arXiv subjects

Ziheng Liu

Publications and source records attributed to Ziheng Liu.

35 records · Page 2Linked to original sources

arXiv2024

Mobile Cell-Free Massive MIMO with Multi-Agent Reinforcement Learning: A Scalable Framework

Cell-free massive multiple-input multiple-output (mMIMO) offers significant advantages in mobility scenarios, mainly due to the elimination of cell boundaries and strong macro diversity. In this paper, we examine the downlink performance of cell-free mMIMO systems equipped with mobile-APs utilizing the concept of unmanned aerial vehicles, where mobility and power control are jointly considered to effectively enhance coverage and suppress interference. However, the high computational complexity, poor collaboration, limited scalability, and uneven reward distribution of conventional optimization schemes lead to serious performance degradation and instability. These factors complicate the provision of consistent and high-quality service across all user equipments in downlink cell-free mMIMO systems. Consequently, we propose a novel scalable framework enhanced by multi-agent reinforcement learning (MARL) to tackle these challenges. The established framework incorporates a graph neural network (GNN)-aided communication mechanism to facilitate effective collaboration among agents, a permutation architecture to improve scalability, and a directional decoupling architecture to accurately distinguish contributions. In the numerical results, we present comparisons of different optimization schemes and network architectures, which reveal that the proposed scheme can effectively enhance system performance compared to conventional schemes due to the adoption of advanced technologies. In particular, appropriately compressing the observation space of agents is beneficial for achieving a better balance between performance and convergence.

cs.IT↗

arXiv2024

Joint Precoding and AP Selection for Energy Efficient RIS-aided Cell-Free Massive MIMO Using Multi-agent Reinforcement Learning

Cell-free (CF) massive multiple-input multiple-output (mMIMO) and reconfigurable intelligent surface (RIS) are two advanced transceiver technologies for realizing future sixth-generation (6G) networks. In this paper, we investigate the joint precoding and access point (AP) selection for energy efficient RIS-aided CF mMIMO system. To address the associated computational complexity and communication power consumption, we advocate for user-centric dynamic networks in which each user is served by a subset of APs rather than by all of them. Based on the user-centric network, we formulate a joint precoding and AP selection problem to maximize the energy efficiency (EE) of the considered system. To solve this complex nonconvex problem, we propose an innovative double-layer multi-agent reinforcement learning (MARL)-based scheme. Moreover, we propose an adaptive power threshold-based AP selection scheme to further enhance the EE of the considered system. To reduce the computational complexity of the RIS-aided CF mMIMO system, we introduce a fuzzy logic (FL) strategy into the MARL scheme to accelerate convergence. The simulation results show that the proposed FL-based MARL cooperative architecture effectively improves EE performance, offering a 85\% enhancement over the zero-forcing (ZF) method, and achieves faster convergence speed compared with MARL. It is important to note that increasing the transmission power of the APs or the number of RIS elements can effectively enhance the spectral efficiency (SE) performance, which also leads to an increase in power consumption, resulting in a non-trivial trade-off between the quality of service and EE performance.

cs.IT↗

arXiv2024

Cooperative Multi-Target Positioning for Cell-Free Massive MIMO with Multi-Agent Reinforcement Learning

Cell-free massive multiple-input multiple-output (mMIMO) is a promising technology to empower next-generation mobile communication networks. In this paper, to address the computational complexity associated with conventional fingerprint positioning, we consider a novel cooperative positioning architecture that involves certain relevant access points (APs) to establish positioning similarity coefficients. Then, we propose an innovative joint positioning and correction framework employing multi-agent reinforcement learning (MARL) to tackle the challenges of high-dimensional sophisticated signal processing, which mainly leverages on the received signal strength information for preliminary positioning, supplemented by the angle of arrival information to refine the initial position estimation. Moreover, to mitigate the bias effects originating from remote APs, we design a cooperative weighted K-nearest neighbor (Co-WKNN)-based estimation scheme to select APs with a high correlation to participate in user positioning. In the numerical results, we present comparisons of various user positioning schemes, which reveal that the proposed MARL-based positioning scheme with Co-WKNN can effectively improve positioning performance. It is important to note that the cooperative positioning architecture is a critical element in striking a balance between positioning performance and computational complexity.

