A New Design Paradigm for Secure Full-Duplex Multiuser Systems

Abstract

We consider a full-duplex (FD) multiuser system where an FD base station (BS) is designed to simultaneously serve both downlink (DL) and uplink (UL) users in the presence of half-duplex eavesdroppers (Eves). The problem is to maximize the minimum (max-min) secrecy rate (SR) among all legitimate users, where the information signals at the FD-BS are accompanied with artificial noise to debilitate the Eves' channels. To enhance the max-min SR, a major part of the power budget should be allocated to serve the users with poor channel qualities, such as those far from the FD-BS, undermining the SR for other users, and thus compromising the SR per-user. In addition, the main obstacle in designing an FD system is due to the selfinterference (SI) and co-channel interference (CCI) among users. We therefore propose an alternative solution, where the FD-BS uses a fraction of the time block to serve near DL users and far UL users, and the remaining fractional time to serve other users. The proposed scheme mitigates the harmful effects of SI, CCI and multiuser interference, and provides system robustness. The SR optimization problem has a highly nonconcave and nonsmooth objective, subject to nonconvex constraints. For the case of perfect channel state information (CSI), we develop a low-complexity path-following algorithm, which involves only a simple convex program of moderate dimension at each iteration. We show that our path-following algorithm guarantees convergence at least to a local optimum. Then, we extend the path-following algorithm to the cases of partially known Eves' CSI, where only statistics of CSI for the Eves are known, and worst-case scenario in which Eves can employ a more advanced linear decoder. The merit of our proposed approach is further demonstrated by extensive numerical results.

Joint Power Control and User Association for NOMA-Based Full-Duplex Systems

“…From the discussions above, the successive convex program to solve (12) at iteration κ + 1 is given as (16), (18), (19), (23), (24), (27), (31),…”

“…Here, PL BS,U and PL ℓk represent the path loss (in dB), as given in Table II, with d BS,U and d ℓk being the distances (in km) between BS and user U and from U u ℓ to U d ik , respectively.ĥ andĝ ℓk are the small-scale fading and distributed as CN (0, I). The entries of the SI channel G SI are modeled as independent and identically distributed Rician random variables, with the Rician factor of 5 dB [19]. Unless specifically stated otherwise, the other parameters are set as given in Table II, following the studies in [12], [14], [40], [41].…”

“…Based on Definition 1, it is realized that each DL user in a certain zone is assigned to exactly one cluster, and two arbitrary DL users in each cluster come from two different zones. Therefore, the matrix C i , i ∈ Z, satisfies the following conditions: Due to the concavity of function √ uv, we make use of the following inequality [19], [34]:…”

“…Besides SI, such FD systems also suffer from multiuser interference (MUI) and co-channel interference (CCI, caused by a UL user to DL users). Recently, the coexistence of NOMA and FD has been analytically and numerically investigated to effectively handle the network interference, which helps boost the performance of FD systems [19], [22]- [24].…”

Optimal User Pairing for Achieving Rate Fairness in Downlink NOMA Networks

“…Moreover, due to the fact that the IA method is employed, Algorithm 1 converges to a stationary point, which also satisfies the Karush-Kuhn-Tucker (KKT) conditions of (6). The detailed proof can be done by following the same steps in [8], [10]. Complexity analysis: Since problem (13) has x = (2(M + N ) + 3M N + 1) quadratic and linear constraints and y = L(M +N )+2M N +1 optimization variables , the per-iteration complexity of solving (13) is O x 2.5 (y 2 +x) [11].…”