Secure communications over SIMO and MISO channels between wireless nodes are commonly encountered in applications and widely considered in the literature. This paper investigates such a problem by considering a newly proposed scheme called secrecy-message transmission by echoing encrypted probes (STEEP). We focus on a type of heavily eavesdropped channels where an aggressive eavesdropper (Eve) has multiple antennas and is capable of jamming and eavesdropping in the full-duplex mode. Assuming optimal jamming and eavesdropping by Eve, we present the secrecy rates achievable by STEEP in two forms: using SIMO channel in phase 1 and using MISO channel in phase 2, and vice versa. With any given channel condition between every pair of nodes and any finite jamming power by Eve, the secrecy rate of STEEP in either form is shown to be positive subject to a positive power in phase 1 and a sufficiently large power in phase 2. The connections between STEEP in either form and the conventional SIMO and MISO schemes are provided, and their secrecy rates are compared comprehensively subject to random fading channels and finite transmit powers.
The seasonal prediction of the Indian summer monsoon (ISM) and Monsoon Intraseasonal Oscillations (MISO), as well as the Madden Julian Oscillations (MJO) that strongly modulate MISO, is important to the country for water and crop management. We have analyzed the precipitation, convection, and total cloud fraction (TCF) in the sixth Coupled Model Intercomparison Projects (CMIP6). This study highlights the significant differences in simulating MISO and MJO between the two groups of selected CMIP6 models and physical reasons behind them. The mean and intraseasonal features of MISO and MJO varied significantly in CMIP6 models, which are linked with a better depiction of convection and total cloud fraction. The probability distributions of rainfall and OLR in CMIP6 models indicate significant variations in simulating ISM precipitating clouds. TaiESM1 and IITM-ESM demonstrate improvements in capturing the MISO features. TaiESM1 depicts better eastward propagation of MJO during both summer and winter. The biases in OLR and TCF are also less in the IITM-ESM and TaiESM1 models than in CanESM5 and FGOALS-g3. The results demonstrate the importance of cloud and convection in CMIP6 models to de
Single sign-on (SSO) allows users to authenticate to third-party applications through a central identity provider. Despite their wide adoption, deployed SSO systems suffer from privacy problems such as user tracking by the identity provider. While numerous solutions have been proposed by academic papers, none were adopted because they require modifying identity providers, a significant adoption barrier in practice. Solutions do get deployed, however, fail to eliminate major privacy issues. Leveraging Trusted Execution Environments (TEEs), we propose MISO, the first privacy-preserving SSO system that is completely compatible with existing identity providers (such as Google and Facebook). This means MISO can be easily integrated into existing SSO ecosystem today and benefit end users. MISO also enables new functionality that standard SSO cannot offer: MISO allows users to leverage multiple identity providers in a single SSO workflow, potentially in a threshold fashion, to better protect user accounts. We fully implemented MISO based on Intel SGX. Our evaluation shows that MISO can handle high user concurrency with practical performance.
Neural implicit representations have had a significant impact on simultaneous localization and mapping (SLAM) by enabling robots to build continuous, differentiable, and high-fidelity 3D maps from sensor data. However, as the scale and complexity of the environment increase, neural SLAM approaches face renewed challenges in the back-end optimization process to keep up with runtime requirements and maintain global consistency. We introduce MISO, a hierarchical optimization approach that leverages multiresolution submaps to achieve efficient and scalable neural implicit reconstruction. For local SLAM within each submap, we develop a hierarchical optimization scheme with learned initialization that substantially reduces the time needed to optimize the implicit submap features. To correct estimation drift globally, we develop a hierarchical method to align and fuse the multiresolution submaps, leading to substantial acceleration by avoiding the need to decode the full scene geometry. MISO significantly improves computational efficiency and estimation accuracy of neural signed distance function (SDF) SLAM on large-scale real-world benchmarks.
This paper studies the receiver to transmitter antenna coupling in near-field communications with massive arrays. Although most works in the literature consider that it is negligible and approximate it by zero, there is no rigorous analysis on its relevance for practical systems. In this work, we leverage multiport communication theory to obtain conditions for the aforementioned approximation to be valid in MISO and SIMO systems. These conditions are then particularized for arrays with fixed inter-element spacing and arrays with fixed size.