cs.IT↗

arXiv2024

Distributed Collaborative User Positioning for Cell-Free Massive MIMO with Multi-Agent Reinforcement Learning

In this paper, we investigate a cell-free massive multiple-input multiple-output system, which exhibits great potential in enhancing the capabilities of next-generation mobile communication networks. We first study the distributed positioning problem to lay the groundwork for solving resource allocation and interference management issues. Instead of relying on computationally and spatially complex fingerprint positioning methods, we propose a novel two-stage distributed collaborative positioning architecture with multi-agent reinforcement learning (MARL) network, consisting of a received signal strength-based preliminary positioning network and an angle of arrival-based auxiliary correction network. Our experimental results demonstrate that the two-stage distributed collaborative user positioning architecture can outperform conventional fingerprint positioning methods in terms of positioning accuracy.

cs.IT↗

arXiv2024

Joint AP-UE Association and Precoding for SIM-Aided Cell-Free Massive MIMO Systems

Cell-free (CF) massive multiple-input multiple-output (mMIMO) systems are emerging as promising alternatives to cellular networks, especially in ultra-dense environments. However, further capacity enhancement requires the deployment of more access points (APs), which will lead to high costs and high energy consumption. To address this issue, in this paper, we explore the integration of low-power, low-cost stacked intelligent metasurfaces (SIM) into CF mMIMO systems to enhance AP capabilities. The key point is that SIM performs precoding-related matrix operations in the wave domain. As a consequence, each AP antenna only needs to transmit data streams for a single user equipment (UE), eliminating the need for complex baseband digital precoding. Then, we formulate the problem of joint AP-UE association and precoding at APs and SIMs to maximize the system sum rate. Due to the non-convexity and high complexity of the formulated problem, we propose a two-stage signal processing framework to solve it. In particular, in the first stage, we propose an AP antenna greedy association (AGA) algorithm to minimize UE interference. In the second stage, we introduce an alternating optimization (AO)-based algorithm that separates the joint power and wave-based precoding optimization problem into two distinct sub-problems: the complex quadratic transform method is used for AP antenna power control, and the projection gradient ascent (PGA) algorithm is employed to find suboptimal solutions for the SIM wave-based precoding. Finally, the numerical results validate the effectiveness of the proposed framework and assess the performance enhancement achieved by the algorithm in comparison to various benchmark schemes. The results show that, with the same number of SIM meta-atoms, the proposed algorithm improves the sum rate by approximately 275% compared to the benchmark scheme.

cs.IT↗

arXiv2024

Joint SIM Configuration and Power Allocation for Stacked Intelligent Metasurface-assisted MU-MISO Systems with TD3

The stacked intelligent metasurface (SIM) emerges as an innovative technology with the ability to directly manipulate electromagnetic (EM) wave signals, drawing parallels to the operational principles of artificial neural networks (ANN). Leveraging its structure for direct EM signal processing alongside its low-power consumption, SIM holds promise for enhancing system performance within wireless communication systems. In this paper, we focus on SIM-assisted multi-user multi-input and single-output (MU-MISO) system downlink scenarios in the transmitter. We proposed a joint optimization method for SIM phase shift configuration and antenna power allocation based on the twin delayed deep deterministic policy gradient (TD3) algorithm to efficiently improve the sum rate. The results show that the proposed algorithm outperforms both deep deterministic policy gradient (DDPG) and alternating optimization (AO) algorithms. Furthermore, increasing the number of meta-atoms per layer of the SIM is always beneficial. However, continuously increasing the number of layers of SIM does not lead to sustained performance improvement.

