Resource-Assisted Communication

• Resource-assisted communication is a paradigm that leverages auxiliary resources, such as IRS and artificial noise, integrated with optimized beamforming to enhance wireless security and efficiency.
• The paper highlights a joint resource allocation strategy using non-convex methods like alternating optimization, SCA/SDR, and manifold optimization to maximize the secrecy rate.
• Empirical findings indicate that strategic IRS placement and scaling of system parameters significantly improve secrecy rates compared to traditional setups.

Resource-assisted communication encompasses a broad class of techniques and architectures in which auxiliary physical entities, environmental reconfigurations, or operational manipulations actively enhance the information transfer capabilities of a communication system. In contemporary wireless networks, this is typified by the use of intelligent reflecting surfaces (IRS) or other programmable metasurfaces, but the paradigm extends to scenarios leveraging external devices, artificial noise, or even quantum resource assistance. The essential principle is that by jointly optimizing these external or auxiliary resources in tandem with the traditional transceiver variables (e.g., beamforming), one can achieve improved metrics—such as secrecy rate, spectral efficiency, energy minimization, or robustness—relative to resource-unassisted designs. The following sections delineate the fundamental principles, resource allocation strategies, algorithmic architectures, physical layer security enhancements, and empirical results, with particular technical detail focused on secure IRS-assisted multiuser MISO systems (Xu et al., 2019).

1. Intelligent Reflecting Surfaces as Resource Enablers

Intelligent Reflecting Surfaces (IRSs) function as dense planar arrays of nearly-passive, electronically steerable reflecting elements. Each element modulates the phase (and in some extensions, amplitude) of impinging electromagnetic waves, effectively synthesizing a reconfigurable multipath environment. In the multiuser MISO context, the IRS is parameterized via a phase shift matrix: $$\boldsymbol{\Phi} = \operatorname{diag}(e^{j\phi_1}, e^{j\phi_2}, \ldots, e^{j\phi_M})$$ with the unit modulus constraint $|\phi_m| = 1$. The phase configuration is adaptively optimized, enabling the IRS to reinforce the intended signals at the legitimate users while weakening (or nulling) the signal at eavesdroppers or unwanted receivers.

This IRS-induced channel reconfiguration fundamentally alters the spatial structure of the cascade between BS and users/eavesdroppers. The resultant effective channel, e.g., for user $k$, is

$\mathbf{g}_k^H\,\boldsymbol{\Phi}\,\mathbf{H}$

where $\mathbf{H}$ is the BS-IRS channel and $\mathbf{g}_k$ is the IRS-user cascaded channel.

2. Joint Resource Allocation for Secure Transmission

Resource-assisted secure communication mandates the joint allocation of:

• BS transmit beamforming vectors $\mathbf{w}_k$ for all users $k$,
• The artificial noise (AN) covariance matrix $\mathbf{Z}$,
• The IRS phase shift vector $\mathbf{u} = [e^{j\phi_1},\dotsc,e^{j\phi_M}]^T$ (with $|u_m|=1$).

The received signal at user $k$ is thus

$$y_k = \mathbf{g}_k^H\,\boldsymbol{\Phi}\,\mathbf{H} \left(\sum_{r\in\mathcal{K}} \mathbf{w}_r s_r + \mathbf{z}\right) + n_k$$

where $s_r$ are information-bearing symbols and $\mathbf{z}\sim\mathcal{CN}(0,\mathbf{Z})$ is the artificial noise vector.

The resource allocation problem is formulated to maximize the sum secrecy rate: $$\max_{\{\mathbf{w}_k, \mathbf{Z}, \mathbf{u}\}} \sum_k [R_k - C_k^E]^+$$ where $R_k$ is the achievable rate for user $k$ and $C_k^E$ is the eavesdropper’s capacity (under worst-case interference cancellation assumptions), all subject to:

• Total BS transmit power constraint: $$\sum_k \operatorname{Tr}(\mathbf{W}_k) + \operatorname{Tr}(\mathbf{Z}) \leq P_{\max}$$,
• Unit-modulus constraints on IRS: $|u_m|=1,\;\forall m$,
• $\mathbf{W}_k \succeq 0,\; \mathbf{Z} \succeq 0$.

The SINR for each user $k$ is

$$\Gamma_k = \frac{ \operatorname{Tr}( \mathbf{W}_k \mathbf{G}_k^H \mathbf{u}\mathbf{u}^H \mathbf{G}_k ) }{ \sum_{r \ne k} \operatorname{Tr}( \mathbf{W}_r \mathbf{G}_k^H \mathbf{u}\mathbf{u}^H \mathbf{G}_k ) + \operatorname{Tr}(\mathbf{Z} \mathbf{G}_k^H \mathbf{u}\mathbf{u}^H \mathbf{G}_k ) + \sigma_{n_k}^2 }$$

