considered system subject to a secrecy rate constraint at the legitimate receiver. To tackle this challenging non-convex problem in which the design variables are coupled in the objective and the constraint, we propose a penalty dual decomposition based alternating gradient projection (PDDAPG) method to obtain an efficient solution. We also show that the computational complexity of the proposed algorithm grows only linearly with the number of reflecting elements at the IRS, as well as with the number of antennas at transmitter/receivers’ nodes. Our results confirm that using an IRS is helpful to improve the SEE of MIMOME WTC compared to its no-IRS counterpart only when the power consumption at IRS is small. In particular, and a large-sized IRS is not always beneficial for the SEE of a MIMOME WTC.

Reconfigurable intelligent surface (RIS) assisted radio is considered as an enabling technology with great potential for the sixth-generation (6G) wireless communications standard. The achievable secrecy rate (ASR) is one of the most fundamental metrics to evaluate the capability of facilitating secure communication for RIS-assisted systems. However, the definition of ASR is based on Shannon's information theory, which generally requires long codewords and thus fails to quantify the secrecy of emerging delay-critical services. Motivated by this, in this paper we investigate the problem of maximizing the secrecy rate under a delay-limited quality-of-service (QoS) constraint, termed as the effective secrecy rate (ESR), for an RIS-assisted multiple-input single-output (MISO) wiretap channel subject to a transmit power constraint. We propose an iterative method to find a stationary solution to the formulated non-convex optimization problem using a block coordinate ascent method (BCAM), where both the beamforming vector at the transmitter as well as the phase shifts at the RIS are obtained in closed forms in each iteration. We also present a convergence proof, an efficient implementation, and the associated complexity analysis for the proposed method. Our numerical results demonstrate that the proposed optimization algorithm converges significantly faster that an existing solution. The simulation results also confirm that the secrecy rate performance of the system with stringent delay requirements reduces significantly compared to the system without any delay constraints, and that this reduction can be significantly mitigated by an appropriately placed large-size RIS.

Question answering and conversational systems are often baffled and need help clarifying certain ambiguities.However, limitations of existing datasets hinder the development of large-scale models capable of generating and utilising clarification questions.In order to overcome these limitations, we devise a novel bootstrapping framework (based on self-supervision) that assists in the creation of a diverse, large-scale dataset of clarification questions based on post-comment tuples extracted from stackexchange.The framework utilises a neural network based architecture for classifying clarification questions.It is a two-step method where the first aims to increase the precision of the classifier and second aims to increase its recall.We quantitatively demonstrate the utility of the newly created dataset by applying it to the downstream task of question-answering.The final dataset, ClarQ, consists of ∼2M examples distributed across 173 domains of stackexchange.We release this dataset 1 in order to foster research into the field of clarification question generation with the larger goal of enhancing dialog and question answering systems.

Given a natural language query, teaching machines to ask clarifying questions is of immense utility in practical natural language processing systems. Such interactions could help in filling information gaps for better machine comprehension of the query. For the task of ranking clarification questions, we hypothesize that determining whether a clarification question pertains to a missing entry in a given post (on QA forums such as StackExchange) could be considered as a special case of Natural Language Inference (NLI), where both the post and the most relevant clarification question point to a shared latent piece of information or context. We validate this hypothesis by incorporating representations from a Siamese BERT model fine-tuned on NLI and Multi-NLI datasets into our models and demonstrate that our best performing model obtains a relative performance improvement of 40 percent and 60 percent respectively (on the key metric of [email protected]), over the state-of-the-art baseline(s) on the two evaluation sets of the StackExchange dataset, thereby, significantly surpassing the state-of-the-art.

Non-orthogonal multiple access (NOMA) has drawn tremendous attention, being a potential candidate for the spectrum access technology for the fifth-generation (5G) and beyond 5G(B5G) wireless communications standards. Most research related to NOMA focuses on the system performance from Shannon's capacity perspective, which, although a critical system design criterion, fails to quantity the effect of delay constraints imposed by future wireless applications. In this paper, we analyze the performance of a single-input multiple-output (SIMO) two-user downlink NOMA system, in terms of the link-layer achievable rate, known as effective capacity (EC), which captures the performance of the system under a delay-limited quality-of-service (QoS) constraint. For signal combining at the receiver side, we use generalized selection combining (GSC), which bridges the performance gap between the two conventional diversity combining schemes, namely selection combining (SC) and maximal-ratio combining (MRC). We also derive two approximate expressions for the EC of NOMA-GSC which are accurate at low-SNR and at high-SNR, respectively. The analysis reveals a tradeoff between the number of implemented receiver radio-frequency (RF) chains and the achieved performance, and can be used to determine the appropriate number of paths to combine in a practical receiver design.

