Over-the-air federated learning (OTA-FL) provides bandwidth-efficient learning by leveraging the inherent superposition property of wireless channels. Personalized federated learning balances performance for users with diverse datasets, addressing real-life data heterogeneity. We propose the first personalized OTA-FL scheme through multi-task learning, assisted by personal reconfigurable intelligent surfaces (RIS) for each user. We take a cross-layer approach that optimizes communication and computation resources for global and personalized tasks in time-varying channels with imperfect channel state information, using multi-task learning for non-i.i.d data. Our PROAR-PFed algorithm adaptively designs power, local iterations, and RIS configurations. We present convergence analysis for non-convex objectives and demonstrate that PROAR-PFed outperforms state-of-the-art on the Fashion-MNIST dataset.
{"title":"Personalized Over-the-Air Federated Learning with Personalized Reconfigurable Intelligent Surfaces","authors":"Jiayu Mao, Aylin Yener","doi":"arxiv-2401.12149","DOIUrl":"https://doi.org/arxiv-2401.12149","url":null,"abstract":"Over-the-air federated learning (OTA-FL) provides bandwidth-efficient\u0000learning by leveraging the inherent superposition property of wireless\u0000channels. Personalized federated learning balances performance for users with\u0000diverse datasets, addressing real-life data heterogeneity. We propose the first\u0000personalized OTA-FL scheme through multi-task learning, assisted by personal\u0000reconfigurable intelligent surfaces (RIS) for each user. We take a cross-layer\u0000approach that optimizes communication and computation resources for global and\u0000personalized tasks in time-varying channels with imperfect channel state\u0000information, using multi-task learning for non-i.i.d data. Our PROAR-PFed\u0000algorithm adaptively designs power, local iterations, and RIS configurations.\u0000We present convergence analysis for non-convex objectives and demonstrate that\u0000PROAR-PFed outperforms state-of-the-art on the Fashion-MNIST dataset.","PeriodicalId":501433,"journal":{"name":"arXiv - CS - Information Theory","volume":"84 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139559638","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}
This paper addresses the problem of finding a minimum-cost $m$-state Markov chain $(S_0,ldots,S_{m-1})$ in a large set of chains. The chains studied have a reward associated with each state. The cost of a chain is its "gain", i.e., its average reward under its stationary distribution. Specifically, for each $k=0,ldots,m-1$ there is a known set ${mathbb S}_k$ of type-$k$ states. A permissible Markov chain contains exactly one state of each type; the problem is to find a minimum-cost permissible chain. The original motivation was to find a cheapest binary AIFV-$m$ lossless code on a source alphabet of size $n$. Such a code is an $m$-tuple of trees, in which each tree can be viewed as a Markov Chain state. This formulation was then used to address other problems in lossless compression. The known solution techniques for finding minimum-cost Markov chains were iterative and ran in exponential time. This paper shows how to map every possible type-$k$ state into a type-$k$ hyperplane and then define a "Markov Chain Polytope" as the lower envelope of all such hyperplanes. Finding a minimum-cost Markov chain can then be shown to be equivalent to finding a "highest" point on this polytope. The local optimization procedures used in the previous iterative algorithms are shown to be separation oracles for this polytope. Since these were often polynomial time, an application of the Ellipsoid method immediately leads to polynomial time algorithms for these problems.
