Coherent Interval Research Articles

Analyzing Time-Frequency Relations between Agricultural Commodities and Equity Index in India during Covid-19 Pandemic

The study examines the interdependence between the returns of Nifty 50 Equity Index and Agricultural Commodities cotton, mentha oil, guar seed, jeera, turmeric and coriander over a period of time. By applying wavelet analysis, the interdependence of agricultural commodities and equity index during the COVID-19 pandemic, identified periods of both strong and weak correlations between these markets. It is critical to note that the reasons for the link between agricultural commodities and equity indexes during COVID-19 pandemic may vary according to market conditions, regional considerations and other contextual factors. According to this study, Nifty 50 has higher co-movements with MCX Cotton, MCX Mentha Oil, and NCDEX Turmeric and lower co-movements with NCDEX Guar seed, NCDEX Jeera and NCDEX Coriander. The low coherence intervals indicate the ability for commodities investments to diversify in the face of a pandemic such as Covid-19. The observed trends by commodity category revealed their potential utility in the development of cross-asset hedge strategies. Thus, combining commodities and stocks increases performance over a variety of investment horizons.. KEYWORDS :Commodities market, Equity market, Co-movements, Wavelet analysis, Covid-19.

Stock market prices and Dividends in the US: Bubbles or Long-run equilibria relationships?

This paper presents a novel approach to identifying potential bubbles in the US stock market by employing alternative time series methods based on long memory, including fractional integration and cointegration, as well as duration dependence non-parametric models. To test for duration dependence, the paper employs a unique non-parametric hazard function estimation method, using monotonic P-splines, which enables the estimation of a coherent and sophisticated Bayesian confidence interval for the function. By analyzing historical data of real US stock index from January 1871 to November 2022, the results reveal strong evidence of bubbles in the US stock market. It is worth noting that this is the first time a fractional integration approach and a novel non-parametric hazard function have been utilized to test for rational speculative bubbles in the US stock market, making this paper a significant contribution to the field.

Credal Valuation Networks for Machine Reasoning Under Uncertainty

Contemporary undertakings provide limitless opportunities for widespread application of machine reasoning and artificial intelligence in situations characterised by uncertainty, hostility and sheer volume of data. The paper develops a valuation network as a graphical system for higher-level fusion and reasoning under uncertainty in support of the human operators. Valuations, which are mathematical representation of (uncertain) knowledge and collected data, are expressed as credal sets, defined as coherent interval probabilities in the framework of imprecise probability theory. The basic operations with such credal sets, combination and marginalisation, are defined to satisfy the axioms of a valuation algebra. A practical implementation of the credal valuation network is discussed and its utility demonstrated on a small scale example.

Estimation of Uplink Channels for Multiple Users Using Tensor Modeling in RIS-Aided MISO Communication

In this paper estimation of uplink channels using tensor modeling is addressed for multiple users in a reconfigurable intelligent surface (RIS)-aided multiple-input single-output (MISO) communication. The coherence interval is divided into structured frames of pilot symbols transmitted by the users and pattern of phase shifts applied by the RIS in order to estimate the base station (BS)-RIS channels and the RIS-user’s channels. Estimation methods that use tensor modeling including Khatri-Rao Factorization (KRF) and bilinear alternating least squares (BALS) are applied to the signal model. Numerical results show that both KRF and BALS are superior to the LS estimator by 10 dB SNR for the correlated Rayleigh fading channel model.

Shell microstructures (disturbance lines) of Arctica islandica (Bivalvia): a potential proxy for severe oxygen depletion

The spread of oxygen deficiency in nearshore coastal habitats endangers benthic communities. To better understand the mechanisms leading to oxygen depletion and eventually hypoxia, predict the future development of affected ecosystems, and define suitable mitigation strategies requires detailed knowledge of the dissolved oxygen (DO) history. Suitable high-resolution DO archives covering coherent time intervals of decades to centuries include bivalve shells. Here, we explored if the microstructure, specifically disturbance lines, in shells of Arctica islandica from the Baltic Sea can be used as an alternative or complementary proxy to Mn/Cashell to track the frequency and severity of past low-DO events. Disturbance lines differ from periodic annual growth lines by the presence of fine complex crossed lamellae instead of irregular simple prisms. Aside from a qualitative assessment of microstructural changes, the morphology of individual biomineral units (BMUs) was quantitatively determined by artificial intelligence-assisted image analysis to derive models for DO reconstruction. As demonstrated, Mn-rich disturbance lines can provide a proxy for past deoxygenation events (i.e., DO < 45 µmol/L), but it currently remains unresolved if low DO leads to microstructurally distinct features that differ from those caused by other environmental stressors. At least in studied specimens from the Baltic Sea and Iceland, low temperature, salinity near the lower physiological tolerance, or food scarcity did not result in disturbance lines. With decreasing DO supply, disturbance lines seem to become more prominent, contain more Mn, and consist of increasingly smaller and more elongated BMUs with a larger perimeter-to-area ratio. Although the relationship between DO and BMU size or elongation was statistically significant, the explained variability (<1.5%) was too small and the error too large to reconstruct DO values. BMU parameters may reveal a closer relationship with DO if studied in three dimensions and if the DO content was determined at high resolution, directly at the position where the bivalves lived, something that future work should address.

