IEEE journal on selected areas in information theory最新文献

{"title":"On Gaussian Codes and Mismatched Decoding for Fading MIMO Multiple-Access Channels","authors":"Mehrangiz Ensan;Hamdi Joudeh;Frans M. J. Willems","doi":"10.1109/JSAIT.2026.3675858","DOIUrl":"https://doi.org/10.1109/JSAIT.2026.3675858","url":null,"abstract":"We consider a multiple-access channel with single-antenna users and a multi-antenna access point under imperfect channel state information. We derive an achievable rate region under Gaussian codebooks and a mismatched decoder that treats the signal-dependent noise from channel imperfection as additional Gaussian noise. We further show that the rate tuples in this region are also achieved using a lower-complexity scheme based on linear combining and mismatched single-user decoding with successive interference cancellation. We then specialize the results to a block-fading setting with pilot-assisted channel estimation and derive analytical bounds on the sum-rate gap between matched and mismatched decoding. We show that this gap is typically small in practically relevant scenarios.","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"7 ","pages":"106-119"},"PeriodicalIF":2.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147737056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Support Recovery in One-Bit Compressed Sensing With Near-Optimal Measurements and Sublinear Time","authors":"Xiaxin Li;Arya Mazumdar","doi":"10.1109/JSAIT.2026.3728742","DOIUrl":"https://doi.org/10.1109/JSAIT.2026.3728742","url":null,"abstract":"One-bit compressed sensing (1bCS) addresses the recovery of sparse signals from highly quantized measurements, retaining only the sign of each linear measurement. From a data compression perspective, the one-bit measurements form a compact binary representation of sparse signals. The support recovery problem seeks to recover the support of an unknown signal <inline-formula> <tex-math>$xin mathbb {R}^{n}$ </tex-math></inline-formula>, <inline-formula> <tex-math>$mathrm {supp}(x)$ </tex-math></inline-formula>, from <inline-formula> <tex-math>$y=mathrm {sign}(Ax)$ </tex-math></inline-formula>, where <inline-formula> <tex-math>$Ain mathbb {R}^{mtimes n}$ </tex-math></inline-formula> is the measurement matrix and <inline-formula> <tex-math>$|mathrm {supp}(x)|le kll n$ </tex-math></inline-formula>. Existing methods seek to minimize the number of measurements but often incur <inline-formula> <tex-math>$Omega (n)$ </tex-math></inline-formula> decoding complexity, limiting their applicability to large-scale problems. We propose new 1bCS schemes that achieve sublinear decoding complexity while maintaining near-optimal measurement bounds. For universal support recovery, our framework provides: (i) exact recovery with <inline-formula> <tex-math>$m=O(k^{2}log (n/k)log n)$ </tex-math></inline-formula> measurements and decoding complexity <inline-formula> <tex-math>$D=O(km)$ </tex-math></inline-formula>, and (ii) <inline-formula> <tex-math>$epsilon $ </tex-math></inline-formula>-approximate recovery with <inline-formula> <tex-math>$m=O(kepsilon ^{-1}log (n/k)log n)$ </tex-math></inline-formula> and <inline-formula> <tex-math>$D=O(epsilon ^{-1}m)$ </tex-math></inline-formula>. For probabilistic exact recovery, we design a scheme with <inline-formula> <tex-math>$m=O(klog klog n)$ </tex-math></inline-formula> and <inline-formula> <tex-math>$D=O(m)$ </tex-math></inline-formula>, achieving vanishing error probability. Our schemes leverage ideas from group testing to achieve near-optimal support compression with substantially reduced decoding complexity.","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"7 ","pages":"446-457"},"PeriodicalIF":2.