{"title":"The Markov-Chain Polytope with Applications","authors":"Mordecai J. Golin, Albert John Lalim Patupat","doi":"arxiv-2401.11622","DOIUrl":"https://doi.org/arxiv-2401.11622","url":null,"abstract":"This paper addresses the problem of finding a minimum-cost $m$-state Markov\u0000chain $(S_0,ldots,S_{m-1})$ in a large set of chains. The chains studied have\u0000a reward associated with each state. The cost of a chain is its \"gain\", i.e.,\u0000its average reward under its stationary distribution. Specifically, for each $k=0,ldots,m-1$ there is a known set ${mathbb S}_k$\u0000of type-$k$ states. A permissible Markov chain contains exactly one state of\u0000each type; the problem is to find a minimum-cost permissible chain. The original motivation was to find a cheapest binary AIFV-$m$ lossless code\u0000on a source alphabet of size $n$. Such a code is an $m$-tuple of trees, in\u0000which each tree can be viewed as a Markov Chain state. This formulation was\u0000then used to address other problems in lossless compression. The known solution\u0000techniques for finding minimum-cost Markov chains were iterative and ran in\u0000exponential time. This paper shows how to map every possible type-$k$ state into a type-$k$\u0000hyperplane and then define a \"Markov Chain Polytope\" as the lower envelope of\u0000all such hyperplanes. Finding a minimum-cost Markov chain can then be shown to\u0000be equivalent to finding a \"highest\" point on this polytope. The local optimization procedures used in the previous iterative algorithms\u0000are shown to be separation oracles for this polytope. Since these were often\u0000polynomial time, an application of the Ellipsoid method immediately leads to\u0000polynomial time algorithms for these problems.","PeriodicalId":501433,"journal":{"name":"arXiv - CS - Information Theory","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139559353","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}
We make use of an entropic property to establish a convergence theorem (Main Theorem), which reveals that the conditional entropy measures the asymptotic Gaussianity. As an application, we establish the {it entropic conditional central limit theorem} (CCLT), which is stronger than the classical CCLT. As another application, we show that continuous input under iterated Hadamard transform, almost every distribution of the output conditional on the values of the previous signals will tend to Gaussian, and the conditional distribution is in fact insensitive to the condition. The results enable us to make a theoretic study concerning Hadamard compression, which provides a solid theoretical analysis supporting the simulation results in previous literature. We show also that the conditional Fisher information can be used to measure the asymptotic Gaussianity.
{"title":"Entropic Conditional Central Limit Theorem and Hadamard Compression","authors":"Zhi-Ming Ma, Liu-Quan Yao, Shuai Yuan, Hua-Zi Zhang","doi":"arxiv-2401.11383","DOIUrl":"https://doi.org/arxiv-2401.11383","url":null,"abstract":"We make use of an entropic property to establish a convergence theorem (Main\u0000Theorem), which reveals that the conditional entropy measures the asymptotic\u0000Gaussianity. As an application, we establish the {it entropic conditional\u0000central limit theorem} (CCLT), which is stronger than the classical CCLT. As\u0000another application, we show that continuous input under iterated Hadamard\u0000transform, almost every distribution of the output conditional on the values of\u0000the previous signals will tend to Gaussian, and the conditional distribution is\u0000in fact insensitive to the condition. The results enable us to make a theoretic\u0000study concerning Hadamard compression, which provides a solid theoretical\u0000analysis supporting the simulation results in previous literature. We show also\u0000that the conditional Fisher information can be used to measure the asymptotic\u0000Gaussianity.","PeriodicalId":501433,"journal":{"name":"arXiv - CS - Information Theory","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139559770","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}
In this paper, we address the node repair problem of Reed-Solomon (RS) coded distributed storage systems. Specifically, to overcome the challenges of multiple-node failures of RS codes under the rack-aware storage model, we employ good polynomials to guide the placement of the conventional RS codes into racks and then propose a novel repair framework for the resultant rack-aware RS codes, which can transform its repair to that under the homogeneous storage model. As applications of our repair framework, firstly we present the repair scheme of multiple-node failures for some existing constructions, which can only repair a single-node failure before. Secondly, we deduce several new constructions of rack-aware RS codes supporting the repair of multiple-node failures.