Two-Timescale Transmission Design and RIS Optimization for Integrated Localization and Communications

Reconfigurable intelligent surfaces (RISs) have tremendous potential to boost communication performance, especially when the line-of-sight (LOS) path between the user equipment (UE) and base station (BS) is blocked. To control the RIS, channel state information (CSI) is needed, which entails significant pilot overhead. To reduce this overhead and the need for frequent RIS reconfiguration, we propose a novel framework for integrated localization and communications, where RIS configurations are fixed during location coherence intervals, while BS precoders are optimized every channel coherence interval. This framework leverages accurate location information obtained with the aid of several RISs as well as novel RIS optimization and channel estimation methods. Performance in terms of localization accuracy, channel estimation error, and achievable rate demonstrates the effectiveness of the proposed approach.

Detecting Abrupt Change in Channel Covariance Matrix for MIMO Communication

The acquisition of the channel covariance matrix is of paramount importance to many strategies in multiple-input-multiple-output (MIMO) communications, such as the minimum mean-square error (MMSE) channel estimation. Therefore, plenty of efficient channel covariance matrix estimation schemes have been proposed in the literature. However, an abrupt change in the channel covariance matrix may happen occasionally in practice due to the change in the scattering environment and the user location. Our paper aims to adopt the classic change detection theory to detect the change in the channel covariance matrix as accurately and quickly as possible such that the new covariance matrix can be re-estimated in time. Specifically, this paper first considers the technique of on-line change detection (also known as quickest/sequential change detection), where we need to detect whether a change in the channel covariance matrix occurs at each channel coherence time interval. Next, because the complexity of detecting the change in a high-dimension covariance matrix at each coherence time interval is too high, we devise a low-complexity off-line strategy in massive MIMO systems, where change detection is merely performed at the last channel coherence time interval of a given time period. Numerical results show that our proposed on-line and off-line schemes can detect the channel covariance change with a small delay and a low false alarm rate. Therefore, our paper theoretically and numerically verifies the feasibility of detecting the channel covariance change accurately and quickly in practice.

An efficient location-based pilot assignment in Cell-Free Massive MIMO

Cell-free Massive multiple-input multiple-output can offer many degrees of freedom for the high data rate and coverage probability. This network topology is one of the cutting-edge technologies potentially applied for next-generation wireless communications. In the canonical structure, however, each access point needs to accurately estimate the channel state information for the signal detection in the uplink and the precoding design in the downlink. Due to the finite lengths of coherence intervals, pilot contamination is one of the bottlenecks, reducing the data throughput of each user. We propose an efficient pilot assignment scheme called location-based pilot assignment (LPA) to overcome this limitation. It takes full advantage of the number of orthogonal pilot signals into account. The proposed algorithm divides the coverage area into smaller areas based on the number of users, where each area uses a subset of the pilot signals. Numerical results show that the proposed pilot assignment, LPA, offers good spectral efficiency to every users in the network with a tolerable computational complexity.

Cooperative RIS and STAR-RIS Assisted mMIMO Communication: Analysis and Optimization

Reconfigurable intelligent surface (RIS) has emerged as a cost-effective and promising solution to extend the wireless signal coverage and improve the performance via passive signal reflection. Different from existing works which do not account for the cooperation between RISs or do not provide full space coverage, we propose the marriage of cooperative double-RIS with simultaneously transmitting and reflecting RIS (STAR-RIS) technologies denoted as RIS/STAR-RIS under correlated Rayleigh fading conditions to assist the communication in a massive multiple-input multiple-output (mMIMO) setup. The proposed architecture is superior since it enjoys the benefits of the individual designs. We introduce a channel estimation approach of the cascaded channels with reduced overhead. Also, we obtain the deterministic equivalent (DE) of the downlink achievable sum spectral efficiency (SE) in closed form based on large-scale statistics. Notably, relied on statistical channel state information (CSI), we optimise both surfaces by means of the projected gradient ascent method (PGAM), and obtain the gradients in closed form. The proposed optimization achieves to maximise the sum SE of such a complex system, and has low complexity and low overhead since it can be performed at every several coherence intervals. Numerical results show the benefit of the proposed architecture and verify the analytical framework. In particular, we show that the RIS/STAR-RIS architecture outperforms the conventional double-RIS or its single-RIS counterparts.

