The emergence of RIS technology has redefined the concept of wireless communication by enabling the control of electromagnetic waves through programmable surfaces, as summarized in Table 1, which dynamically manipulate phase shifts and amplitudes [15,16]. RIS technology is increasingly being integrated into 6G communication systems to meet the demands of high transmission rates, incredibly low latency and ubiquitous connectivity especially for IoT applications [17]. Through RIS, communication systems can reduce interference, improve spectral efficiency, and mitigate fading effects, thereby extending the operational range and robustness of both backscatter communication (BackCom) and direct transmissions [18,19].

Backscatter communication has gained prominence for its energy-efficient data transmission capabilities by using passive reflection of continuous carrier waves. This makes it particularly suitable for IoT devices with constrained energy resources [3]. However, BackCom faces key challenges such as the double-fading effect, which severely impacts the quality of transmission and limits communication range [20,21]. To overcome these limitations, researchers have proposed RIS-aided BackCom systems that enhance transmission efficiency by optimizing signal paths through intelligent reflections [22]. By introducing additional propagation channels, RIS helps mitigate path loss and extend coverage, facilitating seamless communication in dense IoT environments [23,24].

In parallel, the rise of PLS has been motivated by the growing need for lightweight security solutions in IoT and wireless systems. PLS takes advantage of the inherent randomness in wireless channels to secure data transmission without the computational overhead associated with cryptographic methods [25,12]. RIS-assisted PLS frameworks have demonstrated the ability to boost secrecy capacity by tailoring phase shifts to steer reflected signals toward legitimate users while weakening signals received by potential eavesdroppers [13,26]. This has led to the development of RIS-aided wiretap communication models where beamforming strategies are optimized to maximize secrecy rates [27,28]. These models ensure that confidential data remains protected even in highly dynamic network environments.

The adoption of machine learning (ML) algorithms for managing RIS configurations is another significant advancement. Reinforcement learning techniques, such as the DDPG algorithm, have been used to dynamically adjust RIS parameters and optimize beamforming patterns [29,12]. These algorithms are particularly effective in continuous action spaces such as those encountered in RIS systems where precise phase shifts are required to optimize both signal strength and security [30]. DDPG-based frameworks enhance the adaptability of RIS-aided communication systems by enabling real-time optimization in response to changing environmental conditions [31].

Further research has focused on the development of bistatic BackCom systems integrated with RIS. In these systems, the carrier emitter and receiver are spatially separated enabling more flexible network deployments [24]. RIS enhances these setups by introducing controllable propagation paths leading to better signal quality and reduced interference [20]. Resource allocation and beamforming optimization are critical research areas that empower RIS to achieve an effective balance between power distribution and the fulfillment of secrecy constraints [32].

As the number of RIS elements increases in advanced 6G communication systems, the computational complexity of optimization algorithms like DDPG becomes a critical consideration. This paper investigates how the complexity of DDPG scales with the number of RIS elements and demonstrates that the proposed approach remains efficient and scalable, even for large-scale RIS configurations.

Recent studies highlight the effectiveness of Deep Reinforcement Learning (DRL) in optimizing RIS-assisted networks, reference [33] proposed a HAP-based integrated satellite-aerial-terrestrial relay network using a DRL-based Long Short-Term Memory - Double Deep Q-Network (LSTM-DDQN) framework to maximize ergodic rate under Unmanned Aerial Vehicle (UAV) energy constraints. Similarly, reference [34] explored multi-RIS-assisted satellite-UAV-terrestrial networks, addressing dynamic environments and spectrum scarcity with a multi-objective deep deterministic policy gradient (MO-DDPG) algorithm to optimize achievable rate and energy efficiency. These works demonstrate DRL’s potential for efficient RIS-enabled integrated networks.

As the role of RIS expands in the realm of 6G networks, several works have established new benchmarks for secrecy rate, spectral efficiency, and power management by integrating RIS, PLS, and advanced ML techniques. This study builds upon these foundations, proposing a novel Joint Backscattering and Communication (JBAC) system that leverages DDPG to dynamically optimize RIS configurations. The framework ensures efficient and secure communication by enhancing legitimate signal paths while suppressing those accessible to eavesdroppers, thereby aligning with the key goals of future 6G-enabled IoT networks.