{"title":"A Transformation of Repairing Reed-Solomon Codes from Rack-Aware Storage Model to Homogeneous Storage Model","authors":"Yumeng Yang, Han Cai, Xiaohu Tang","doi":"arxiv-2401.11390","DOIUrl":"https://doi.org/arxiv-2401.11390","url":null,"abstract":"In this paper, we address the node repair problem of Reed-Solomon (RS) coded\u0000distributed storage systems. Specifically, to overcome the challenges of\u0000multiple-node failures of RS codes under the rack-aware storage model, we\u0000employ good polynomials to guide the placement of the conventional RS codes\u0000into racks and then propose a novel repair framework for the resultant\u0000rack-aware RS codes, which can transform its repair to that under the\u0000homogeneous storage model. As applications of our repair framework, firstly we\u0000present the repair scheme of multiple-node failures for some existing\u0000constructions, which can only repair a single-node failure before. Secondly, we\u0000deduce several new constructions of rack-aware RS codes supporting the repair\u0000of multiple-node failures.","PeriodicalId":501433,"journal":{"name":"arXiv - CS - Information Theory","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139559146","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}
Identifying the most suitable variables to represent the state is a fundamental challenge in Reinforcement Learning (RL). These variables must efficiently capture the information necessary for making optimal decisions. In order to address this problem, in this paper, we introduce the Transfer Entropy Redundancy Criterion (TERC), an information-theoretic criterion, which determines if there is textit{entropy transferred} from state variables to actions during training. We define an algorithm based on TERC that provably excludes variables from the state that have no effect on the final performance of the agent, resulting in more sample efficient learning. Experimental results show that this speed-up is present across three different algorithm classes (represented by tabular Q-learning, Actor-Critic, and Proximal Policy Optimization (PPO)) in a variety of environments. Furthermore, to highlight the differences between the proposed methodology and the current state-of-the-art feature selection approaches, we present a series of controlled experiments on synthetic data, before generalizing to real-world decision-making tasks. We also introduce a representation of the problem that compactly captures the transfer of information from state variables to actions as Bayesian networks.
{"title":"Information-Theoretic State Variable Selection for Reinforcement Learning","authors":"Charles Westphal, Stephen Hailes, Mirco Musolesi","doi":"arxiv-2401.11512","DOIUrl":"https://doi.org/arxiv-2401.11512","url":null,"abstract":"Identifying the most suitable variables to represent the state is a\u0000fundamental challenge in Reinforcement Learning (RL). These variables must\u0000efficiently capture the information necessary for making optimal decisions. In\u0000order to address this problem, in this paper, we introduce the Transfer Entropy\u0000Redundancy Criterion (TERC), an information-theoretic criterion, which\u0000determines if there is textit{entropy transferred} from state variables to\u0000actions during training. We define an algorithm based on TERC that provably\u0000excludes variables from the state that have no effect on the final performance\u0000of the agent, resulting in more sample efficient learning. Experimental results\u0000show that this speed-up is present across three different algorithm classes\u0000(represented by tabular Q-learning, Actor-Critic, and Proximal Policy\u0000Optimization (PPO)) in a variety of environments. Furthermore, to highlight the\u0000differences between the proposed methodology and the current state-of-the-art\u0000feature selection approaches, we present a series of controlled experiments on\u0000synthetic data, before generalizing to real-world decision-making tasks. We\u0000also introduce a representation of the problem that compactly captures the\u0000transfer of information from state variables to actions as Bayesian networks.","PeriodicalId":501433,"journal":{"name":"arXiv - CS - Information Theory","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139559167","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}
The aim of this article is to give lower bounds on the parameters of algebraic geometric error-correcting codes constructed from projective bundles over Deligne--Lusztig surfaces. The methods based on an intensive use of the intersection theory allow us to extend the codes previously constructed from higher-dimensional varieties, as well as those coming from curves. General bounds are obtained for the case of projective bundles of rank $2$ over standard Deligne-Lusztig surfaces, and some explicit examples coming from surfaces of type $A_{2}$ and ${}^{2}A_{4}$ are given.