3. Artificial Noise Injection and Secrecy Rate Enhancement

A central feature for physical-layer security is the BS-driven artificial noise (AN) vector $\mathbf{z}$. This AN is spatially and power-wise shaped such that its impact degrades the eavesdropper channel more severely than that of legitimate users. For the eavesdropper channel, assuming the eavesdropper is able to cancel all multi-user interference optimally, the eavesdropper’s SINR for user $k$ (capacity $C_k^E$) is

$$C_k^E = \log_2 \left(1 + \frac{ \operatorname{Tr}( \mathbf{W}_k \mathbf{L}^H \mathbf{u}\mathbf{u}^H \mathbf{L} ) }{ \operatorname{Tr}(\mathbf{Z} \mathbf{L}^H \mathbf{u}\mathbf{u}^H \mathbf{L}) + \sigma_{n_e}^2 } \right)$$

This joint optimization ensures that the secrecy rate $R_k^{(\text{Sec})} = [R_k - C_k^E]^+$ is maximized, with AN actively hampering eavesdropper decoding while maintaining user SINR requirements.

4. Multi-Stage Non-Convex Optimization Algorithms

Multiple advanced optimization techniques address the problem’s non-convexity:

1. Alternating Optimization: Variables $\{\mathbf{W}_k, \mathbf{Z}\}$ and $\mathbf{u}$ are updated in an alternating manner. At each step, a block of variables is optimized while the other block(s) are kept fixed.
2. Successive Convex Approximation (SCA) and Semidefinite Relaxation (SDR):
   • When $\mathbf{u}$ is fixed, the problem over $\{\mathbf{W}_k, \mathbf{Z}\}$ is reformulated, relaxing the rank-1 constraints (SDR). The SCA method linearizes concave (non-convex) parts of the objective:

   $$G_1(\mathbf{W}, \mathbf{Z}) \geq G_1(\mathbf{W}^i, \mathbf{Z}^i) + \mathrm{Tr}((\nabla_\mathbf{W} G_1(\mathbf{W}^i, \mathbf{Z}^i))^H (\mathbf{W}-\mathbf{W}^i)) + \dotsc$$

• The SDR relaxation is proved to be tight for this setup.

1. Manifold Optimization:
   • For a fixed $\{\mathbf{W}_k, \mathbf{Z}\}$, the optimization over $\mathbf{u}$ is conducted over the oblique manifold (unit-modulus constraints). The Riemannian gradient is projected onto the tangent space, followed by a conjugate gradient update step, retraction, and vector transport (see equations (obmupdate), (retraction)). This maintains feasibility with respect to the original non-convex unit modulus constraint, avoiding the need for further relaxation or penalty methods.

Algorithmic pseudocode is provided in the paper for each of these subproblems, and the convergence of the overall block coordinate procedure is established.

5. Critical Empirical Findings and Design Insights

A synthesis of the simulation results reveals:

• Secrecy rate scaling: System sum secrecy rate improves as BS transmit power $P_{\max}$ increases, but the gap with non-IRS or random-phase IRS benchmarks remains pronounced.
• User scaling: Increasing the number of users enhances system secrecy performance due to multiuser diversity.
• IRS deployment scaling: With increasing IRS element count $M$ and BS antenna count $N_T$, the achievable secrecy rate correspondingly increases; the flexibility and focusing capability of the IRS magnifies as $M$ grows.
• Security-critical geometry: IRS effectiveness is strongly dictated by its placement relative to both users and eavesdroppers. Shorter IRS-to-user and longer IRS-to-eavesdropper distances yield maximal gain.

A table summarizing key scaling effects:

Parameter	Effect on Secrecy Rate	Note
$P_{\max}$	Increases	Diminishing returns at high power
$K$ (users)	Increases (multiuser diversity)	More resource allocation freedom
$M$ (IRS elems.)	Increases (steeper with optimized IRS)	Outperforms random-phase or AN-off baselines
Placement	Highly sensitive; optimal IRS–user proximity	Surveys must precede deployment

6. System-Level Implications and Broader Context

The results demonstrate that combining programmable environmental control (IRS), advanced spatial signal design (beamforming), and artificial noise can drastically improve security without requiring extra spectrum or brute-force power increases. Resource-assisted approaches thus extend the viable envelope for high-security, high-efficiency wireless networking. Notably:

• The modular IRS architecture enables scalable security enhancement adaptable to network densification.
• Trade-offs arise between IRS hardware expense, phase quantization constraints, and achievable secrecy rates.
• The multi-stage algorithmic framework (alternating optimization, SCA/SDR, manifold optimization) is extensible to more general system settings (e.g., multi-IRS, imperfect CSI, multi-cell contexts).

7. Conclusion

Secure resource-assisted communication in IRS-assisted multiuser MISO systems is achieved by exploiting the combined degrees of freedom provided by IRS phase tuning, BS beamforming, and artificial noise shaping. The joint optimization of these resources translates into significant gains in system sum secrecy rate compared to IRS-unaware or AN-free baselines. The core algorithmic paradigm—alternating block optimization combining convex relaxations (SCA/SDR) and geometric (manifold) methods—enables efficient convergence to suboptimal yet high-performance solutions. Empirical evidence underscores the importance of optimized resource allocation and careful IRS deployment for realizing the full benefits of this architecture in next-generation wireless systems (Xu et al., 2019).