The rapid evolution of wireless communication systems and the advent of 6G networks bring unprecedented opportunities for hyper-connected environments, primarily driven by the proliferation of IoT devices. However, these advancements also pose significant challenges: ensuring secure, efficient, and reliable communication under stringent resource constraints, addressing the limitations of imperfect CSI, and managing quantized RIS phase shifts.

BackCom emerges as a promising solution for energy-efficient data transfer in IoT networks, yet it suffers from double-fading effects, limited communication range, and security vulnerabilities. Moreover, the dense connectivity envisioned for 6G networks amplifies the risks of eavesdropping, necessitating advanced security strategies that align with resource-efficient operation.

RIS offer a paradigm shift by transforming wireless environments with their ability to manipulate electromagnetic waves. While traditional research treats RIS as a passive signal reflector, this work explores its potential as an active optimization component. By strategically configuring RIS, the limitations of backscatter communication can be mitigated, and both communication performance and security can be enhanced. Furthermore, the integration of advanced reinforcement learning techniques, such as the DDPG algorithm, provides a pathway for real-time adaptability in dynamic wireless environments.

This study offers the following distinctive contributions:

Novel RIS-Enabled Joint Backscatter and Communication (JBAC) System with Imperfect CSI Management: We present a groundbreaking framework that seamlessly integrates RIS into IoT networks, revolutionizing both backscatter and direct communication modes. Unlike traditional approaches, our model tackles the challenge of imperfect CSI through an adaptive, learning-based strategy, ensuring robust system performance even in the face of channel estimation inaccuracies. By reimagining RIS as an active optimization component rather than a passive reflector, the framework dynamically optimizes signal paths, unlocking new levels of efficiency and reliability for next-generation IoT communication.

We propose an integrated framework that utilizes BackCom with PLS in an RIS-aided environment as shown in Fig. 1. This model incorporates elements from both the bistatic BackCom system and the RIS-assisted wiretap communication system, providing a comprehensive approach to secure communication.

The system involves several components. A base station (BS) equipped along M antennas functions as both a Carrier Emitter (CE) for BackCom and a transmitter for direct communication. The setup involves two single-antenna devices: a Backscatter Device (BD) and a legitimate user referred to as Bob. An Eavesdropper (Eve), equipped with M^′ antennas, aims to intercept communications. Additionally, an RIS with N reflecting elements is employed to enhance signal propagation through adjustable phase shifts.

In this system, the Carrier Emitter (CE) broadcasts a continuous wave signal s(t) with power P and a beamforming vector w∈CL×1. The power of the transmitted signal is subject to the constraint: (1)P=‖w‖2,where w represents the beamforming vector.

The Base Station (BS) also performs direct communication with Bob by transmitting a signal s, which satisfies the power condition E{|s|2}=1. The beamforming vector w is constrained by: (2)‖w‖2≤Pmax.

The communication channels between different components are represented as follows: the link from the CE/BS to the RIS is denoted as HCI∈CN×L and HTI∈CN×M; the channel from the CE/BS to Bob and Eve are denoted as hCB∈CM×1 and HCE∈CM×M′, respectively. Similarly, the RIS-to-receiver channels are represented by hIB∈CN×1 for Bob and HIE∈CN×M′ for Eve. Additionally, the direct channel from the Backscatter Device (BD) to Bob is given by hTB∈C.

The RIS reflects incoming signals by applying phase shifts. These phase shifts are described by the matrix: (3)Φ=diag(ejΨ1,ejΨ2,…,ejΨN),where Ψi∈[0,2π] represents the phase shift of the ith reflecting element. These phase shifts influence both the backscatter communication and direct transmission, adding a degree of control to the signal propagation.

The received signal at Bob incorporates contributions from both direct communication and backscatter communication. It is given by: (4)yB=(hIBHΦHTI+hTBH)ws+nB,where nB∼𝒞𝒩(0,σB2) is the additive noise at Bob. Similarly, the signal received by Eve is expressed as: (5)yE=(HIEHΦHTI+HTEH)ws+nE,where nE∼𝒞𝒩(0,σE2I) represents the additive noise at Eve.

