A generalized downlink multi-antenna non-orthogonal multiple access (NOMA) transmission framework is proposed with the novel concept of cluster-free successive interference cancellation (SIC). In contrast to conventional NOMA approaches, where SIC is successively carried out within the same cluster, the key idea is that the SIC can be flexibly implemented between any arbitrary users to achieve efficient interference elimination. Based on the proposed framework, a sum rate maximization problem is formulated for jointly optimizing the transmit beamforming and the SIC operations between users, subject to the SIC decoding conditions and users' minimal data rate requirements. To tackle this highly-coupled mixed-integer nonlinear programming problem, an alternating direction method of multipliers-successive convex approximation (ADMM-SCA) algorithm is developed. The original problem is first reformulated into a tractable biconvex augmented Lagrangian (AL) problem by handling the non-convex terms via SCA. Then, this AL problem is decomposed into two subproblems that are iteratively solved by the ADMM to obtain the stationary solution. Moreover, to reduce the computational complexity and alleviate the parameter initialization sensitivity of ADMM-SCA, a Matching-SCA algorithm is proposed. The intractable binary SIC operations are solved through an extended many-to-many matching, which is jointly combined with an SCA process to optimize the transmit beamforming. The proposed Matching-SCA can converge to an enhanced exchange-stable matching that guarantees the local optimality. Numerical results demonstrate that: i) the proposed Matching-SCA algorithm achieves comparable performance and a faster convergence compared to ADMM-SCA; ii) the proposed generalized framework realizes scenario-adaptive communications and outperforms traditional multi-antenna NOMA approaches in various communication regimes.
翻译:与常规的诺马方法不同,即SIC在同一组内相继实施,关键的想法是,在任意用户之间可以灵活实施SIC,以有效消除干扰。根据拟议框架,将最高比率最大化问题放在共同优化用户之间的传输波束成型和 SIC 操作上,但须遵守SIC 解码条件和用户最低数据率要求。要解决这种高度混合的混合内联非线性内联式内联性非线性编程(SIC )方法,一个常规的NOMA 方法则不同。最初的问题首先被重新定位为可感动的双向内联式内联式内联式内联(ADMM-SC ),通过 SAC 处理拟议的非convex 条件,使Lagrangian (AL) 问题。然后,AL 问题被分解为两个小问题,由ADMM 进行反复的混合化混合内联式内联式内联式内联式内混合的混合内联式内联式内(MIC- mal- mal-modal-modal-modal-modal-modal-modal-mod-mod-modal-modal-modal-modal-modal-mod-mod-mod-modal-modal-mod-mod-modal-modal-modal-modal-modal-modal-modal-modal-modal-modal-modal-modal-modal-mod-mod-mod-mod-mod-mod-mod-modal-mod-mod-mod-mod-modal-mod-mod-mod-mod-mod-mod-modal-modal-modal-modal-modal-modal-modal-mod-mod-mod-mod-modal-mod-mod-mod-mod-mod-mod-mod-modal-mod-mod-mod-mod-mod-mod-mod