In this paper, we tackle the issue of timbre transfer on a given monophonic music sample. The objective is to change the timbre of source audio from one instrument to another while preserving features such as loudness, pitch, and rhythm. Existing approaches use image-to-image translation techniques on the entire region of time-frequency representations of the raw audio wave, which may lead to the addition of unwanted elements in the final audio waveform. We propose Attention-based Timbre Transfer (ATT), an attention-based pipeline for transferring timbre. To the best of our knowledge, ATT is the first approach which leverages attention for achieving timbre transfer. Further, ATT uses MelGAN for spectrogram inversion, which provides a fast and parallel alternative to other autoregressive music generation approaches, without compromising on the quality. ATT shows promising results, thus efficaciously transferring timbre with minimal offset to other physical characteristics.

CAsT-19 is a new dataset that supports research on conversational information seeking. The corpus is 38,426,252 passages from the TREC Complex Answer Retrieval (CAR) and Microsoft MAchine Reading COmprehension (MARCO) datasets. Eighty information seeking dialogues (30 train, 50 test) are an average of 9 to 10 questions long. A dialogue may explore a topic broadly or drill down into subtopics. Questions contain ellipsis, implied context, mild topic shifts, and other characteristics of human conversation that may prevent them from being understood in isolation. Relevance assessments are provided for 30 training topics and 20 test topics.

Word embeddings are the standard model for semantic and syntactic representations of words. Unfortunately, these models have been shown to exhibit undesirable word associations resulting from gender, racial, and religious biases. Existing post-processing methods for debiasing word embeddings are unable to mitigate gender bias hidden in the spatial arrangement of word vectors. In this paper, we propose RAN-Debias, a novel gender debiasing methodology that not only eliminates the bias present in a word vector but also alters the spatial distribution of its neighboring vectors, achieving a bias-free setting while maintaining minimal semantic offset. We also propose a new bias evaluation metric, Gender-based Illicit Proximity Estimate (GIPE), which measures the extent of undue proximity in word vectors resulting from the presence of gender-based predilections. Experiments based on a suite of evaluation metrics show that RAN-Debias significantly outperforms the state-of-the-art in reducing proximity bias (GIPE) by at least 42.02%. It also reduces direct bias, adding minimal semantic disturbance, and achieves the best performance in a downstream application task (coreference resolution).

Non-orthogonal multiple access (NOMA) is widely recognized as a potential multiple access technology for efficient radio spectrum utilization in the fifth-generation (5G) wireless communications standard. In this paper, we study the average achievable rate and outage probability of a cooperative relaying system (CRS) based on NOMA (CRS-NOMA) over wireless links governed by the α-μ generalized fading model; here α and μ designate the nonlinearity and clustering parameters, respectively, of each link. The average achievable rate is represented in closed-form using Meijer's G-function and the extended generalized bivariate Fox's H-function (EGBFHF), and the outage probability is represented using the lower incomplete Gamma function. Our results confirm that the CRS-NOMA outperforms the CRS with conventional orthogonal multiple access (CRS-OMA) in terms of spectral efficiency at high transmit signal-to-noise ratio (SNR). It is also evident from our results that with an increase in the value of the nonlinearity/clustering parameter, the SNR at which the CRS-NOMA outperforms its OMA based counterpart becomes higher. Furthermore, the asymptotic analysis of the outage probability reveals the dependency of the diversity order of each symbol in the CRS-NOMA system on the α and μ parameters of the fading links.