{"title":"Error-Correcting Codes on Projective Bundles over Deligne-Lusztig varieties","authors":"Daniel Camazón Portela, Juan Antonio López Ramos","doi":"arxiv-2401.11433","DOIUrl":"https://doi.org/arxiv-2401.11433","url":null,"abstract":"The aim of this article is to give lower bounds on the parameters of\u0000algebraic geometric error-correcting codes constructed from projective bundles\u0000over Deligne--Lusztig surfaces. The methods based on an intensive use of the\u0000intersection theory allow us to extend the codes previously constructed from\u0000higher-dimensional varieties, as well as those coming from curves. General\u0000bounds are obtained for the case of projective bundles of rank $2$ over\u0000standard Deligne-Lusztig surfaces, and some explicit examples coming from\u0000surfaces of type $A_{2}$ and ${}^{2}A_{4}$ are given.","PeriodicalId":501433,"journal":{"name":"arXiv - CS - Information Theory","volume":"113 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139559320","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}
In this paper we study gossip networks where a source observing a process sends updates to an underlying graph. Nodes in the graph communicate to their neighbors by randomly sending updates. Our interest is studying the version age of information (vAoI) metric over various classes of networks. It is known that the version age of $K_n$ is logarithmic, and the version age of $overline{K_n}$ is linear. We study the question `how does the vAoI evolve as we interpolate between $K_n$ and $overline{K_n}$' by studying ErdH{o}s-Reyni random graphs, random $d$-regular graphs, and bipartite networks. Our main results are proving the existence of a threshold in $G(n,p)$ from rational to logarithmic average version age, and showing $G(n,d)$ almost surely has logarithmic version age for constant $d$. We also characterize the version age of complete bipartite graphs $K_{L,R}$, when we let $L$ vary from $O(1)$ to $O(n)$.
{"title":"Age of Gossip in Random and Bipartite Networks","authors":"Thomas maranzatto","doi":"arxiv-2401.11580","DOIUrl":"https://doi.org/arxiv-2401.11580","url":null,"abstract":"In this paper we study gossip networks where a source observing a process\u0000sends updates to an underlying graph. Nodes in the graph communicate to their\u0000neighbors by randomly sending updates. Our interest is studying the version age\u0000of information (vAoI) metric over various classes of networks. It is known that\u0000the version age of $K_n$ is logarithmic, and the version age of\u0000$overline{K_n}$ is linear. We study the question `how does the vAoI evolve as\u0000we interpolate between $K_n$ and $overline{K_n}$' by studying ErdH{o}s-Reyni\u0000random graphs, random $d$-regular graphs, and bipartite networks. Our main\u0000results are proving the existence of a threshold in $G(n,p)$ from rational to\u0000logarithmic average version age, and showing $G(n,d)$ almost surely has\u0000logarithmic version age for constant $d$. We also characterize the version age\u0000of complete bipartite graphs $K_{L,R}$, when we let $L$ vary from $O(1)$ to\u0000$O(n)$.","PeriodicalId":501433,"journal":{"name":"arXiv - CS - Information Theory","volume":"113 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139559336","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}
Gyoseung Lee, Hyeongtaek Lee, Donghwan Kim, Jaehoon Chung, A. Lee. Swindlehurst, Junil Choi
This paper investigates reconfigurable intelligent surface (RIS)-aided frequency division duplexing (FDD) communication systems. Since the downlink and uplink signals are simultaneously transmitted in FDD, the phase shifts at the RIS should be designed to support both transmissions. Considering a single-user multiple-input multiple-output system, we formulate a weighted sum-rate maximization problem to jointly maximize the downlink and uplink system performance. To tackle the non-convex optimization problem, we adopt an alternating optimization (AO) algorithm, in which two phase shift optimization techniques are developed to handle the unit-modulus constraints induced by the reflection coefficients at the RIS. The first technique exploits the manifold optimization-based algorithm, while the second uses a lower-complexity AO approach. Numerical results verify that the proposed techniques rapidly converge to local optima and significantly improve the overall system performance compared to existing benchmark schemes.