eess.SP↗

arXiv2024

CXL Shared Memory Programming: Barely Distributed and Almost Persistent

While Compute Express Link (CXL) enables support for cache-coherent shared memory among multiple nodes, it also introduces new types of failures--processes can fail before data does, or data might fail before a process does. The lack of a failure model for CXL-based shared memory makes it challenging to understand and mitigate these failures. To solve these challenges, in this paper, we describe a model categorizing and handling the CXL-based shared memory's failures: data and process failures. Data failures in CXL-based shared memory render data inaccessible or inconsistent for a currently running application. We argue that such failures are unlike data failures in distributed storage systems and require CXL-specific handling. To address this, we look into traditional data failure mitigation techniques like erasure coding and replication and propose new solutions to better handle data failures in CXL-based shared memory systems. Next, we look into process failures and compare the failures and potential solutions with PMEM's failure model and programming solutions. We argue that although PMEM shares some of CXL's characteristics, it does not fully address CXL's volatile nature and low access latencies. Finally, taking inspiration from PMEM programming solutions, we propose techniques to handle these new failures. Thus, this paper is the first work to define the CXL-based shared memory failure model and propose tailored solutions that address challenges specific to CXL-based systems.

cs.DC↗

arXiv2024

Joint Cooperative Clustering and Power Control for Energy-Efficient Cell-Free XL-MIMO with Multi-Agent Reinforcement Learning

In this paper, we investigate the amalgamation of cell-free (CF) and extremely large-scale multiple-input multiple-output (XL-MIMO) technologies, referred to as a CF XL-MIMO, as a promising advancement for enabling future mobile networks. To address the computational complexity and communication power consumption associated with conventional centralized optimization, we focus on user-centric dynamic networks in which each user is served by an adaptive subset of access points (AP) rather than all of them. We begin our research by analyzing a joint resource allocation problem for energy-efficient CF XL-MIMO systems, encompassing cooperative clustering and power control design, where all clusters are adaptively adjustable. Then, we propose an innovative double-layer multi-agent reinforcement learning (MARL)-based scheme, which offers an effective strategy to tackle the challenges of high-dimensional signal processing. In the section of numerical results, we compare various algorithms with different network architectures. These comparisons reveal that the proposed MARL-based cooperative architecture can effectively strike a balance between system performance and communication overhead, thereby improving energy efficiency performance. It is important to note that increasing the number of user equipments participating in information sharing can effectively enhance SE performance, which also leads to an increase in power consumption, resulting in a non-trivial trade-off between the number of participants and EE performance.

cs.IT↗

arXiv2024

Vertex Block Descent

We introduce vertex block descent, a block coordinate descent solution for the variational form of implicit Euler through vertex-level Gauss-Seidel iterations. It operates with local vertex position updates that achieve reductions in global variational energy with maximized parallelism. This forms a physics solver that can achieve numerical convergence with unconditional stability and exceptional computation performance. It can also fit in a given computation budget by simply limiting the iteration count while maintaining its stability and superior convergence rate. We present and evaluate our method in the context of elastic body dynamics, providing details of all essential components and showing that it outperforms alternative techniques. In addition, we discuss and show examples of how our method can be used for other simulation systems, including particle-based simulations and rigid bodies.

cs.GR↗

arXiv2024

Multi-agent Reinforcement Learning-based Joint Precoding and Phase Shift Optimization for RIS-aided Cell-Free Massive MIMO Systems

Cell-free (CF) massive multiple-input multiple-output (mMIMO) is a promising technique for achieving high spectral efficiency (SE) using multiple distributed access points (APs). However, harsh propagation environments often lead to significant communication performance degradation due to high penetration loss. To overcome this issue, we introduce the reconfigurable intelligent surface (RIS) into the CF mMIMO system as a low-cost and power-efficient solution. In this paper, we focus on optimizing the joint precoding design of the RIS-aided CF mMIMO system to maximize the sum SE. This involves optimizing the precoding matrix at the APs and the reflection coefficients at the RIS. To tackle this problem, we propose a fully distributed multi-agent reinforcement learning (MARL) algorithm that incorporates fuzzy logic (FL). Unlike conventional approaches that rely on alternating optimization techniques, our FL-based MARL algorithm only requires local channel state information, which reduces the need for high backhaul capacity. Simulation results demonstrate that our proposed FL-MARL algorithm effectively reduces computational complexity while achieving similar performance as conventional MARL methods.