In this paper, we propose a dynamic hybrid active-passive reconfigurable intelligent surface (HRIS) to enhance multiple-input-single-output (MISO) communications, leveraging the property of dynamically placing active elements. Specifically, considering the impact of hardware impairments (HWIs), we investigate channel-aware configurations of the receive antennas at the base station (BS) and the active/passive elements at the HRIS to improve transmission reliability. To this end, we address the average mean-square-error (MSE) minimization problem for the HRIS-aided MISO system by jointly optimizing the BS receive antenna selection matrix, the reflection phase coefficients, the reflection amplitude matrix, and the mode selection matrix of the HRIS. To overcome the non-convexity and intractability of this problem, we first transform the binary and discrete variables into continuous ones, and then propose a penalty-based exact block coordinate descent (PEBCD) algorithm to alternately solve these subproblems. Numerical simulations demonstrate the significant superiority of our proposed scheme over conventional benchmark schemes.
Multiuser, Multiple Input, Single Output (MU-MISO) systems are proving to be indispensable in the next generation wireless networks such as 5G and 6G. The spatial diversity of MISO systems have been leveraged in physical layer designs in these wireless systems to improve the capacity. Several recent studies have utilised redundancies in the content request along with the spatial diversity of a MISO system to improve the capacity further. It is shown that Max-Min Fair (MMF) Beamforming schemes for MISO based on SDMA, NOMA, OMA and Rate-Splitting could be used to improve the content delivery rates. However, in most of these studies the key aspects such as the queueing delays in the downlink and the user dynamics have generally been ignored. In this work, we study how the interplay between queueing, beamforming and the user dynamics affects the Quality-of-Service (user experienced delay) of downlink in MU-MISO content centric networks (CCNs). We propose queueing theoretic models that are simple in nature and can be directly adapted to MU-MISO CCNs to perform optimal multi-group multicast downlink transmissions. We show that a recently developed Simple Multicast Queue (SMQ) for SISO sy
This correspondence studies the downlink transmission in a multi-cell system, where multiple base stations (BSs) each with multiple antennas cooperatively design their respective transmit beamforming vectors to optimize the overall system performance. For simplicity, it is assumed that all mobile stations (MSs) are equipped with a single antenna each, and there is one active MS in each cell at one time. Accordingly, the system of interests can be modeled by a multiple-input single-output (MISO) interference channel (IC), termed as MISO-IC, with interference treated as noise. We propose a new method to characterize different rate-tuples for active MSs on the Pareto boundary of the achievable rate region for the MISO-IC, by exploring the relationship between the MISO-IC and the cognitive radio (CR) MISO channel. We show that each Pareto-boundary rate-tuple of the MISO-IC can be achieved in a decentralized manner when each of the BSs attains its own channel capacity subject to a certain set of interference-power constraints (also known as interference-temperature constraints in the CR system) at the other MS receivers. Furthermore, we show that this result leads to a new decentralized
GPU technology has been improving at an expedited pace in terms of size and performance, empowering HPC and AI/ML researchers to advance the scientific discovery process. However, this also leads to inefficient resource usage, as most GPU workloads, including complicated AI/ML models, are not able to utilize the GPU resources to their fullest extent -- encouraging support for GPU multi-tenancy. We propose MISO, a technique to exploit the Multi-Instance GPU (MIG) capability on the latest NVIDIA datacenter GPUs (e.g., A100, H100) to dynamically partition GPU resources among co-located jobs. MISO's key insight is to use the lightweight, more flexible Multi-Process Service (MPS) capability to predict the best MIG partition allocation for different jobs, without incurring the overhead of implementing them during exploration. Due to its ability to utilize GPU resources more efficiently, MISO achieves 49% and 16% lower average job completion time than the unpartitioned and optimal static GPU partition schemes, respectively.
This paper considers the channel estimation of a single user in a MISO system with an intelligent reflecting surface (IRS). The performances of the minimum variance unbiased (MVU) and minimum mean square error (MMSE) estimators using the discrete Fourier transform activation pattern for the IRS, updated at every symbol interval, are compared. Numerical results show that the MMSE estimator provides over 10 dB SNR improvement compared to the MVU estimator.