本文研究了可重构智能表面（RIS）辅助频分双工（FDD）通信系统。由于下行链路和上行链路信号在 FDD 中同时传输，因此 RIS 的相移设计应支持这两种传输。考虑到单用户多输入多输出系统，我们提出了一个加权和速率最大化问题，以共同实现下行链路和上行链路系统性能的最大化。为了解决这个非凸优化问题，我们采用了模拟优化（AO）算法，其中开发了两种相移优化技术来处理由 RIS 上的反射系数引起的单位模数约束。第一种技术利用了基于流形优化的算法，而第二种技术则使用了复杂度较低的 AO 方法。数值结果证明，与现有的基准方案相比，所提出的技术能迅速收敛到局部最优，并显著提高系统的整体性能。
{"title":"Joint Downlink and Uplink Optimization for RIS-Aided FDD MIMO Communication Systems","authors":"Gyoseung Lee, Hyeongtaek Lee, Donghwan Kim, Jaehoon Chung, A. Lee. Swindlehurst, Junil Choi","doi":"arxiv-2401.11429","DOIUrl":"https://doi.org/arxiv-2401.11429","url":null,"abstract":"This paper investigates reconfigurable intelligent surface (RIS)-aided\u0000frequency division duplexing (FDD) communication systems. Since the downlink\u0000and uplink signals are simultaneously transmitted in FDD, the phase shifts at\u0000the RIS should be designed to support both transmissions. Considering a\u0000single-user multiple-input multiple-output system, we formulate a weighted\u0000sum-rate maximization problem to jointly maximize the downlink and uplink\u0000system performance. To tackle the non-convex optimization problem, we adopt an\u0000alternating optimization (AO) algorithm, in which two phase shift optimization\u0000techniques are developed to handle the unit-modulus constraints induced by the\u0000reflection coefficients at the RIS. The first technique exploits the manifold\u0000optimization-based algorithm, while the second uses a lower-complexity AO\u0000approach. Numerical results verify that the proposed techniques rapidly\u0000converge to local optima and significantly improve the overall system\u0000performance compared to existing benchmark schemes.","PeriodicalId":501433,"journal":{"name":"arXiv - CS - Information Theory","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139559763","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}
Ruichen Zhang, Hongyang Du, Yinqiu Liu, Dusit Niyato, Jiawen Kang, Sumei Sun, Xuemin Shen, H. Vincent Poor
With the advance of artificial intelligence (AI), the emergence of Google Gemini and OpenAI Q* marks the direction towards artificial general intelligence (AGI). To implement AGI, the concept of interactive AI (IAI) has been introduced, which can interactively understand and respond not only to human user input but also to dynamic system and network conditions. In this article, we explore an integration and enhancement of IAI in networking. We first comprehensively review recent developments and future perspectives of AI and then introduce the technology and components of IAI. We then explore the integration of IAI into the next-generation networks, focusing on how implicit and explicit interactions can enhance network functionality, improve user experience, and promote efficient network management. Subsequently, we propose an IAI-enabled network management and optimization framework, which consists of environment, perception, action, and brain units. We also design the pluggable large language model (LLM) module and retrieval augmented generation (RAG) module to build the knowledge base and contextual memory for decision-making in the brain unit. We demonstrate the effectiveness of the framework through case studies. Finally, we discuss potential research directions for IAI-based networks.