cs.IT↗

arXiv2024

Graph Neural Network Meets Multi-Agent Reinforcement Learning: Fundamentals, Applications, and Future Directions

Multi-agent reinforcement learning (MARL) has become a fundamental component of next-generation wireless communication systems. Theoretically, although MARL has the advantages of low computational complexity and fast convergence rate, there exist several challenges including partial observability, non-stationary, and scalability. In this article, we investigate a novel MARL with graph neural network-aided communication (GNNComm-MARL) to address the aforementioned challenges by making use of graph attention networks to effectively sample neighborhoods and selectively aggregate messages. Furthermore, we thoroughly study the architecture of GNNComm-MARL and present a systematic design solution. We then present the typical applications of GNNComm-MARL from two aspects: resource allocation and mobility management. The results obtained unveil that GNNComm-MARL can achieve better performance with lower communication overhead compared to conventional communication schemes. Finally, several important research directions regarding GNNComm-MARL are presented to facilitate further investigation.

cs.IT↗

arXiv2023

Cell-Free XL-MIMO Meets Multi-Agent Reinforcement Learning: Architectures, Challenges, and Future Directions

Cell-free massive multiple-input multiple-output (mMIMO) and extremely large-scale MIMO (XL-MIMO) are regarded as promising innovations for the forthcoming generation of wireless communication systems. Their significant advantages in augmenting the number of degrees of freedom have garnered considerable interest. In this article, we first review the essential opportunities and challenges induced by XL-MIMO systems. We then propose the enhanced paradigm of cell-free XL-MIMO, which incorporates multi-agent reinforcement learning (MARL) to provide a distributed strategy for tackling the problem of high-dimension signal processing and costly energy consumption. Based on the unique near-field characteristics, we propose two categories of the low-complexity design, i.e., antenna selection and power control, to adapt to different cell-free XL-MIMO scenarios and achieve the maximum data rate. For inspiration, several critical future research directions pertaining to green cell-free XL-MIMO systems are presented.

cs.IT↗

arXiv2023

Double-Layer Power Control for Mobile Cell-Free XL-MIMO with Multi-Agent Reinforcement Learning

Cell-free (CF) extremely large-scale multiple-input multiple-output (XL-MIMO) is regarded as a promising technology for enabling future wireless communication systems. Significant attention has been generated by its considerable advantages in augmenting degrees of freedom. In this paper, we first investigate a CF XL-MIMO system with base stations equipped with XL-MIMO panels under a dynamic environment. Then, we propose an innovative multi-agent reinforcement learning (MARL)-based power control algorithm that incorporates predictive management and distributed optimization architecture, which provides a dynamic strategy for addressing high-dimension signal processing problems. Specifically, we compare various MARL-based algorithms, which shows that the proposed MARL-based algorithm effectively strikes a balance between spectral efficiency (SE) performance and convergence time. Moreover, we consider a double-layer power control architecture based on the large-scale fading coefficients between antennas to suppress interference within dynamic systems. Compared to the single-layer architecture, the results obtained unveil that the proposed double-layer architecture has a nearly24% SE performance improvement, especially with massive antennas and smaller antenna spacing.