Emergency Response Services (ERS) in the developing countries face a dual challenge of lack of resources and selection of optimal locations to distribute the available resources. Moreover, the exclusion of urban space details in the previous models leads to unfeasible solutions. Due to variations in the travel time and coverage demand throughout the day, static location problem is not sufficient to provide good solutions. Hence, we propose an approach to incorporate dynamic aspects like demand, travel time, and coverage area in developing an asset location model. We also incorporate the influence of urban settlement elements like built-up compactness etc. in the model to find the suitable locations for building mini and large fire stations. Our formulated a mixed integer program tries to maximize the empirical demand coverage by firefighting vehicles. The solution is applied to the southern region of Mumbai. When compared to the existing scenario an approximate increase of 10 to 15% increase in the demand coverage by various vehicles is achieved. It is observed that increasing the resources further increases the demand coverage.

In this paper, we present the error performance analysis of a multiple-input multiple-output (MIMO) physical-layer network coding (PNC) system with two different user-antenna selection (AS) schemes in asymmetric channel conditions. For the first antenna selection scheme (AS1), where the user antenna is selected in order to maximize the overall channel gain between the user and the relay, we give an explicit analytical proof that for binary modulations, the system achieves full diversity order of min(NA, NB) × NRin the multiple-access (MA) phase, where NA, NB, and NRdenote the number of antennas at user A, user B, and relay R, respectively. We present a detailed investigation of the diversity order for the MIMO-PNC system with AS1 in the MA phase for any modulation order. A tight closed-form upper bound on the average SER is also derived for the special case when NR= 1, which is valid for any modulation order. We show that in this case, the system fails to achieve transmit diversity in the MA phase, as the system diversity order drops to 1 irrespective of the number of transmit antennas at the user nodes. Additionally, we propose a Euclidean distance (ED) based user-antenna selection scheme (AS2) that outperforms the first scheme in terms of error performance. Moreover, by deriving upper and lower bounds on the diversity order for the MIMO-PNC system with AS2, we show that this system enjoys both transmit and receive diversity, achieving full diversity order of min(NA, NB) × NRin the MA phase for any modulation order. Monte Carlo simulations are provided which confirm the correctness of the derived analytical results.

Non-orthogonal multiple access (NOMA) and spectrum sharing (SS) are two emerging multiple access technologies for efficient spectrum utilization in future wireless communications standards. In this paper, we present the performance analysis of a NOMA-based cooperative relaying system (CRS) in an underlay spectrum sharing scenario, considering a peak interference constraint (PIC), where the peak interference inflicted by the secondary (unlicensed) network on the primary-user (licensed) receiver (PU-Rx) should be less than a predetermined threshold. In the proposed system the relay and the secondary-user receiver (SU-Rx) are equipped with multiple receive antennas and apply selection combining (SC), where the antenna with highest instantaneous signal-to-noise ratio (SNR) is selected, and maximal-ratio combining (MRC), for signal reception. Closed-form expressions are derived for the average achievable rate and outage probabilities for SS-based CRS-NOMA. These results show that for large values of peak interference power, the SS-based CRS-NOMA outperforms the CRS with conventional orthogonal multiple access (OMA) in terms of spectral efficiency. The effect of the interference channel on the system performance is also discussed, and in particular, it is shown that the interference channel between the secondary-user transmitter (SU-Tx) and the PU-Rx has a more severe effect on the average achievable rate as compared to that between the relay and the PU-Rx. A close agreement between the analytical and numerical results confirm the correctness of our rate and outage analysis.

Popular methods for news recommendation which are based on collaborative filtering and content-based filtering have multiple drawbacks. The former method does not account for the sequential nature of news reading and suffers from the problem of cold-start, while the latter, suffers from over-specialization. In order to address these issues for news recommendation we propose a Hybrid Recurrent Attention Machine (HRAM). HRAM consists of two components. The first component utilizes a neural network for matrix factorization. While in the second component, we first learn the distributed representation of each news article. We then use the historical data of the user in a sequential manner and feed it to an attention-based recurrent layer. Finally, we concatenate the outputs from both these components and use further hidden layers in order to make predictions. In this way, we harness the information present in the user reading history and boost it with the information available through collaborative filtering for providing better news recommendations. Extensive experiments over two real-world datasets show that the proposed model provides significant improvement over the state-of-the-art.