Determination of the coherence interval of radar targets using a multipoint signal reflection model

The problem of determining the coherence interval of the target (CIT) is considered – the time interval during which the signal reflected from the target can be considered coherent. In this case, the criterion of signal coherence is the condition of limiting the random phase fluctuation of the reflected signal to a value. The basis of the method of determining the CIT is the use of a multipoint model of a target that performs a rectilinear motion at a constant speed. When describing the resulting signal, the complex envelope method is used. It is shown that for an arbitrary number of reflection points, the resulting signal can be represented as a product of two functions. The first function is deterministic and determines the dependence of the signal phase on the speed of movement of the target center. The second function determines the amplitude and phase fluctuations of the reflected signal and, due to the unknown number and location of the reflection points, as well as the angle of observation of the target, can be considered random. In the case of a two-point target model, an approximate ratio for the CIT is obtained, depending on the relative size of the target, its range and speed of movement. It is shown that for typical target sizes and ranges of the order of several tens of kilometers, the errors in calculating the CIT when using the obtained approximate ratio do not exceed 10% of the true value of the CIT. It is also shown that the frequently used approach of calculating the CIT based on the calculation of the backscattering diagram of the target does not take into account the target range and therefore can lead to large errors in calculating the CIT. At the same time, the calculated values of the CIT, depending on the size of the target and its range, may differ from the true values by an order of magnitude or more. When switching to the multipoint model of the target, differences in comparison with the two-point model in the calculation of the CIT are determined and estimates of the most rational number of reflection points are obtained, the choice of which can be limited to five or six points.

Time-Frequency Analysis of COVID-19 Shocks and Energy Commodities

In a time-frequency biwavelet framework, we analysed the short-, medium-, and long-term impacts of COVID-19-related shocks on ten energy commodities (i.e., Brent, crude oil, coal, heating oil, natural gas, gasoline, ethanol, naphtha, propane, and uranium) from January 2020 to April 2022. We document intervals of high and low coherence between COVID-19 cases and the returns on energy commodities across the short-, medium-, and long-term horizons. Low coherence at high frequencies indicated weak correlation and signified diversification, hedging, and safe-haven potentials in the short term of the pandemic. Our findings suggest that energy markets’ dynamics were highly driven by the pandemic, causing significant changes in market returns, particularly across the medium- and low-frequency bands. Furthermore, the empirical results indicate dynamic lead-lag relationships between COVID-19 cases and energy returns between the medium- and long-term horizons, signifying that diversification could be sought through crossinvestment in different energy commodities. The results have significant implications for market participants, regulators, and practitioners.

Two-Timescale Design for Uplink Systems: How Many IRS Beamforming Patterns Are Needed?

In this paper, we develop a new dynamic intelligent reflecting surface (IRS) beamforming (BF) framework for an IRS aided energy-constrained Internet-of-Things system, where multiple IRS BF patterns are employed to assist uplink transmission. To alleviate the channel estimation overhead incurred by the IRS, a novel two-timescale scheme is proposed to maximize the ergodic sum-rate. Specifically, multiple IRS BF patterns are first optimized based on the statistical channel state information (CSI). Under the obtained IRS BF patterns, the transmit power and time allocation for devices in each channel coherence interval are optimized based on the instantaneous CSI. By characterizing the lower bound of the objective value, we further analytically quantify the performance degradation incurred by using less IRS BF patterns to shed light on the fundamental performance-overhead tradeoff. Simulation results validate the effectiveness of our proposed design and demonstrate that only <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$33\%$</tex-math></inline-formula> of the total IRS BF patterns are needed to maintain <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$95\%$</tex-math></inline-formula> of the maximum achieved ergodic sum-rate.