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148986012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Perfectly Private Lossless Variable-Length Coding With a Sequential Encoder","authors":"Amirreza Zamani;Mikael Skoglund","doi":"10.1109/JSAIT.2026.3737657","DOIUrl":"https://doi.org/10.1109/JSAIT.2026.3737657","url":null,"abstract":"A lossless data compression problem with a privacy constraint is studied where a server has access to a database of &lt;inline-formula&gt; &lt;tex-math&gt;$N$ &lt;/tex-math&gt;&lt;/inline-formula&gt; files, &lt;inline-formula&gt; &lt;tex-math&gt;$(Y_{1},ldots,Y_{N})$ &lt;/tex-math&gt;&lt;/inline-formula&gt;, each of size &lt;inline-formula&gt; &lt;tex-math&gt;$F$ &lt;/tex-math&gt;&lt;/inline-formula&gt; bits and is connected to an encoder. The encoder is connected through an unsecured public link to a user. We assume that each file &lt;inline-formula&gt; &lt;tex-math&gt;$Y_{i}$ &lt;/tex-math&gt;&lt;/inline-formula&gt; is arbitrarily correlated with a private attribute &lt;inline-formula&gt; &lt;tex-math&gt;$X$ &lt;/tex-math&gt;&lt;/inline-formula&gt;, which is assumed to be accessible by the encoder. Moreover, an adversary is assumed to have access to the public link. The user and the encoder have access to a shared secret key &lt;inline-formula&gt; &lt;tex-math&gt;$W$ &lt;/tex-math&gt;&lt;/inline-formula&gt;. We assume that at each time the user asks for a file &lt;inline-formula&gt; &lt;tex-math&gt;$Y_{d_{i}}$ &lt;/tex-math&gt;&lt;/inline-formula&gt;, where &lt;inline-formula&gt; &lt;tex-math&gt;$(d_{1},ldots,d_{K})$ &lt;/tex-math&gt;&lt;/inline-formula&gt; corresponds to the demand vector. The goal is to design the delivered message &lt;inline-formula&gt; &lt;tex-math&gt;$mathcal {C}=(mathcal {C}_{1},ldots,mathcal {C}_{K})$ &lt;/tex-math&gt;&lt;/inline-formula&gt; after the user sends the demands to the encoder such that the average length of &lt;inline-formula&gt; &lt;tex-math&gt;$mathcal {C}$ &lt;/tex-math&gt;&lt;/inline-formula&gt; is minimized, while satisfying: i. The message &lt;inline-formula&gt; &lt;tex-math&gt;$cal C$ &lt;/tex-math&gt;&lt;/inline-formula&gt; does not reveal any information about &lt;inline-formula&gt; &lt;tex-math&gt;$X$ &lt;/tex-math&gt;&lt;/inline-formula&gt; yielding &lt;inline-formula&gt; &lt;tex-math&gt;$I(X;mathcal {C})=0$ &lt;/tex-math&gt;&lt;/inline-formula&gt;, which corresponds to the perfect privacy constraint; ii. The user is able to decode its demands, &lt;inline-formula&gt; &lt;tex-math&gt;$Y_{d_{i}}$ &lt;/tex-math&gt;&lt;/inline-formula&gt;, without any loss by using &lt;inline-formula&gt; &lt;tex-math&gt;$cal C$ &lt;/tex-math&gt;&lt;/inline-formula&gt; and the shared key &lt;inline-formula&gt; &lt;tex-math&gt;$W$ &lt;/tex-math&gt;&lt;/inline-formula&gt;, i.e., &lt;inline-formula&gt; &lt;tex-math&gt;$H(Y_{d_{i}}|mathcal {C},W)=0$ &lt;/tex-math&gt;&lt;/inline-formula&gt;. Here, the encoder sequentially encodes each demand &lt;inline-formula&gt; &lt;tex-math&gt;$Y_{d_{i}}$ &lt;/tex-math&gt;&lt;/inline-formula&gt; at time &lt;inline-formula&gt; &lt;tex-math&gt;$i$ &lt;/tex-math&gt;&lt;/inline-formula&gt;, using the shared key, the database, the private attribute, and the previous demands. We propose a recursive coding framework with three constructions for generating the privacy-preserving auxiliary variables. In all designs, we use a multi-part code construction in which &lt;inline-formula&gt; &lt;tex-math&gt;$X$ &lt;/tex-math&gt;&lt;/inline-formula&gt; is concealed using a one-time pad scheme. In the first design, we employ an extension of the Functional Representation Lemma (FRL). We then improve this scheme by using minimum-entropy functional representation. In a special case, we further enhance both schemes using the concept of Wyner’s common information. We study the tig","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"7 ","pages":"473-488"},"PeriodicalIF":2.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149133408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Structured Polynomial Codes for Distributed Matrix Multiplication","authors":"Derya Malak","doi":"10.1109/JSAIT.2026.3688105","DOIUrl":"https://doi.org/10.1109/JSAIT.2026.3688105","url":null,"abstract":"We consider the practical source-worker-receiver framework for distributed matrix multiplication over finite fields. In a setting with two distributed sources with separate encoders, with the first source having access to A and the second to B, with <inline-formula> <tex-math>$I$ </tex-math></inline-formula> distributed workers, where each has a bounded storage (memory) capability reflecting a bounded computational capability, and thus can multiply matrices of smaller size, and one receiver, which aims to reconstruct <inline-formula> <tex-math>${mathbf { A}}^{intercal }{mathbf { B}}$ </tex-math></inline-formula> from the transmissions of workers. By combining a structured linear encoding scheme with the polynomial code framework, we introduce a novel class of structured polynomial codes (StPolyDot codes) for distributed matrix multiplication, leveraging the bilinear structure of this problem, and show that our codes can surpass the performance of the existing state of art in terms of both memory and communication costs, while also maintaining certain advantages in terms of security.","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"7 ","pages":"175-191"},"PeriodicalIF":2.