An effective news recommendation system should harness the historical information of the user based on her interactions as well as the content of the articles. In this paper we propose a novel deep learning model for news recommendation which utilizes the content of the news articles as well as the sequence in which the articles were read by the user. To model both of these information, which are essentially of different types, we propose a simple yet effective architecture which utilizes a 3-dimensional Convolutional Neural Network which takes the word embeddings of the articles present in the user history as its input. Using such a method endows the model with the capability to automatically learn spatial (features of a particular article) as well as temporal features (features across articles read by a user) which signify the interest of the user. At test time, we use this in combination with a 2-dimensional Convolutional Neural Network for recommending articles to users. On a real-world dataset our method outperformed strong baselines which also model the news recommendation problem using neural networks.

Online media outlets, in a bid to expand their reach and subsequently increase revenue through ad monetisation, have begun adopting clickbait techniques to lure readers to click on articles. The article fails to fulfill the promise made by the headline. Traditional methods for clickbait detection have relied heavily on feature engineering which, in turn, is dependent on the dataset it is built for. The application of neural networks for this task has only been explored partially. We propose a novel approach considering all information found in a social media post. We train a bidirectional LSTM with an attention mechanism to learn the extent to which a word contributes to the post's clickbait score in a differential manner. We also employ a Siamese net to capture the similarity between source and target information. Information gleaned from images has not been considered in previous approaches. We learn image embeddings from large amounts of data using Convolutional Neural Networks to add another layer of complexity to our model. Finally, we concatenate the outputs from the three separate components, serving it as input to a fully connected layer. We conduct experiments over a test corpus of 19538 social media posts, attaining an F1 score of 65.37% on the dataset bettering the previous state-of-the-art, as well as other proposed approaches, feature engineering or otherwise.

Physical-layer network coding (PNC) is now well- known as a potential candidate for delay-sensitive and spectrally efficient communication applications, especially in two-way relay channels (TWRCs). In this paper, we present the error performance analysis of a multiple-input single- output (MISO) fixed network coding (FNC) system with two different transmit antenna selection (TAS) schemes. For the first scheme, where the antenna selection is performed based on the strongest channel, we derive a tight closed-form upper bound on the average symbol error rate (SER) with M-ary modulation and show that the system achieves a diversity order of 1 for M > 2. Next, we propose a Euclidean distance (ED) based antenna selection scheme which outperforms the first scheme in terms of error performance and is shown to achieve a diversity order lower bounded by the minimum of the number of antennas at the two users.

Efficient heterogeneous communication technologies are critical components to provide flawless connectivity in smart cities. The proliferation of wireless technologies, services and communication devices has created the need for green and spectrally efficient communication technologies. Physical-layer network coding (PNC) is now well-known as a potential candidate for delay-sensitive and spectrally efficient communication applications, especially in bidirectional relaying, and is therefore well-suited for smart city applications. In this paper, we provide a brief introduction to PNC and the associated distance shortening phenomenon which occurs at the relay. We discuss the issues with existing schemes that mitigate the deleterious effect of distance shortening, and we propose simple and effective solutions based on the use of multiple antenna systems. Simulation results confirm that full diversity order can be achieved in a PNC system by using antenna selection schemes based on the Euclidean distance metric.

The TV white space (TVWS) or equivalently the spatially available unused TV spectrum has drawn much attention recently due to its availability for opportunistic access by unlicensed users in fulfilling the spectrum demands. In this paper, we have evaluated the capacity of secondary (unlicensed) user network meeting the constraint on the aggregated secondary interference power at the TV receiver. Particular importance has been given to the interference modeling to define the protection region around the TV-receiver as per the Federal Communications Committee (FCC) rules. The impact of different system parameters such as power, transmission range, and density of secondary users contending for the same TV channel are also explored in the paper for different channel scenarios.

Diversity reception schemes are well-known effective techniques for mitigating the adverse effects of multipath fading in wireless mobile channels. This paper analyzes the performance of an amplify-and-forward (AF) relay-based cooperative spectrum sensing system with generalized selection combining (GSC) over a Rayleigh fading channel and compare the results with those of the conventional diversity combining schemes such as maximal-ratio-combining (MRC) and selection combining (SC). Novel closed-form expression has been derived for the average detection probability over the independently and identically distributed (i.i.d) diversity paths. Receiver operating characteristics (ROCs) and the average detection probability versus the average signal-to-noise ratio (SNR) curves have been presented for different scenarios of interest.