21 research outputs found
Downlink and Uplink Intelligent Reflecting Surface Aided Networks: NOMA and OMA
Intelligent reflecting surfaces (IRSs) are envisioned to provide
reconfigurable wireless environments for future communication networks. In this
paper, both downlink and uplink IRS-aided non-orthogonal multiple access (NOMA)
and orthogonal multiple access (OMA) networks are studied, in which an IRS is
deployed to enhance the coverage by assisting a cell-edge user device (UD) to
communicate with the base station (BS). To characterize system performance, new
channel statistics of the BS-IRS-UD link with Nakagami- fading are
investigated. For each scenario, the closed-form expressions for the outage
probability and ergodic rate are derived. To gain further insight, the
diversity order and high signal-to-noise ratio (SNR) slope for each scenario
are obtained according to asymptotic approximations in the high-SNR regime. It
is demonstrated that the diversity order is affected by the number of IRS
reflecting elements and Nakagami fading parameters, but the high-SNR slope is
not related to these parameters. Simulation results validate our analysis and
reveal the superiority of the IRS over the full-duplex decode-and-forward
relay.Comment: Accepted for publication in the IEEE Transactions on Wireless
Communication
Reconfigurable Intelligent Surface (RIS) Aided Multi-User Networks: Interplay Between NOMA and RIS
This article focuses on the exploitation of reconfigurable intelligent
surfaces (RISs) in multi-user networks employing orthogonal multiple access
(OMA) or non-orthogonal multiple access (NOMA), with an emphasis on
investigating the interplay between NOMA and RIS. Depending on whether the RIS
reflection coefficients can be adjusted only once or multiple times during one
transmission, we distinguish between static and dynamic RIS configurations. In
particular, the capacity region of RIS aided single-antenna NOMA networks is
characterized and compared with the OMA rate region from an
information-theoretic perspective, revealing that the dynamic RIS configuration
is capacity-achieving. Then, the impact of the RIS deployment location on the
performance of different multiple access schemes is investigated, which reveals
that asymmetric and symmetric deployment strategies are preferable for NOMA and
OMA, respectively. Furthermore, for RIS aided multiple-antenna NOMA networks,
three novel joint active and passive beamformer designs are proposed based on
both beamformer based and cluster based strategies. Finally, open research
problems for RIS-NOMA networks are highlighted.Comment: 13 pages, 6 figure
Dynamic Resource Management in CDRT Systems through Adaptive NOMA
This paper introduces a novel adaptive transmission scheme to amplify the
prowess of coordinated direct and relay transmission (CDRT) systems rooted in
non-orthogonal multiple access principles. Leveraging the maximum ratio
transmission scheme, we seamlessly meet the prerequisites of CDRT while
harnessing the potential of dynamic power allocation and directional antennas
to elevate the system's operational efficiency. Through meticulous derivations,
we unveil closed-form expressions depicting the exact effective sum throughput.
Our simulation results adeptly validate the theoretical analysis and vividly
showcase the effectiveness of the proposed scheme.Comment: 11 pages, 7 figures, submitted to IEEE journal for revie
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
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
Outage Probability Analysis for Two-antennas MISO-NOMA Downlink with Statistical CSI
International audienceIn this paper, we analyze the outage probability of the multiuser multiple-input single-output (MISO) down-link system by combining the non-orthogonal multiple access (NOMA) scheme. We derive tractable closed-form outage expressions given a minimum target rate for the individual users for the case of two antennas, by modeling cumulative distribution function (CDF) of received signal-to interference plus noise ratio (SINR). Simulation results illustrate the outage performance for different power allocation scenarios and verify the accuracy of our outage probability analysis
On Optimal Beamforming Design for Downlink MISO NOMA Systems
This work focuses on the beamforming design for downlink multiple-input single-output (MISO) nonorthogonal multiple access (NOMA) systems. The beamforming vectors are designed by solving a total transmission power minimization (TPM) problem with quality-of-service (QoS) constraints. In order to solve the proposed nonconvex optimization problem, we provide an efficient method using semidefinite relaxation. Moreover, for the first time, we characterize the optimal beam- forming in a closed form with quasi-degradation condition, which is proven to achieve the same performance as dirty- paper coding (DPC). For the special case with two users, we further show that the original nonconvex TPM problem can be equivalently transferred into a convex optimization problem and easily solved via standard optimization tools. In addition, the optimal beamforming is also characterized in a closed form and we show that it achieves the same performance as the DPC. In the simulation, we show that our proposed optimal NOMA beamforming outperforms OMA schemes and can even achieve the same performance as DPC. Our solutions dramatically simplifies the problem of beamforming design in the downlink MISO NOMA systems and improve the system performance