In this paper, we propose a simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and energy buffer aided multiple-input single-output (MISO) simultaneous wireless information and power transfer (SWIPT) non-orthogonal multiple access (NOMA) system, which consists of a STAR-RIS, an access point (AP), and reflection users and transmission users with energy buffers. In the proposed system, the multi-antenna AP can transmit information and energy to several single-antenna reflection and transmission users simultaneously by the NOMA fashion in the downlink, where the power transfer and information transmission states of the users are modeled using Markov chains. The reflection and transmission users harvest and store the energy in energy buffers as additional power supplies, which are partially utilized for uplink information transmission. The power outage probability, information outage probability, sum throughput, and joint outage probability closed-form expressions of the proposed system are derived over Nakagami-m fading channels, which are validated via simulations. Results demonstrate that the proposed system achieves better performance as compared
We present an analytical, simulation, and experimental-based study of beamforming Multiple Input Single Output (MISO) systems. We analyze the performance of beamforming MISO systems taking into account implementation complexity and effects of imperfect channel estimate, delayed feedback, real Radio Frequency (RF) hardware, and imperfect timing synchronization. Our results show that efficient implementation of codebook-based beamforming MISO systems with good performance is feasible in the presence of channel and implementation-induced imperfections. As part of our study we develop a framework for Average Error Vector Magnitude Squared (AEVMS)-based analysis of beamforming MISO systems which facilitates comparison of analytical, simulation, and experimental results on the same scale. In addition, AEVMS allows fair comparison of experimental results obtained from different wireless testbeds. We derive novel expressions for the AEVMS of beamforming MISO systems and show how the AEVMS relates to important system characteristics like the diversity gain, coding gain, and error floor.
This paper considers a K-user Multiple-Input-Single-Output (MISO) Interference Channel (IC), where the channel state information obtained by the transmitters (CSIT) is perfect, but completely outdated. A Retrospective Interference Alignment (RIA) using such delayed CSIT was proposed by the authors of [1] for the MISO Broadcast Channel (BC), but the extension to the MISO IC is a non-trivial step as each transmitter only has the message intended for the corresponding user. Recently, [7] focused on a Single-Input-Single-Output (SISO) IC and solved such bottleneck by inventing a distributed higher order symbol generation. Our main work is to extend [7] to the MISO case by integrating some features of the scheme proposed in [1]. The achieved sum Degrees-of-Freedom (DoF) performance is asymptotically given by 64/15 when $K\to\infty$, outperforming all the previously known results.
We consider a $K$-user multiple-input single-output (MISO) broadcast channel (BC) where the channel state information (CSI) of user $i(i=1,2,\ldots,K)$ may be either perfect (P), delayed (D) or not known (N) at the transmitter with probabilities $λ_P^i$, $λ_D^i$ and $λ_N^i$, respectively. In this channel, according to the three possible CSIT for each user, joint CSIT of the $K$ users could have at most $3^K$ realizations. Although the results by Tandon et al. show that the Degrees of Freedom (DoF) region for the two user MISO BC with symmetric marginal probabilities (i.e., $λ_Q^i=λ_Q \forall i\in \{1,2,\ldots,K\}, Q\in \{P,D,N\}$) depends only on the marginal probabilities, we show that this interesting result does not hold in general when the number of users is more than two. In other words, the DoF region is a function of the \textit{CSIT pattern}, or equivalently, all the joint probabilities. In this paper, given the marginal probabilities of CSIT, we derive an outer bound for the DoF region of the $K$-user MISO BC. Subsequently, the achievability of these outer bounds are considered in certain scenarios. Finally, we show the dependence of the DoF region on the joint probabiliti
We consider a $K$-user multiple-input single-output (MISO) broadcast channel (BC) where the channel state information (CSI) of user $i(i=1,2,\ldots,K)$ may be either instantaneously perfect (P), delayed (D) or not known (N) at the transmitter with probabilities $λ_P^i$, $λ_D^i$ and $λ_N^i$, respectively. In this setting, according to the three possible CSIT for each user, knowledge of the joint CSIT of the $K$ users could have at most $3^K$ states. Although the results by Tandon et al. show that for the symmetric two user MISO BC (i.e., $λ_Q^i=λ_Q,\ \forall i\in \{1,2\}, Q\in \{P,D,N\}$), the Degrees of Freedom (DoF) region depends only on the marginal probabilities, we show that this interesting result does not hold in general when $K\geq3$. In other words, the DoF region is a function of all the joint probabilities. In this paper, given the marginal probabilities of CSIT, we derive an outer bound for the DoF region of the $K$-user MISO BC. Subsequently, we investigate the achievability of the outer bound in some scenarios. Finally, we show the dependence of the DoF region on the joint probabilities.
Fuzzy inference engine, as one of the most important components of fuzzy systems, can obtain some meaningful outputs from fuzzy sets on input space and fuzzy rule base using fuzzy logic inference methods. In order to enhance the computational efficiency of fuzzy inference engine in multi-input-single-output(MISO) fuzzy systems,this paper aims mainly to investigate three MISO fuzzy hierarchial inference engines based on fuzzy implications satisfying the law of importation with aggregation functions (LIA). We firstly find some aggregation functions for well-known fuzzy implications such that they satisfy (LIA). For a given aggregation function, the fuzzy implication which satisfies (LIA) with this aggregation function is then characterized. Finally, we construct three fuzzy hierarchical inference engines in MISO fuzzy systems applying aforementioned theoretical developments.