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147937619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Array Configuration and Scaling Laws in Near-Field Uplink Multiuser MIMO","authors":"Jiyoung Yun;Wan Choi;Jeffrey G. Andrews","doi":"10.1109/JSAIT.2026.3674909","DOIUrl":"https://doi.org/10.1109/JSAIT.2026.3674909","url":null,"abstract":"Future wireless networks must deliver high spectral efficiency in dense environments where many users require simultaneous high-rate communication. Higher carrier frequencies offer more bandwidth but suffer from shorter range and lower channel rank, making new capacity enhancing strategies essential. This paper studies joint antenna array design at the base station (BS) and user devices in line-of-sight (LoS) uplink multiuser multiple-input multiple-output (MU-MIMO) systems with the goal of exploiting radiative near-field effects to unlock full multiplexing gains. In particular, we establish a scaling law under which there exist uniform sparse array configurations whose sum rate gap to an interference-free upper bound decays with the number of BS antennas. For dense user deployments beyond this scaling regime, we propose a joint array design algorithm with provable performance guarantees, in which each user array is configured to maximize its single-user achievable rate conditioned on the BS array, followed by non-uniform BS array optimization using a submodular surrogate. Our simulation results verify that uniform sparse arrays achieve the interference-free regime predicted by the scaling law. Outside this regime, the proposed joint design closely tracks the interference-free upper bound, demonstrating the benefit of non-uniform BS array optimization.","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"7 ","pages":"62-74"},"PeriodicalIF":2.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147557873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Synonymous Variational Perspective on the Rate-Distortion-Perception Tradeoff","authors":"Zijian Liang;Kai Niu;Changshuo Wang;Jin Xu;Ping Zhang","doi":"10.1109/JSAIT.2026.3730187","DOIUrl":"https://doi.org/10.1109/JSAIT.2026.3730187","url":null,"abstract":"The fundamental limit of natural signal compression is characterized by classical rate-distortion (RD) theory through the tradeoff between coding rate and reconstruction distortion, while the rate-distortion-perception (RDP) framework extends this tradeoff by adding a divergence-based perception criterion. This motivates investigating whether a generalized reconstruction formulation can provide a theoretical basis for aligning source and reconstruction distributions in practical perceptual codec optimization. Motivated by a synonymity-based semantic information perspective, we reformulate perceptual reconstruction as recovering any admissible sample within an ideal synonymous set (synset) associated with the source, rather than the source sample itself, and establish a synonymous source coding architecture. We then develop a synonymous variational inference (SVI) framework with a synonymous variational lower bound (SVLBO) to analyze synset-oriented compression. Within this framework, we establish a synonymity-perception consistency principle, showing that optimal identification of semantic information is theoretically consistent with perceptual optimization. We further derive a tight-bound synonymous source coding rate characterization and show that its Jensen-limit relaxation leads to a synonymous rate-distortion-perception form for practical optimization. These results provide a principled synonymity-based theoretical rationale for promoting distributional alignment, clarify compatibility with existing RDP formulations and classical RD theory, and suggest the potential practical advantages of synonymous source coding methods.","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"7 ","pages":"431-445"},"PeriodicalIF":2.