Spectral-Efficiency of Cell-Free Massive MIMO With Multicarrier-Division Duplex

A multicarrier-division duplex (MDD)-based cell-free (CF) scheme, namely MDD-CF, is proposed, which enables downlink (DL) data and uplink (UL) data or pilots to be concurrently transmitted on mutually orthogonal subcarriers in distributed CF massive MIMO (mMIMO) systems. To demonstrate the advantages of MDD-CF, we firstly study the spectral-efficiency (SE) performance in terms of one coherence interval (CT) associated with access point (AP)-selection, power- and subcarrier-allocation. Since the formulated SE optimization is a mixed-integer non-convex problem that is NP-hard to solve, we leverage the inherent association between involved variables to transform it into a continuous-integer convex-concave problem. Then, a quadratic transform (QT)-assisted iterative algorithm is proposed to achieve SE maximization. Next, we extend our study to the case of one radio frame consisting of several CT intervals. In this regard, a novel two-phase CT interval (TPCT) scheme is designed to not only improve the SE in radio frame but also provide consistent data transmissions over fast time-varying channels. Correspondingly, to facilitate the optimization, we propose a two-step iterative algorithm by building the connections between two phases in TPCT through an iteration factor. Simulation results show that, MDD-CF can significantly outperform in-band full duplex (IBFD)-CF due to the efficient interference management. Furthermore, compared with time-division duplex (TDD)-CF, MDD-CF is more robust to high-mobility scenarios and achieves better SE performance.

Fluid Antenna With Linear MMSE Channel Estimation for Large-Scale Cellular Networks

The concept of reconfigurable fluid antennas (FA) is a potential and promising solution to enhance the spectral efficiency of wireless communication networks. Despite their many advantages, FA-enabled communications have limitations as they require an enormous amount of spectral resources in order to select the most desirable position of the radiating element from a large number of prescribed locations. In this paper, we present an analytical framework for the outage performance of large-scale FA-enabled communications, where all user equipments (UEs) employ circular multi-FA array. In contrast to existing studies, which assume perfect channel state information, the developed framework accurately captures the channel estimation errors on the performance of the considered network deployments. In particular, we focus on the limited coherence interval scenario, where a novel sequential linear minimum mean-squared error (LMMSE)-based channel estimation method is performed for only a very small number of FA ports. Next, for the communication of each BS with its associated UE, a low-complexity port-selection technique is employed, where the port that provides the highest signal-to-interference-plus-noise-ratio is selected among the ports that are estimated to provide the strongest channel from each FA. By using stochastic geometry tools, we derive both analytical and closed-form expressions for the outage probability, highlighting the impact of channel estimation on the performance of FA-based UEs. Our results reveal the trade-off imposed between improving the network’s performance and reducing the channel estimation quality, indicating new insights for the design of FA-enabled communications.

A novel blind spatial-based random access to pilots (BSB-RAP) in overloaded massive MIMO systems

In this study, to increase the success rate of active user admission in overloaded massive multi-input multi-output (MIMO) systems, a new spatially based random access to pilots (RAP) is proposed to assign orthogonal pilots to the users requesting network access. Therefore, by increasing the acceptance rate of users in a cell, this approach reduces the training overhead and waste of resources. In the massive MIMO for crowd scenarios, the main issue is the limited number of available orthogonal pilots employed by the users in the channel estimation process. This novel approach as spatially based random access enables us to have more connected users during every coherence interval (CI) despite the mentioned limitation. Intrinsic angular domain sparsity of massive MIMO channels and the sporadic traffic of users can help us obtain the spatial features of active UEs in a blind continuous compressed sensing (CCS) approach. Proposed approach is to use a continuous compressed sensing technique based on a prior optimization that provides users’ angle of arrival (AoA) and an innovative space-based RAP protocol to assign orthogonal pilots to active users in coherent transmission. Unlike the previous works, this strategy does not need to limit the number of users to the number of available orthogonal pilots due to the employed spatial degrees of freedom.

Mitigating Smart Jammers in Multi-User MIMO

Wireless systems must be resilient to jamming attacks. Existing mitigation methods based on multi-antenna processing require knowledge of the jammer's transmit characteristics that may be difficult to acquire, especially for smart jammers that evade mitigation by transmitting only at specific instants. We propose a novel method to mitigate smart jamming attacks on the massive multi-user multiple-input multiple-output (MU-MIMO) uplink which does not require the jammer to be active at any specific instant. By formulating an optimization problem that unifies jammer estimation and mitigation, channel estimation, and data detection, we exploit that a jammer cannot change its subspace within a coherence interval. Theoretical results for our problem formulation show that its solution is guaranteed to recover the users' data symbols under certain conditions. We develop two efficient iterative algorithms for approximately solving the proposed problem formulation: MAED, a parameter-free algorithm which uses forward-backward splitting with a box symbol prior, and SO-MAED, which replaces the prior of MAED with soft-output symbol estimates that exploit the discrete transmit constellation and which uses deep unfolding to optimize algorithm parameters. We use simulations to demonstrate that the proposed algorithms effectively mitigate a wide range of smart jammers without a priori knowledge about the attack type.

