Adaptive Constellation Multiple Access for Beyond 5G Wireless Systems

TL;DR

ACMA addresses limitations of conventional NOMA by designing a power/modulation/phase-agnostic constellations and optimizing per-user phase offsets at the base station to maximize the minimum Euclidean distance $d^2_{min}$. The system uses a scalable algorithm for phase offset selection and deploys an MML receiver that blind-detects offsets via a shared control channel, enabling flexible multi-user support beyond two users and arbitrary modulations. Results show ACMA offers notable SER and throughput gains over PD-NOMA and JD-NOMA, especially when users share similar powers, and demonstrates robustness with receive diversity; the framework remains computationally tractable for practical $K$. The work suggests ACMA as a viable path toward higher-capacity beyond-5G systems with improved interference mitigation and signaling efficiency, with future directions including MIMO extensions and reduced feedback overheads.

Abstract

We propose a novel nonorthogonal multiple access (NOMA) scheme referred as adaptive constellation multiple access (ACMA) which addresses key limitations of existing NOMA schemes for beyond 5G wireless systems. Unlike the latter, that are often constrained in choices of allocation of power, modulations and phases to allow enough separation of clusters from users combined signals, ACMA is power, modulation and phase agnostic forming unified constellations instead where distances of all possible neighbouring points are optimized. It includes an algorithm at basestation (BS) calculating phase offsets for users signals such that, when combined, it gives best minimum Euclidean distance of points from all possibilities. The BS adaptively changes the phase offsets whenever system parameters change. We also propose an enhanced receiver using a modified maximum likelihood (MML) method that dynamically exploits information from the BS to blindly estimate correct phase offsets and exploit them to enhance data rate and error performances. Superiority of this scheme, which may also be referred to as AC NOMA, is verified through extensive analyses and simulations.

Figures (6)

• Figure 1: Constellation of a) proposed ACMA and b) JD-NOMA IEEEguan:phjd, IEEEchang:phsic clusters for a two-user system using 16-QAM and 4-QAM and power allocation factors of $[\alpha_{1},\alpha_{2}]=[0.35,0.65]$. Phase offsets of $\delta_{1}=2\pi/3$ by the former and $9.4^{\circ}$ by the latter are generated, leading to different $d^{2}_{min}$.
• Figure 2: Phase rotation $\delta_{1}$ of user U1 for ACMA for $K=2$, different $M_{1}$-QAM$/M_{2}$-QAM configurations, $V=20, Q=20$ under different power sharing values of $\alpha_{1}$ and $\alpha_{2}$. Rotation values from JD-NOMA IEEEchang:phsic are also included for reference.
• Figure 3: SER of users U1 and U2 for ACMA compared with JD-NOMA, PD-NOMA for $K=2$, $\abs{h_{1}}^{2}/\abs{h_{2}}^{2}=0$ dB, $M_{1}=16$, $M_{2}=4$ and for $[\alpha_{1},\alpha_{2}]=$ a) $[0.1,0.9]$, b) $[0.35, 0.65]$.
• Figure 4: SER of U1 and U2 for ACMA compared with JD-NOMA, PD-NOMA for $K=2$, $M_{1}=16$, $M_{2}=16$, $\abs{h_{1}}^{2}/\abs{h_{2}}^{2}=0$ dB, $[\alpha_{1}, \alpha_{2}]=$ a) $[0.75,0.25]$ and b) $[0.5,0.5]$. Two-antenna receive diversity (Solid lines).
• Figure 5: SER of ACMA for U1 and U2 compared with JD-NOMA and PD-NOMA under $\abs{h_{1}}^{2}/\abs{h_{2}}^{2}=0$ dB for a) $M_{1}=16, M_{2}=4, P_{t}/N_{0}=40$ dB, and b) $M_{1}=16, M_{2}=16, P_{t}/N_{0}=50$ dB, for different $\alpha_{1}$ values in the range $[0.05-0.5]$ where $\alpha_{2}=1-\alpha_{1}$.