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148986092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Blank Space: Adaptive Causal Coding for Streaming Communications Over Multi-Hop Networks","authors":"Rivka Gitik;Adina Waxman;Shai Ginzach;Aviel Glam;Alejandro Cohen","doi":"10.1109/JSAIT.2026.3698351","DOIUrl":"https://doi.org/10.1109/JSAIT.2026.3698351","url":null,"abstract":"In this work, we introduce Blank Space Adaptive Causal Random Linear Network Coding (BS-AC-RLNC), a novel coding scheme designed to mitigate the triplet trade-off between throughput-delay-efficiency in multi-hop networks. BS-AC-RLNC leverages the physical limitations of the network, considering the bottleneck from each node to the destination. In particular, this approach introduces a light-computational re-encoding algorithm, called Network AC-RLNC (NET), implemented independently at intermediate nodes. NET adaptively adjusts the Forward Error Correction (FEC) rates and schedules idle periods. It incorporates two distinct suspension mechanisms: 1) Blank Space Period, accounting for the forward-channels bottleneck, and 2) No-New No-FEC approach, based on data availability. We present theoretical lower and upper bounds on in-order delivery delay, goodput, and throughput; in the case of in-order delay, we further derive a mean bound. These analytical results are extended to the multicast scenario, providing a broader understanding of the algorithm’s performance under diverse network conditions. The experimental results achieve significant improvements in resource efficiency, demonstrating a 20% reduction in channel usage compared to baseline RLNC solutions. Notably, these efficiency gains are achieved while maintaining competitive throughput and delay performance, ensuring improved resource utilization does not compromise network performance.","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"7 ","pages":"263-278"},"PeriodicalIF":2.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148236329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Uncertainty Quantification and Data Efficiency in AI: An Information-Theoretic Perspective","authors":"Osvaldo Simeone;Yaniv Romano","doi":"10.1109/JSAIT.2026.3705302","DOIUrl":"https://doi.org/10.1109/JSAIT.2026.3705302","url":null,"abstract":"In context-specific applications such as robotics, telecommunications, and healthcare, artificial intelligence systems often face the challenge of limited training data. This scarcity introduces epistemic uncertainty, i.e., reducible uncertainty stemming from incomplete knowledge of the underlying data distribution, which fundamentally limits predictive performance. This review paper examines formal methodologies that address data-limited regimes through two complementary approaches: quantifying epistemic uncertainty and mitigating data scarcity via synthetic data augmentation. We begin by reviewing generalized Bayesian learning frameworks that characterize epistemic uncertainty through generalized posteriors in the model parameter space, as well as “post-Bayes” learning frameworks. We continue by presenting information-theoretic generalization bounds that formalize the relationship between training data quantity and predictive uncertainty, providing a theoretical justification for generalized Bayesian learning. Moving beyond methods with asymptotic statistical validity, we survey uncertainty quantification methods that provide finite-sample statistical guarantees, including conformal prediction and conformal risk control. Finally, we examine recent advances in data efficiency by combining limited labeled data with abundant model predictions or synthetic data. Throughout, we take an information-theoretic perspective, highlighting the role of information measures in quantifying the impact of data scarcity.","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"7 ","pages":"290-301"},"PeriodicalIF":2.