The RIS enhances communication quality and secrecy by intelligently adjusting the phase shifts to strengthen the desired signals toward the legitimate user and reduce the signals leaking to the eavesdropper. This dual capability ensures efficient communication while mitigating the risk of eavesdropping.

CSI Acquisition in the Proposed System

Acquiring accurate CSI is essential for optimizing RIS configurations and ensuring secure communication. However, achieving perfect CSI in backscatter communication systems presents significant challenges due to double-fading effects, weaker received signals, and environmental dynamics. To address these issues, we adopt the following CSI acquisition strategies:

Direct Links: CSI for direct links (e.g., BS-to-Bob and BS-to-Eve) is obtained through traditional pilot-based channel estimation. The BS transmits known pilot sequences to Bob, who estimates the channel response and provides feedback. For Eve, the system assumes partial CSI knowledge via statistical models or worst-case approximations to ensure robust optimization.

The optimization problem for the RIS-aided 6G communication system seeks to maximize the secrecy rate RS, defined as the difference between the achievable rates at Bob (legitimate user) and Eve (eavesdropper): (6)RS=[RB(w,Φ)−RE(w,Φ)]+.

The goal is to increase RS by jointly enhancing the beamforming vector w and RIS phase shift matrix Φ: (7)maxw,ΦRS(w,Φ),subject to the following practical constraints:

Constraints 1. Transmit Power Constraint: Overall power of the beamforming vector must not exceed the set maximum limit: (8)‖w‖2≤Pmax.

2. RIS Phase Shift Constraints: Each RIS element’s phase shift must be within the feasible range: (9)Ψi∈[0,2π],∀i∈{1,2,…,N},and for quantized phase shifts: (10)Ψi∈{0,Δ,2Δ,…,2π−Δ},Δ=2πQ.

3. Imperfect CSI: Robustness to CSI estimation errors is incorporated as: (11)Pr(‖h−h^‖≤ϵ)≥δ,where h^ is the estimated channel, ϵ the error tolerance, and δ the confidence level.

4. Energy Efficiency Constraint: The system ensures minimum energy efficiency: (12)η=RB(w,Φ)‖w‖2≥ηmin,where ηmin is the threshold.

5. Multi-Objective Trade-Off: A balance between secrecy and communication quality is captured as: (13)λ1RS(w,Φ)+λ2RB(w,Φ),where λ1 and λ2 control the relative importance of secrecy and quality.

Problem Statement: Incorporating these constraints, the extended optimization problem is expressed as: (14)maxw,Φλ1⋅[RB(w,Φ)−RE(w,Φ)]++λ2⋅RB(w,Φ),subject to the transmit power constraint (8), RIS phase shift constraints (9) and (10), imperfect CSI robustness (11), and energy efficiency requirements (12).

This optimization framework integrates practical considerations, ensuring efficient and secure RIS-aided operation under real-world conditions while addressing performance and hardware limitations.

A Markov Decision Process (MDP) serves as a mathematical framework to model decision-making in situations where outcomes are influenced by both the decision-maker’s actions and elements of randomness. The key elements of an MDP, along with their mathematical formulations, are described as follows:

State Space (S): The state space S defines the collection of all possible states within the environment. Each state s∈S represents the condition or configuration of the environment at a specific moment in time.

MDP is formally represented by (S,A,P,R,γ). The agent goal is to determine an optimal policy π∗ that maximizes the expected sum of discounted future rewards. A policy π maps states to actions and is denoted as: π(a∣s)=Pr(At=a∣St=s).

The objective is to find the optimal policy π∗ that maximizes the anticipated cumulative reward Gt beginning from a given state: (17)π∗=arg⁡maxπE[Gt∣St=s,π],where the total discounted reward Gt from time t onward is defined as: (18)Gt=∑k=0∞γkRt+k+1.

Solving an MDP involves identifying the optimal policy π∗ that optimizes the expected cumulative rewards. Common approaches for solving MDPs, such as Q-Learning, Policy Iteration and Value Iteration, work by iteratively improving the policy or updating state and action values using the MDP’s defined components.

We apply the Markov Decision Process (MDP) framework to tackle the optimization hurdles in ensuring secure wireless communication systems, including both direct and backscatter communication. MDPs are well-suited for scenarios where current decisions–such as selecting the beamforming vector and RIS phase shifts–impact future states and rewards. This approach considers long-term consequences for the system’s performance and secrecy, unlike straightforward optimization approaches that prioritize immediate results.

