Decode-Forward Cooperative NOMA

• Decode-Forward Cooperative NOMA is a multiuser framework that integrates non-orthogonal multiple access with decode-and-forward relaying to enhance spectral and energy efficiencies.
• It employs superposition coding and successive interference cancellation, enabling strong nodes to forward information to weaker users for improved reliability.
• Performance analyses highlight improvements in achievable rate, outage probability, and secrecy, with applications in multi-cell, UAV, and hybrid VLC/RF networks.

Decode-Forward Cooperative NOMA (DF-Cooperative NOMA) defines a multiuser transmission framework integrating non-orthogonal multiple access (NOMA) with decode-and-forward (DF) relaying protocols, thereby harnessing spatial diversity, spectrally-efficient multiplexing, and cooperative communication. The essential attribute of DF-Cooperative NOMA is that nodes with favorable channels (e.g., relays, near users) opportunistically forward decodable information to users with weaker channels, while successive interference cancellation (SIC) separates superimposed information flows at receivers. This architecture underpins broad performance enhancements for spectral efficiency, user fairness, and secrecy, especially in environments exhibiting path-loss, fading heterogeneity, or practical energy/information coexistence requirements.

1. Core Protocol Structures and Channel Models

DF-Cooperative NOMA systems generally consist of three principal node types: a source (S), at least one decode-and-forward relay (R) or a strong user acting as a relay, and one or more destination (weak) users (D). The canonical link setup employs two distinct transmission phases:

• Phase I: S broadcasts a (possibly superposed) signal to both R (and sometimes D(s)) with power-domain NOMA superposition.
• Phase II: R forwards the decoded signal(s) to D; S may simultaneously transmit new data—enabling full usage of both time slots, as seen in CRS-NOMA (Jha et al., 2017), or only forward prior information, as in conventional DF-NOMA (So et al., 2015).

The relay and user links often undergo independent fading (Rician (Jha et al., 2017), Rayleigh (Kader et al., 2018), Nakagami-m (Kara et al., 2020), or deterministic in VLC/RF hybrid systems (Obeed et al., 2020)). The mathematical abstractions for instantaneous SNRs and fading coefficients are fundamental to all analytical derivations—see, for example, the Rician models for S–R, S–D, R–D links in CRS-NOMA (Jha et al., 2017), or the Nakagami-m power-gain PDFs in (Kara et al., 2020).

2. Signal Processing: Superposition Coding and Successive Interference Cancellation

NOMA’s spectrum-sharing capability stems from the superposition of distinct user symbols on the same resource block, using either explicit power allocation (α, β, etc.) or, in some cases, full-power transmissions without dedicated coefficients (Jha et al., 2017). At reception, SIC is applied in an order dictated by the power-domain hierarchy or channel state:

• At the relay: R decodes the higher-power symbol (typically intended for far users), cancels it, and then decodes the lower-power symbol. Reversal of this order, i.e., R-DFNOMA, is used for fairness improvement (Kara et al., 2020).
• At users: Near users first decode the far user's symbol (SIC), then subtract and decode their own; far users decode their symbol directly while treating others as interference (So et al., 2015, Kara et al., 2020).

In practical settings, imperfect SIC is modeled via a residual interference parameter Ξ, which directly degrades rates, increases outage probability, and affects fairness (Kara et al., 2020, Kader et al., 2018). Analytical expressions for SINR and capacity incorporate these nonidealities.

3. Achievable Rate, Outage, and Ergodic Performance Metrics

The achievable rates in DF-Cooperative NOMA are determined by min-max formulations along the weak links and SIC-limited decoding chains. For instance, in CRS-NOMA (Jha et al., 2017), the total sum-rate is

$$R_{tot} = \frac{1}{2} \min \left\{ \log_2(1+\gamma_{SR}), \log_2(1+\gamma_{RD}) \right\} + \log_2(1+\rho|h_{SD}|^2)$$

reflecting both the relay and direct channels. In dual-hop NOMA (Kader et al., 2018), the joint exploitation of downlink and uplink NOMA through two-hop relaying allows transmission of three symbols over two phases, raising sum-capacity compared to CRS-NOMA.

