644 research outputs found
A NOMA-enhanced reconfigurable access scheme with device pairing for M2M networks
This paper aims to address the distinct requirements
of machine-to-machine networks, particularly heterogeneity and
massive transmissions. To this end, a reconfigurable medium
access control (MAC) with the ability to choose a proper access
scheme with the optimal configuration for devices based on
the network status is proposed. In this scheme, in each frame,
a separate time duration is allocated for each of the nonorthogonal multiple access (NOMA)-based, orthogonal multiple
access (OMA)-based, and random access-based segments, where
the length of each segment can be optimized. To solve this
optimization problem, an iterative algorithm consisting of two
sub-problems is proposed. The first sub-problem deals with
selecting devices for the NOMA/OMA-based transmissions, while
the second one optimizes the parameter of the random access
scheme. To show the efficacy of the proposed scheme, the results
are compared with the reconfigurable scheme which does not
support NOMA. The results demonstrate that by using a proper
device pairing scheme for the NOMA-based transmissions, the
proposed reconfigurable scheme achieves better performance
when NOMA is adopted
Federated Learning for 6G: Applications, Challenges, and Opportunities
Traditional machine learning is centralized in the cloud (data centers).
Recently, the security concern and the availability of abundant data and
computation resources in wireless networks are pushing the deployment of
learning algorithms towards the network edge. This has led to the emergence of
a fast growing area, called federated learning (FL), which integrates two
originally decoupled areas: wireless communication and machine learning. In
this paper, we provide a comprehensive study on the applications of FL for
sixth generation (6G) wireless networks. First, we discuss the key requirements
in applying FL for wireless communications. Then, we focus on the motivating
application of FL for wireless communications. We identify the main problems,
challenges, and provide a comprehensive treatment of implementing FL techniques
for wireless communications
Federated Learning for 6G: Applications, Challenges, and Opportunities
Standard machine-learning approaches involve the centralization of training data in a data center, where centralized machine-learning algorithms can be applied for data analysis and inference. However, due to privacy restrictions and limited communication resources in wireless networks, it is often undesirable or impractical for the devices to transmit data to parameter sever. One approach to mitigate these problems is federated learning (FL), which enables the devices to train a common machine learning model without data sharing and transmission. This paper provides a comprehensive overview of FL applications for envisioned sixth generation (6G) wireless networks. In particular, the essential requirements for applying FL to wireless communications are first described. Then potential FL applications in wireless communications are detailed. The main problems and challenges associated with such applications are discussed. Finally, a comprehensive FL implementation for wireless communications is described
Resource Allocation in the RIS Assisted SCMA Cellular Network Coexisting with D2D Communications
The cellular network coexisting with device-to-device (D2D) communications
has been studied extensively. Reconfigurable intelligent surface (RIS) and
non-orthogonal multiple access (NOMA) are promising technologies for the
evolution of 5G, 6G and beyond. Besides, sparse code multiple access (SCMA) is
considered suitable for next-generation wireless network in code-domain NOMA.
In this paper, we consider the RIS-aided uplink SCMA cellular network
simultaneously with D2D users. We formulate the optimization problem which aims
to maximize the cellular sum-rate by jointly designing D2D users resource block
(RB) association, the transmitted power for both cellular users and D2D users,
and the phase shifts at the RIS. The power limitation and users communication
requirements are considered. The problem is non-convex, and it is challenging
to solve it directly. To handle this optimization problem, we propose an
efficient iterative algorithm based on block coordinate descent (BCD) method.
The original problem is decoupled into three subproblems to solve separately.
Simulation results demonstrate that the proposed scheme can significantly
improve the sum-rate performance over various schemes.Comment: IEEE Acces
Signal Processing and Learning for Next Generation Multiple Access in 6G
Wireless communication systems to date primarily rely on the orthogonality of
resources to facilitate the design and implementation, from user access to data
transmission. Emerging applications and scenarios in the sixth generation (6G)
wireless systems will require massive connectivity and transmission of a deluge
of data, which calls for more flexibility in the design concept that goes
beyond orthogonality. Furthermore, recent advances in signal processing and
learning have attracted considerable attention, as they provide promising
approaches to various complex and previously intractable problems of signal
processing in many fields. This article provides an overview of research
efforts to date in the field of signal processing and learning for
next-generation multiple access, with an emphasis on massive random access and
non-orthogonal multiple access. The promising interplay with new technologies
and the challenges in learning-based NGMA are discussed
RIS-Aided Cell-Free Massive MIMO Systems for 6G: Fundamentals, System Design, and Applications
An introduction of intelligent interconnectivity for people and things has
posed higher demands and more challenges for sixth-generation (6G) networks,
such as high spectral efficiency and energy efficiency, ultra-low latency, and
ultra-high reliability. Cell-free (CF) massive multiple-input multiple-output
(mMIMO) and reconfigurable intelligent surface (RIS), also called intelligent
reflecting surface (IRS), are two promising technologies for coping with these
unprecedented demands. Given their distinct capabilities, integrating the two
technologies to further enhance wireless network performances has received
great research and development attention. In this paper, we provide a
comprehensive survey of research on RIS-aided CF mMIMO wireless communication
systems. We first introduce system models focusing on system architecture and
application scenarios, channel models, and communication protocols.
Subsequently, we summarize the relevant studies on system operation and
resource allocation, providing in-depth analyses and discussions. Following
this, we present practical challenges faced by RIS-aided CF mMIMO systems,
particularly those introduced by RIS, such as hardware impairments and
electromagnetic interference. We summarize corresponding analyses and solutions
to further facilitate the implementation of RIS-aided CF mMIMO systems.
Furthermore, we explore an interplay between RIS-aided CF mMIMO and other
emerging 6G technologies, such as next-generation multiple-access (NGMA),
simultaneous wireless information and power transfer (SWIPT), and millimeter
wave (mmWave). Finally, we outline several research directions for future
RIS-aided CF mMIMO systems.Comment: 30 pages, 15 figure
- …