Impact of Channel Aging on Reconfigurable Intelligent Surface Aided Massive MIMO Systems With Statistical CSI

The incorporation of reconfigurable intelligent surface (RIS) into massive multiple-input-multiple-output (mMIMO) systems can unleash the potential of next-generation networks by improving the performance of user equipments (UEs) in service dead zones. However, their requirement for accurate channel state information (CSI) is critical, and especially, applications with UE mobility that induce channel aging make challenging the achievement of adequate quality of service. Hence, in this work, we investigate the impact of channel aging on the performance of RIS-assisted mMIMO systems under both spatial correlation and imperfect CSI conditions. Specifically, by accounting for channel aging during both uplink training and downlink data transmission phases, we first perform minimum mean square error (MMSE) channel estimation to obtain the UE effective channels with low overhead similar to conventional systems without RIS. Next, we derive the downlink achievable sum spectral efficiency (SE) with regularized zero-forcing (RZF) precoding in closed-form being dependent only on large-scale statistics by using the deterministic equivalent (DE) analysis. Subsequently, we present the attractive optimization of the achievable sum SE with respect to the phase shifts and the total transmit power that can be performed every several coherence intervals due to the slow variation of the large-scale statistics. Numerical results validate the analytical expressions and demonstrate the performance while allowing the extraction of insightful design conclusions for common scenarios including UE mobility. In particular, channel aging degrades the performance but its impact can be controlled by choosing appropriately the frame duration or by increasing the number of RIS elements.

Decoupling Between the Energy and Semiconductor Sectors During the Pandemic: New Evidence from Wavelet Analysis

ABSTRACT We study the impact of COVID-19 on the pairwise dependence between three indices, the COVID-19 Media Coverage Index, MSCI World Semiconductor Index, and the MSCI World Energy Index, as well as investigate the respective volatility spillovers. We find intervals of weak, moderate, and strong coherence between the Media Coverage Index and returns and volatility of semiconductor and energy sector companies. Low coherence intervals indicate a diversification potential of investments in these sectors and in their volatility-based products during periods of systemic crises such as the financial turmoil induced by COVID-19. Our results provide evidence that after the escalation of the pandemic in early 2020, the energy sector cedes its leading role in terms of volatility to the semiconductor industry. We report on appealing hedging attributes related to the decoupling between the trends in the global semiconductor industry and the global energy sector accelerated by the COVID-19 triggered crisis.

Transmit Beampattern Design for Distributed Satellite Constellation Based on Space–Time–Frequency DoFs

For distributed satellite constellations, detection performance can be equivalently regarded as a single large satellite by the cooperative operation of multiple small satellites, which is a promising research topic of the Next-Generation Radar (NGR) system. However, dense grating lobes inevitably occur in the synthetic transmit pattern due to its distributed configuration, as a result of which the detection performance of dynamic coherent radar is seriously weakened. In this paper, a novel transmit beampattern optimization method for dynamic coherent radar based on a distributed satellite constellation is presented. Firstly, the effective coherent detection range interval is determined by several influence factors, i.e., coherent detection, far-field, and system link constraints. Then, we discuss the quantitative evaluation method for coherent integration in terms of synchronization error, beam pointing error, and high-speed motion characteristics and we allocate the corresponding terms in a reasonable way from the perspective of engineering. Finally, the space–time–frequency degrees of freedom (DOFs), which can be collected from satellite spacing, carrier frequencies, and platform motion characteristics, are utilized to realize a robust transmit beampattern with low sidelobe by invoking a genetic algorithm (GA). Simulation results validate the effectiveness of our theoretic analysis, and unambiguous coherent transmit beamforming with a satellite constellation of limited scale is accomplished.

Performance of CSI-Based Power Control and NOMA/OMA Switching for Uplink Underlay Networks With Imperfect SIC

In a multiuser underlay uplink network, we show that despite the fact that the transmit powers are constrained by the interference temperature limit (ITL) imposed by the primary network, allowing upto two users to concurrently transmit using non-orthogonal multiple access (NOMA) principles ensures better performance than when only a single user is picked in an orthogonal multiple access (OMA) scheme. To deal with the large variation in the link SNRs in underlay networks, an optimal channel state information (CSI) based ITL apportioning scheme is suggested for the first time in literature that carefully controls the transmit powers of the selected users to maximize throughput. CSI-based throughput-optimal strategies are evolved for user selection and NOMA/OMA switching. We analyze the throughput performance of the uplink with NOMA/OMA switching, optimal user selection, and CSI based ITL apportioning, taking successive interference cancellation (SIC) errors into account. Useful bounds on performance are also derived. It is shown that when the ITL is broadcast in every coherence interval, and the ITL can also be channel dependent, large thoughput gains accrue. Computer simulations confirm accuracy of the derived expressions.