Outage probability (OP) and diversity analysis provide characterizations under reliability or finite-blocklength constraints (Salehi et al., 2021). In high SNR regimes, standard CRS-NOMA approaches show zero diversity for some users due to SIC-induced error floors unless dynamic power allocation (DPA) is used (Lei et al., 2018).

4. Fairness, Power Allocation, and User Pairing

Controlling inter-user performance disparities is central, especially with imperfect SIC and fading imbalance. DFs with reversed power allocation (R-DFNOMA) in the first hop (i.e., allocating more power to the user destined for the stronger subchannel) and classical NOMA allocation in the relay phase have been proven to substantially improve fairness metrics for ergodic rate, outage, and bit-error probability—all being driven closer to unity in the fairness index (Kara et al., 2020).

Joint optimization across power allocation (α, β), blocklength (in finite-blocklength scenarios), and user pairing yields optimally balanced throughput, even in strong relay environments and multistage multi-cell settings (Salehi et al., 2021, Obeed et al., 2020). DF-Cooperative NOMA architectures in visible light communications with hybrid VLC/RF relay links explicitly combine user, link, and power assignment via mixed-integer nonconvex optimization to maintain fairness while leveraging relaying diversity (Obeed et al., 2020).

5. Secrecy, Reliability, and Energy Harvesting

DF-Cooperative NOMA frameworks accommodate security considerations by enabling the strong user or a selected relay to forward messages in the presence of an eavesdropper. Analytical expressions for secrecy outage probability (SOP) are derived under various relay selection schemes (OSRS, TSRS, ODRS), showing that jamming via non-forwarding relays and dynamic power allocation can restore positive secrecy diversity order (Lei et al., 2018). In energy-harvesting scenarios, the near user acting as a DF relay can use power-splitting strategies for simultaneous information and energy transfer, with deep learning-based optimization of power allocation coefficients shown to approach iteratively optimal secrecy rates at a fraction of the computational cost (Jameel et al., 2019).

6. Extensions: Uplink, Multicell, and UAV Scenarios

The DF-Cooperative NOMA paradigm generalizes to uplink and multi-cell macro-diversity. In cellular-connected UAV systems, cooperative NOMA across BSs uses backhaul links to decode and forward UAV signals, enabling interference mitigation and improving system-wide sum-rates far beyond both OMA and noncooperative NOMA benchmarks (Mei et al., 2018). The same principles underpin power and link selection in hybrid VLC/RF networks, where relaying over RF can bypass severe VLC cell-edge interference (Obeed et al., 2020).

7. Performance Insights, Limitations, and Comparative Evaluation

DF-Cooperative NOMA consistently outperforms both conventional NOMA (no relay) and OMA in achievable rate, diversity, fairness, and, under appropriate power allocation, secrecy. The spectral and energy efficiencies are maximized when both transmission phases are fully exploited (e.g., by transmitting fresh data in both slots) (Jha et al., 2017, Kader et al., 2018). Performance advantages are most pronounced at moderate SNR and for network configurations featuring significant channel heterogeneity; at very high SNR, the relative gain narrows due to saturation effects.

A marginal sum-rate gain is seen for simple DF relaying over pure BC/SIC NOMA; compress-forward and dirty paper coding further enhance these gains in relaying broadcast channels, especially as user/channel asymmetry increases (So et al., 2015). Dynamic relay selection and jamming protocols offer significant secrecy outage improvement but require precise CSI and additional coordination overhead (Lei et al., 2018).

In summary, DF-Cooperative NOMA architectures provide a robust and extensible framework for multiuser next-generation networks requiring high spectral efficiency, fairness under practical nonidealities, and secure, energy-sustainable communications (Jha et al., 2017, Kader et al., 2018, Kara et al., 2020, Kara et al., 2020, Lei et al., 2018, So et al., 2015, Obeed et al., 2020, Salehi et al., 2021, Jameel et al., 2019, Mei et al., 2018).