To ensure scalability in large-scale RIS deployments, we utilize DDPG, a RL-based framework that excels in managing large action spaces, such as those present in RIS systems with a high number of reflecting elements. DDPG efficiently adapts the system to dynamically fine-tune the phase shift of RIS and beamforming vectors, balancing the trade-off between communication quality and secrecy. It also leverages actor-critic networks, where the actor determines the optimal actions based on the current state, while the critic evaluates these actions by estimating their Q-values, representing the expected rewards.

This reinforcement learning-based approach enables the system to learn and adjust configurations in real-time, making it computationally feasible for large-scale environments, especially an increase in the number of RIS elements. Moreover, parallelization and distributed learning techniques can further enhance the scalability and computational efficiency, allowing the system to handle complex, large-scale RIS deployments in real-world scenarios.

Actor Network: In our secure communication framework, the actor network defines the policy by linking the current environmental state st to a group of actions at, which includes the RIS phase shifts and beamforming vector elements. The policy is given as: (19)at=π(st|θπ),where at is the action vector at time t, st represents the current environmental state information along with the current vector for beamforming and θπ represents the actor network variables.

The action vector at is structured such that the first N elements correspond to the RIS phase shifts Φt=diag(ejΨ1,…,ejΨN). These Phase shifts are normalized to fall within the specified range. [−π,π] to ensure valid values: (20)Ψi∈[−π,π],∀i∈{1,2,…,N}.

The remaining elements of at correspond actual and virtual elements associated with the vector components of beamforming wt, which must satisfy the transmit power constraint: (21)‖wt‖2≤Pmax.

To ensure compliance, the beamforming vector is normalized as: (22)wt←wtmax(1,‖wt‖/Pmax).

By leveraging this approach, the agent can flexibly fine-tune the RIS phase adjustments and beamforming configuration according to the environment, optimizing transmission efficiency and ensuring secure communication.

Critic Network: The role of the critic network is to evaluate the actions generated by the actor network by estimating their Q-values. A Q-value signifies the expected cumulative reward for executing an action at in the state st and continuing to follow the policy π. The Q-value function is given as: (23)Q(st,at|θQ)=E[rt+γQ(st+1,π(st+1|θπ)|θQ′)|st,at],where θQ defines the parameters of the critic network, while θQ′ represents the targeted critic network characteristics. The target critic network improves training stability by providing a consistent reference for updating Q-values.

The state st consists of the CSI HTI,hIB,HIE,HCE,hTB, along with the current beamforming vector wt and RIS phase shifts Φt. The action at encompasses the phase adjustments of RIS and beamforming vector elements.

Reward Function: The immediate reward at time t, denoted by rt, indicates the system, s secrecy performance, RIS phase stability, and energy efficiency. It is defined as: (24)rt=α⋅[RB(wt,Φt)−RE(wt,Φt)]+−β⋅‖Φt‖variation2+η⋅RB(wt,Φt)Pused,where

RB(wt,Φt) and RE(wt,Φt) are the achievable rates at Bob (legitimate user) and Eve (eavesdropper), respectively.

Training Process: The DDPG algorithm iteratively trains the actor and critic networks using experience replay and target networks to ensure stable learning.

Transitions (st,at,rt,st+1) are stored in a replay buffer ℬ. During training, a minibatch of transitions is sampled from ℬ to break correlations between consecutive steps and improve learning stability.

The critic network is optimized by minimizing the following loss function: (25)L=1N∑i(yi−Q(si,ai|θQ))2,where yi=ri+γQ′(si+1,π′(si+1|θπ′)|θQ′) represents the target Q-value, calculated with the help of the target networks.

The actor network is updated using the policy gradient: (26)∇θπJ≈1N∑i∇aQ(s,a|θQ)|s=si,a=π(si)∇θππ(s|θπ)|si.

Target networks are updated slowly to ensure stability: (27)θπ′←τθπ+(1−τ)θπ′, (28)θQ′←τθQ+(1−τ)θQ′,where τ∈(0,1) is the soft update coefficient.