We study the problem of secure transmission over a Gaussian two-user multi-input single-output (MISO) broadcast channel under the assumption that links connecting the transmitter to the two receivers may have unequal strength statistically. In addition to this, the state of the channel to each receiver is conveyed in a strictly causal manner to the transmitter. We focus on a two state topological setting of strong v.s. weak links. Under these assumptions, we first consider the MISO wiretap channel and establish bounds on generalized secure degrees of freedom (GSDoF). Next, we extend this model to the two-user MISO broadcast channel and establish inner and outer bounds on GSDoF region with different topology states. The encoding scheme sheds light on the usage of both resources, i.e., topology of the model and strictly causal channel state information at the transmitter (CSIT); and, allows digitization and multi-casting of overheard side information, while transmitting confidential message over the stronger link. Furthermore, for a special class of channels, we show that the established bounds agree and so we characterize the sum GSDoF.
The two user multiple-input single-output (MISO) broadcast channel with confidential messages (BCCM) is studied in which the nature of channel state information at the transmitter (CSIT) from each user can be of the form $I_{i}$, $i=1,2$ where $I_{1}, I_{2}\in \{\mathsf{P}, \mathsf{D}, \mathsf{N}\}$, and the forms $\mathsf{P}$, $\mathsf{D}$ and $\mathsf{N}$ correspond to perfect and instantaneous, completely delayed, and no CSIT, respectively. Thus, the overall CSIT can alternate between $9$ possible states corresponding to all possible values of $I_{1}I_{2}$, with each state occurring for $λ_{I_{1}I_{2}}$ fraction of the total duration. The main contribution of this paper is to establish the secure degrees of freedom (s.d.o.f.) region of the MISO BCCM with alternating CSIT with the symmetry assumption, where $λ_{I_{1} I_{2}}=λ_{I_{2}I_{1}}$. The main technical contributions include developing a) novel achievable schemes for MISO BCCM with alternating CSIT with security constraints which also highlight the synergistic benefits of inter-state coding for secrecy, b) new converse proofs via local statistical equivalence and channel enhancement; and c) showing the interplay between var
Recent advances in large language models (LLMs) have produced many specialized multimodal LLMs (MLLMs) that share common foundational LLMs, forming distinct model lineages. It remains unclear whether a fundamental behavioral link exists between the foundational LLMs and downstream variants. We investigate this question by quantifying head-level context-truthfulness scores. Across diverse LLM and MLLM lineages, including Vicuna-, Qwen2.5-, LLaMA2-, and Mistral-based models, we find that Truth Scores are strongly preserved within model families, even after instruction tuning or multimodal adaptation. We further show that this inheritance is consistent with attention-head weight preservation, and that context-truthful heads attend to query-relevant evidence. Building on this finding, we propose TruthProbe, a soft-gating strategy that amplifies context-truthful heads while preserving other head contributions. TruthProbe improves contextual truthfulness on HaluEval and reduces multimodal hallucination on POPE and CHAIR, with base-LLM Truth Scores transferring effectively to their fine-tuned LLM and MLLM descendants. Code is available at https://github.com/miso-choi/TruthProbe.
While Gaussian Splatting-based Feature Fields (GSFFs) have shown promise for visual localization, this paper highlights that photometrically optimized GSFFs are inherently ill-suited for 2D-3D matching. The volumetric extent of each Gaussian induces many-to-one pixel-to-point mappings that destabilize PnP-based pose estimation, while photometric optimization gives rise to superfluous Gaussians devoid of multi-view consistency. To address these issues, we propose SplitGS-Loc, a localization-specialized GSFFs construction framework that disambiguates 2D-3D correspondences by exploiting Gaussian attributes. Our key design, Mixture-of-Gaussians-based splitting, decomposes each Gaussian into smaller Gaussians, replacing ambiguous many-to-one with precise one-to-one correspondences. In parallel, we exploit composition weights from GS rasterization to select Gaussians that significantly and consistently contribute across multiple views and aggregate discriminative features through strong pixel-Gaussian associations, enforcing multi-view consistency. The resulting compact yet discriminative feature fields enable stable PnP convergence, achieving state-of-the-art performance on localization