cs.IT↗

arXiv2023

RECIPE: Rateless Erasure Codes Induced by Protocol-Based Encoding

LT (Luby transform) codes are a celebrated family of rateless erasure codes (RECs). Most of existing LT codes were designed for applications in which a centralized encoder possesses all message blocks and is solely responsible for encoding them into codewords. Distributed LT codes, in which message blocks are physically scattered across multiple different locations (encoders) that need to collaboratively perform the encoding, has never been systemically studied before despite its growing importance in applications. In this work, we present the first systemic study of LT codes in the distributed setting, and make the following three major contributions. First, we show that only a proper subset of LT codes are feasible in the distributed setting, and give the sufficient and necessary condition for such feasibility. Second, we propose a distributed encoding protocol that can efficiently implement any feasible code. The protocol is parameterized by a so-called action probability array (APA) that is only a few KBs in size, and any feasible code corresponds to a valid APA setting and vice versa. Third, we propose two heuristic search algorithms that have led to the discovery of feasible codes that are much more efficient than the state of the art.

cs.IT↗

arXiv2023

Uplink Power Control for Extremely Large-Scale MIMO with Multi-Agent Reinforcement Learning and Fuzzy Logic

In this paper, we investigate the uplink transmit power optimization problem in cell-free (CF) extremely large-scale multiple-input multiple-output (XL-MIMO) systems. Instead of applying the traditional methods, we propose two signal processing architectures: the centralized training and centralized execution with fuzzy logic as well as the centralized training and decentralized execution with fuzzy logic, respectively, which adopt the amalgamation of multi-agent reinforcement learning (MARL) and fuzzy logic to solve the design problem of power control for the maximization of the system spectral efficiency (SE). Furthermore, the uplink performance of the system adopting maximum ratio (MR) combining and local minimum mean-squared error (L-MMSE) combining is evaluated. Our results show that the proposed methods with fuzzy logic outperform the conventional MARL-based method and signal processing methods in terms of computational complexity. Also, the SE performance under MR combining is even better than that of the conventional MARL-based method.

cs.IT↗

arXiv2022

SPOC learner's final grade prediction based on a novel sampling batch normalization embedded neural network method

Recent years have witnessed the rapid growth of Small Private Online Courses (SPOC) which is able to highly customized and personalized to adapt variable educational requests, in which machine learning techniques are explored to summarize and predict the learner's performance, mostly focus on the final grade. However, the problem is that the final grade of learners on SPOC is generally seriously imbalance which handicaps the training of prediction model. To solve this problem, a sampling batch normalization embedded deep neural network (SBNEDNN) method is developed in this paper. First, a combined indicator is defined to measure the distribution of the data, then a rule is established to guide the sampling process. Second, the batch normalization (BN) modified layers are embedded into full connected neural network to solve the data imbalanced problem. Experimental results with other three deep learning methods demonstrates the superiority of the proposed method.

cs.CV↗

arXiv2020

Space- and Computationally-Efficient Set Reconciliation via Parity Bitmap Sketch (PBS)

Set reconciliation is a fundamental algorithmic problem that arises in many networking, system, and database applications. In this problem, two large sets A and B of objects (bitcoins, files, records, etc.) are stored respectively at two different network-connected hosts, which we name Alice and Bob respectively. Alice and Bob communicate with each other to learn $AΔB$, the difference between A and B, and as a result the reconciled set $A\bigcup B$. Current set reconciliation schemes are based on either Invertible Bloom Filters (IBF) or Error-Correction Codes (ECC). The former has a low computational complexity of O(d), where d is the cardinality of $AΔB$, but has a high communication overhead that is several times larger than the theoretical minimum. The latter has a low communication overhead close to the theoretical minimum, but has a much higher computational complexity of $O(d^2)$. In this work, we propose Parity Bitmap Sketch (PBS), an ECC- based set reconciliation scheme that gets the better of both worlds: PBS has both a low computational complexity of O(d) just like IBF-based solutions and a low communication overhead of roughly twice the theoretical minimum. A separate contribution of this work is a novel rigorous analytical framework that can be used for the precise calculation of various performance metrics and for the near-optimal parameter tuning of PBS.

cs.DB↗