The computational cost of the DDPG algorithm in RIS optimization is dictated by the architecture of the actor and critic networks, the scale of the state and action spaces (which are proportional to the number of RIS elements N), and the minibatch size B during the training process.

The actor network maps the current state st, which includes the channel state information (CSI) and RIS configuration states, to an action at, representing the adjustments in RIS phases (N elements) and vector components of beamforming. The critic network evaluates the Q-value of the selected action, providing feedback for policy improvement.

The total complexity for one training iteration can be expressed as: (29)𝒪(B⋅l⋅h2)+𝒪(B⋅N),where l is the number of layers in the neural networks, h is the number of neurons per layer, B is the minibatch size, and N is the number of RIS reflective elements.

Scalability with N: The complexity grows linearly with N because the action space dimension is proportional to N. The phase shifts of each RIS element must be optimized, increasing the input size of the networks and the number of optimization variables. Despite this linear scaling, the use of parallelized computations in GPUs and efficient sampling in DDPG ensures scalability for practical deployments with RIS sizes of up to 200–300 elements.

Practical Considerations: Compact neural network designs with fewer layers and neurons (l, h) reduce overhead while maintaining performance. Feature engineering or clustering techniques can also group RIS elements to simplify control in large-scale scenarios.

The scalability of the DDPG algorithm makes it suitable for RIS-aided 6G networks, even as the number of reflective elements increases.

To enhance security DDPG approach is used by the system for communication that iteratively updates the actor, critic networks influenced by the feedback from the environment, enabling continuous improvement of the policy.

The parameters of critic network’s θQ improved by minimizing loss function L, which measures the error in the target Q-values and predicted Q-values computed from rewards and the target critic network’s output: (30)L=1N∑i(yi−Q(si,ai|θQ))2.

Here, yi is the Q-value of target, defined as the reward plus the discounted Q-value of the subsequent state-action combination as estimated by the target critic network.

The actor network updates its parameters θπ to select actions that yield larger Q-values as evaluated by the network of critic. The actor’s objective function J gradient is approximated through: (31)∇θπJ≈1N∑i∇aQ(s,a|θQ)|s=sia=π(si)⋅∇θππ(s|θπ)|si.

This iterative training process aligns the actor’s policy with the objective of maximizing rewards, improving both the efficiency and security of wireless communication.

In the proposed framework for secure data transmission utilizing RIS-assisted backscatter technology, the Markov Decision Process (MDP) is modeled to optimize both transmission efficiency and security. The components of the MDP, namely the state space, action space, and reward mechanism, are described as follows:

State Space: The state at time t, denoted as st, encapsulates the real-time conditions of the environment, including channel state information (CSI) for all relevant communication links. It is formally represented as: (32)st={HCI,HTI,hCB,hIB,HIE,HCE,hTB},where HCI∈CN×L represents the CSI from the Base Station (BS) to the RIS, HTI∈CN×M denotes the CSI from the transmitter to the RIS, and hCB∈CM×1 represents the CSI for the direct link between the BS and Bob (the legitimate user). The CSI for the RIS to Bob link is denoted by hIB∈CN×1, while HIE∈CN×M′ represents the RIS to Eve (eavesdropper) link. Additionally, HCE∈CM×M′ describes the direct channel from the BS to Eve, and hTB∈C captures the direct link from the Backscatter Device (BD) to Bob. These parameters collectively define the current state of the network, enabling informed decision-making for secure communication.

Action Space: The action at is the decision variable at time t and consists of two components: the beamforming vector wt and the RIS phase shift matrix Φt. The action is determined as:

(33)at=(wt,Φt)=π(st|θπ)+𝒩t,

where π(st|θπ) represents the actor network’s policy function, mapping the current state from st to action at and 𝒩t is the introduced during training to promote diverse actions.

The action space is subject to the following constraints. The beamforming vector must satisfy the transmit power constraint: (34)‖wt‖2≤Pmax,where Pmax denotes the maximum allowable transmit power. The RIS phase shifts are constrained as: (35)Ψi∈[0,2π],∀i∈{1,2,…,N},where Ψi represents the phase shift of the i-th RIS element. These constraints ensure that the actions taken are both physically feasible and compliant with the system’s hardware limitations.