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148443128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Hardware-Aware Analysis of Energy Efficiency and Quantization Trade-Offs in Spiking Neural Networks","authors":"Andrea Castagnetti;Alain Pegatoquet;Benoît Miramond","doi":"10.1109/JSAIT.2026.3721047","DOIUrl":"https://doi.org/10.1109/JSAIT.2026.3721047","url":null,"abstract":"Spiking Neural Networks (SNNs) are one of the most promising bio-inspired neural networks models and have drawn increasing attention in recent years. The event-driven communication mechanism of SNNs allows for sparse and theoretically low-power operations on dedicated neuromorphic hardware. However, the binary nature of instantaneous spikes also leads to considerable information loss in SNNs, resulting in performance degradation. To address this issue, multi-level spiking neuron models have been proposed to provide both low-quantization error and minimal inference latency while approaching the performance of full precision Artificial Neural Networks (ANNs). In this paper, we provide an in depth analysis of the different trade-offs between quantization, generalization performance, and energy efficiency between binary SNNs, multi-level SNNs and ANNs for two different applications scenarios: image classification and image denoising. Our goal is to determine analytical thresholds on SNNs activity to understand when SNNs can be more energy efficient than ANNs but also to explain why SNNs cannot provide energy gains in other scenarios. Our analysis is based on an hardware execution model, which allows us to take into account the energy costs related to synaptic operations as well as memory accesses. Our experimental results show that multi-level spiking neurons provide better information compression, allowing therefore a reduction in latency without performance loss for classification tasks. When compared to binary SNNs on image classification scenarios, multi-level SNNs indeed allow reducing by 2 to 3 times the energy consumption depending on the number of quantization intervals. On neuromorphic data, multi-level SNNs allows us to drastically reduce the inference latency to 1 timestep, which corresponds to a compression factor of 10 compared to previously published results on binary SNNs. However, for the image denoising task we observe that neither the binary nor the multi-level SNNs can match the ANNs energy efficiency and performance. Based on a hardware-aware execution model of a neuromorphic SNN accelerator, we then provide analytical upper-bounds on SNNs activity and energy-efficiency. These theoretical bounds are then compared against the experimental results and allows us to understand, from a system level point of view, the relation between energy efficiency, quantization schemes and finally the performance of the SNNs.","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"7 ","pages":"411-430"},"PeriodicalIF":2.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11643735","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148836849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Uplink-Downlink Duality for Beamforming in Integrated Sensing and Communications","authors":"Kareem M. Attiah;Wei Yu","doi":"10.1109/JSAIT.2026.3693910","DOIUrl":"https://doi.org/10.1109/JSAIT.2026.3693910","url":null,"abstract":"This paper considers the beamforming and power optimization problem for a class of integrated sensing and communications (ISAC) problems that utilize the communication signals simultaneously for sensing. We formulate the problem of minimizing the Bayesian Cramér-Rao bound (BCRB) on the mean-squared error of estimating a vector of parameters, while satisfying downlink signal-to-interference-and-noise-ratio constraints for a set of communication users at the same time. The proposed optimization framework comprises two key new ingredients. First, we show that the BCRB minimization problem corresponds to maximizing beamforming power along certain sensing directions of interest. Second, the classical uplink-downlink duality for multiple-input multiple-output communications can be extended to the ISAC setting, but unlike the classical communication problem, the dual uplink problem for ISAC may entail negative noise power and needs to include an extra condition on the uplink beamformers. This new duality theory opens doors for efficient iterative algorithm for optimizing power and beamformers for ISAC.","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"7 ","pages":"354-372"},"PeriodicalIF":2.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148626688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}