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This paper investigates the security enhancement of an intelligent reflecting surface (IRS) assisted non-orthogonal multiple access (NOMA) network, where a distributed IRS enabled NOMA transmission framework is proposed to serve users securely in the presence of a passive eavesdropper. Considering that eavesdroppers instantaneous channel state information (CSI) is challenging to acquire in practice, we utilize secrecy outage probability (SOP) as the security metric. A problem of maximizing the minimum secrecy rate among users, subject to the successive interference cancellation (SIC) decoding constraints and SOP constraints, by jointly optimizing transmit beamforming at the BS and phase shifts of IRSs, is formulated. For special case with a single-antenna BS, we derive the exact closed-form SOP expressions and propose a novel ring-penalty based successive convex approximation (SCA) algorithm to design power allocation and phase shifts jointly. While for the more general and challenging case with a multi-antenna BS, we adopt the Bernstein-type inequality to approximate the SOP constraints by a deterministic convex form. To proceed, an efficient alternating optimization (AO) algorithm is developed to solve the considered problem. Numerical results validate the advantages of the proposed algorithms over the baseline schemes. Particularly, two interesting phenomena on distributed IRS deployment are revealed: 1) the secrecy rate peak is achieved only when distributed IRSs share the reflecting elements equally; and 2) the distributed IRS deployment does not always outperform the centralized IRS deployment, due to the tradeoff between the number of IRSs and the reflecting elements equipped at each IRS.
Intelligent reflecting surface (IRS) enhanced multi-unmanned aerial vehicle (UAV) non-orthogonal multiple access (NOMA) networks are investigated. A new transmission framework is proposed, where multiple UAV-mounted base stations employ NOMA to serve
This paper investigates the model aggregation process in an over-the-air federated learning (AirFL) system, where an intelligent reflecting surface (IRS) is deployed to assist the transmission from users to the base station (BS). With the purpose of
This paper proposes a novel framework of resource allocation in intelligent reflecting surface (IRS) aided multi-cell non-orthogonal multiple access (NOMA) networks, where a sum-rate maximization problem is formulated. To address this challenging mix
Intelligent reflecting surface (IRS) is a promising new paradigm to achieve high spectral and energy efficiency for future wireless networks by reconfiguring the wireless signal propagation via passive reflection. To reap the potential gains of IRS,
In this paper, the adoption of an intelligent reflecting surface (IRS) for multiple single-antenna source terminal (ST)-DT pairs in two-hop networks is investigated. Different from the previous studies on IRS that merely focused on tuning the reflect