Secure Multiuser Transmission Framework

• Secure Multiuser Transmission Framework is a strategy that integrates joint beamforming, precoder design, and artificial noise injection to protect multiuser MIMO/MISO channels from eavesdropping.
• It employs optimization algorithms like SDP, SCA, and interference alignment to balance secrecy rates with system performance under imperfect CSI and hardware limitations.
• The framework enables scalable, practical deployments in IRS-assisted, SWIPT, and cooperative networks, ensuring robust throughput and confidentiality.

A secure multiuser transmission framework comprises coordinated methodologies for achieving information-theoretic or computational security against eavesdroppers in wireless systems involving multiple legitimate users. Such frameworks are characterized by joint signal processing, beamforming, coding, and resource allocation architectures tailored for the multiuser MIMO/MISO setting, often under practical constraints like imperfect CSI, channel variability, hardware impairment, or multi-node cooperation. The integration of secrecy measures at the physical layer is achieved via a combination of precoder design, artificial noise, structured coding, network cooperation, and optimization-theoretic approaches. Below, the major technical pillars and operational dimensions of secure multiuser transmission frameworks are synthesized from the literature.

1. System Architectures and Channel Models

Physical-layer secure multiuser transmission models typically consist of a BS with multiple transmit antennas ($M$), $K$ single-antenna legitimate users, and $J$ single-antenna eavesdroppers (Asaad et al., 2019). Downlink channels $H\in\mathbb{C}^{\,K\times M}$ and eavesdropper channels $G\in\mathbb{C}^{\,J\times M}$ are assumed to follow i.i.d. Rayleigh fading, with perfect CSI available at the BS under TDD reciprocity. The framework generalizes to MISO, MIMO, and ER-based (energy receivers as potential Eves) SWIPT systems, UAV networks, IRS/RIS-assisted topologies, and frameworks with hardware impairments or movable antennas (Ng et al., 2013, Peng et al., 2022, Cheng et al., 9 Jan 2026, Ng et al., 2014).

2. Precoding, Artificial Noise, and Leakage Suppression

Linear Precoding Techniques

Regularized zero-forcing (RZF) is the classical linear precoding approach in multiuser MIMO. Secure RZF (SRZF) modifies RZF by incorporating an explicit leakage-suppression penalty to minimize information leakage to eavesdroppers (Asaad et al., 2019): $$W_{\lambda,\theta} = \arg\min_W \|HW - I_K\|_F^2 + \lambda\|W\|_F^2 + \theta\|GW\|_F^2$$ Closed-form precoders are derived, where $\lambda$ tunes noise/interference robustness and $\theta$ controls secrecy-leakage suppression. SRZF guarantees nonzero secrecy rates even when eavesdropper SNR becomes large.

Artificial Noise Injection

Artificial noise (AN) is injected into beams to jam eavesdropper channels while minimally affecting legitimate users. Joint optimization of beamforming vectors and AN covariance matrices is central in IRS/RIS systems (Xu et al., 2019), layered video frameworks (Ng et al., 2014, Ng et al., 2013), and SWIPT models (Ng et al., 2014). AN is often projected onto the null-space of legitimate channels to maximize its disruptive effect on eavesdroppers.

Interference Alignment for Security

Interference alignment (IA) is leveraged to pack eavesdropper signal leakage into low-dimensional subspaces. Secure IA techniques minimize both total inter-main-link interference and wiretapped signals using alternated minimization over transmit and receive subspace matrices (Vu et al., 2015, Ha et al., 2015). Zero-forcing wiretapped signal (ZFWS) designs force eavesdropper received signals to zero when system configurations permit.

3. Coding-Based and Cooperative Approaches

Secure multiuser coding schemes utilize random linear network coding with private decoding coefficients to ensure each client only obtains its own message (Tajbakhsh et al., 2013). Security is achieved both computationally (brute-force hardness) and at a weak information-theoretic level (intercepted packets provide negligible information about unintended messages). Recovery of lost packets is managed either centrally via base station retransmissions (using instantly-decodable network coding, IDNC) or cooperatively among clients via clustering and local IDNC graph algorithms.

Decentralized and cooperative resource allocation frameworks, such as in UAV networks, employ matching games for transmitter-receiver association and overlapping coalition formation games for cooperative null-steering transmission, achieving physical-layer secrecy at scale (Xu et al., 2020).