{"title":"Interactive AI with Retrieval-Augmented Generation for Next Generation Networking","authors":"Ruichen Zhang, Hongyang Du, Yinqiu Liu, Dusit Niyato, Jiawen Kang, Sumei Sun, Xuemin Shen, H. Vincent Poor","doi":"arxiv-2401.11391","DOIUrl":"https://doi.org/arxiv-2401.11391","url":null,"abstract":"With the advance of artificial intelligence (AI), the emergence of Google\u0000Gemini and OpenAI Q* marks the direction towards artificial general\u0000intelligence (AGI). To implement AGI, the concept of interactive AI (IAI) has\u0000been introduced, which can interactively understand and respond not only to\u0000human user input but also to dynamic system and network conditions. In this\u0000article, we explore an integration and enhancement of IAI in networking. We\u0000first comprehensively review recent developments and future perspectives of AI\u0000and then introduce the technology and components of IAI. We then explore the\u0000integration of IAI into the next-generation networks, focusing on how implicit\u0000and explicit interactions can enhance network functionality, improve user\u0000experience, and promote efficient network management. Subsequently, we propose\u0000an IAI-enabled network management and optimization framework, which consists of\u0000environment, perception, action, and brain units. We also design the pluggable\u0000large language model (LLM) module and retrieval augmented generation (RAG)\u0000module to build the knowledge base and contextual memory for decision-making in\u0000the brain unit. We demonstrate the effectiveness of the framework through case\u0000studies. Finally, we discuss potential research directions for IAI-based\u0000networks.","PeriodicalId":501433,"journal":{"name":"arXiv - CS - Information Theory","volume":"120 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139559144","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}
Utilization of inter-base station cooperation for information processing has shown great potential in enhancing the overall quality of communication services (QoS) in wireless communication networks. Nevertheless, such cooperations require the knowledge of channel state information (CSI) at base stations (BSs), which is assumed to be perfectly known. However, CSI errors are inevitable in practice which necessitates beamforming techniques that can achieve robust performance in the presence of channel estimation errors. Existing approaches relax the robust beamforming design problems into semidefinite programming (SDP), which can only achieve a solution that is far from being optimal. To this end, this paper views robust beamforming design problems from a bilevel optimization perspective. In particular, we focus on maximizing the worst-case weighted sum-rate (WSR) in the downlink multi-cell multi-user multiple-input single-output (MISO) system considering bounded CSI errors. We first reformulate this problem into a bilevel optimization problem and then develop an efficient algorithm based on the cutting plane method. A distributed optimization algorithm has also been developed to facilitate the parallel processing in practical settings. Numerical results are provided to confirm the effectiveness of the proposed algorithm in terms of performance and complexity, particularly in the presence of CSI uncertainties.
{"title":"Robust Beamforming for Downlink Multi-Cell Systems: A Bilevel Optimization Perspective","authors":"Xingdi Chen, Yu Xiong, Kai Yang","doi":"arxiv-2401.11409","DOIUrl":"https://doi.org/arxiv-2401.11409","url":null,"abstract":"Utilization of inter-base station cooperation for information processing has\u0000shown great potential in enhancing the overall quality of communication\u0000services (QoS) in wireless communication networks. Nevertheless, such\u0000cooperations require the knowledge of channel state information (CSI) at base\u0000stations (BSs), which is assumed to be perfectly known. However, CSI errors are\u0000inevitable in practice which necessitates beamforming techniques that can\u0000achieve robust performance in the presence of channel estimation errors.\u0000Existing approaches relax the robust beamforming design problems into\u0000semidefinite programming (SDP), which can only achieve a solution that is far\u0000from being optimal. To this end, this paper views robust beamforming design\u0000problems from a bilevel optimization perspective. In particular, we focus on\u0000maximizing the worst-case weighted sum-rate (WSR) in the downlink multi-cell\u0000multi-user multiple-input single-output (MISO) system considering bounded CSI\u0000errors. We first reformulate this problem into a bilevel optimization problem\u0000and then develop an efficient algorithm based on the cutting plane method. A\u0000distributed optimization algorithm has also been developed to facilitate the\u0000parallel processing in practical settings. Numerical results are provided to\u0000confirm the effectiveness of the proposed algorithm in terms of performance and\u0000complexity, particularly in the presence of CSI uncertainties.","PeriodicalId":501433,"journal":{"name":"arXiv - CS - Information Theory","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139559329","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}
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