4. Large-System and Statistical Analyses

Frameworks often exploit large-system analysis via random matrix theory to derive deterministic equivalents for signal-to-interference-plus-noise ratios (SINRs), secrecy rates, and to guide parameter optimization (Asaad et al., 2019). For example, in the SRZF architecture,

$\mathsf{SINR}_{l}^{\infty} = \frac{\mu_l x^2}{(1+x)^2[\alpha_l x' \mu_l + \beta]}, \quad \mathsf{SINR}_{e}^{\infty} = \frac{\mu_o \alpha_o \alpha_l(1+\theta x^2)}$

and the ergodic secrecy rate is $$R_s = [\log_2(1 + \mathsf{SINR}_l^{\infty}) - \log_2(1 + \mathsf{SINR}_e^{\infty})]^+$$.

Statistical CSI-only designs are leveraged when instantaneous eavesdropper channels are unknown, exploiting channel second-order statistics and optimizing power fractions for AN and IRS/RIS phase shifts using concave optimization and gradient ascent (Yang et al., 2023).

5. Optimization Algorithms and Solution Methods

Semidefinite Programming (SDP) and Relaxations

Non-convex secrecy resource allocation problems, often formulated as quadratically-constrained quadratic programs (QCQPs), are relaxed via SDP (Ng et al., 2014, Ng et al., 2013, Ng et al., 2014). Sufficient rank-one conditions ensure that relaxed SDP solutions yield globally optimal beamformers, recoverable via principal eigenvector extraction.

Successive convex approximation (SCA), block coordinate descent (BCD), and fractional programming transforms are prevalent in secure beamforming, RIS-assisted transmission, and movable antenna array frameworks (Xu et al., 2019, Cheng et al., 9 Jan 2026). Alternating optimization methods efficiently solve coupled subproblems in beamformers and IRS phase design under unit-modulus constraints.

Complexity and Performance Trade-offs

Algorithms are tailored for practical deployment via closed-form updates, alternating steps with low-complexity one-dimensional grid searches (as in movable antenna arrays (Cheng et al., 9 Jan 2026)), or via fast MM-algorithms in RIS settings with hardware impairments (Peng et al., 2022).

Complexity scales with antenna count, user number, and cooperative dimensions. For instance, MM-based RIS designs offer orders-of-magnitude complexity reduction over SOCP-based counterparts.

6. Framework Synthesis, Operational Recipes, and Practical Results

A general synthesis for secure multiuser transmission comprises:

1. Acquisition of CSI for all legitimate and (if available) eavesdropper channels.
2. Specification of design objectives (sum/max-min secrecy, fairness).
3. Formulation and closed-form solution of the joint cost (e.g., SRZF, IDNC, SDP design).
4. Offline tuning of hyperparameters via large-system/statistical analysis.
5. Real-time deployment with block-wise CSI updates and precomputed optimal settings.

Key trade-offs exist: increasing interference/noise robustness ($\lambda$) reduces beam orthogonality, while increasing leakage suppression ($\theta$) sacrifices constructive gain to users. Artificial noise balances secrecy with RF energy harvesting in SWIPT models.

Empirical simulations and theoretical analysis consistently demonstrate that secure multiuser frameworks with optimized precoding, AN, cooperative recovery, and structured coding outperform conventional nonsecure schemes, maintain secrecy against capable eavesdroppers, and provide tangible power/throughput gains under practical impairments (Asaad et al., 2019, Tajbakhsh et al., 2013, Ng et al., 2014, Cheng et al., 9 Jan 2026, Ng et al., 2014).

7. Extensions, Limitations, and Generalization

Secure multiuser frameworks extend to IRS/RIS-assisted massive MIMO (Yang et al., 2023), cognitive radio systems (Ng et al., 2014), SWIPT (Ng et al., 2014), and quantum multiuser secret-sharing protocols (Andronikos et al., 2023). Limitations include reliance on perfect CSI, linear RF models, and static topologies. Prospective generalizations involve robust designs against channel estimation errors, dynamic cooperative networks, integration of mobility, and customized receiver architectures.

The synthesis of advanced secure multiuser transmission frameworks enables systematic, provable, and scalable physical-layer secrecy solutions for modern large-scale wireless networks, accommodating heterogeneous environments and evolving eavesdropper capabilities.