6G Physical Layer Security

Abstract

Securing the proliferation of wireless networks in 6G requires security-based signaling as a native component. This paper analyzes Physical Layer Security (PLS) applied to 6G Radio Access Networks (RAN) to enhance Layer-1 security. The description defines a PLS air interface and system model with AI/ML-based intelligent codebook generation and detection schemes. The paper also proposes an operational overview of AI/ML integrated PLS with shared key-agreement protocol in an O-RAN architecture for 6G security. Results include codebook generation details, the impact of MIMO antenna array size, and the key-Bit Error rate (BER) of 6G-PLS detection in the presence of eavesdroppers and Rayleigh fading plus noise.

1. Introduction

6G Wireless Systems under development provide advanced next-generation mobile communication capability with significant integration of distributed neural networks and joint communication and sensing. Integrating AI/ML and 6G fuse capabilities across high-rate communications, high-speed computing, cyber-physical systems, and biologically inspired frameworks, ushering in an era of true Intelligence of Everything (IoE) [1]. Emerging 6G industries include smart-grids, Factory 5.0, automated transportation, 3-Dim immersive XR, and remote surgical robotics.

Prior generation 5G Systems control machines and Internet of Things (IoT) devices provide connectivity to various industrial applications in agriculture, construction, smart grids, healthcare, transportation, satellites, and IoT. A significant achievement of 5G technology involves operation across a vast expanse of cellular bands (e.g., 3GPP FR1, FR2) from 600 MHz to 71 GHz. The FR2 frequencies of 5G support broadband millimeter wave (mmWave) applications. Fixed-access mmWave early 5G roll-outs will still use sub-6 GHz for supporting mobility. Many 5G enhanced mobile broadband (eMBB) and ultra-reliable low latency communication (URLLC) applications jointly mandate high data rates and low latency. Tailored 6G wireless systems inherently overcome these challenges Many 5G enhanced mobile broadband (eMBB) and ultra-reliable low latency communication (URLLC) applications jointly mandate high data rates and low latency. Tailored 6G wireless systems inherently overcome these challenges [2]. Table 1 presents the key performance indicators to enable 6G applications, with a comparative overlook with 5G technology simultaneously.

KPI	5G	6G
Peak data rate (DL)	20 Gbps	100 Gbps to 1 Tbps
Peak spectrum efficiency	30 b/s/Hz	60 b/s/Hz
Extreme ultra low latency	Less than 1 ms	0.1 ms
Connection density	10^6 device per m^2	10^7 devices per m^2
Area traffic capacity	10 Mb/s/m^2	1 Gb/s/m^2

Table 1.

Key performance indicators of 5G and 6G [3, 4].

Table 1 shows that 6G technology will have almost 1 Tbps peak data rate with connection density of 1 Gb/s/m², which are 100 times more than the current prevailing 5G technology. So this kind of enormous information traffic migration from wired ethernet to 6G wireless necessitates high levels of security. 6G wireless system’s network slicing taxonomy typically delineates Enhanced Mobile Broadband Plus (eMBB-Plus), Ultra-High-Speed with Low Latency Communications (uHSLLC), and Secure Ultra-Reliable Low-Latency Communications (SURLLC) [5]. In addition, AI being the critical enabler of 6G, AI-generated configurations across multiple 6G System layers support massive, densely populated network infrastructure that accommodates an exponential increase in radio access nodes. Therefore, security, especially at air interfaces, is essential. Physical layer security offers the potential for intrinsic, low-latency security at Layer-1 as a native component. Also, within the context of 6G intelligence, AI/ML provides many enhancements to parameter estimation, detection, mobility optimization, and detection of malicious actors [6].

AI/ML in 6G ecosystems requires a sophisticated infrastructure to support developing and testing advanced algorithms and systems. The infrastructure should include high-performance computing and GPU clusters, which are essential for training and validating models. AI/ML algorithms require vast data collection and analytics tools to train and validate models. Integrated intelligence in the 6G Core, 6G Radio Access Network (RAN), and E2E management occur through AI/ML [7, 8, 9]. The 6G research community actively proposes integrated AI/ML design within the 6G system infrastructure for intelligent allocation and management of the network, spectrum, computing, data storage resource, and security. References in [10, 11, 12, 13] delineate 6G-intelligent use case applications in control, sensing, automated operation, and security.

This chapter presents an AI/ML-integrated, shared key-based Layer-1 Physical Layer Security (PLS) protocol as a candidate for 6G SURLLC. With its transmission designs based on the intrinsic randomness of the wireless medium to achieve secrecy, PLS ensures lower complexity and incurs less latency than traditional cryptography [14]. In addition, intelligent optimization produces a more secure and, thus, reliable air interface protocol leveraging AI/ML-based PLS. Along with security, the proposed PLS method offers a fundamentally lower latency exchange of secret information when operating in machine-to-machine (M2M) mode. At the same time, our effort toward achieving an intelligent and optimized PLS also applies to 4G and 5G systems. However, many existing 5G and prior 4G Physical Layer infrastructures must approve standardization changes. Furthermore, the ITU’s recommendations on” IMT for 2030 and Beyond” [15] and the 3GPP 6G Work Items [16] on 6G actively investigate new air interfaces for evolution from 5G-to-6G, leading to a focus on Layer-1 6G security [17, 18].

The overview and organization of this chapter are as follows: in Section 3 of this chapter, we study the fundamental concepts of Physical Layer Security, including a review of the shared Key-based PLS system. Section 4 of the chapter introduces practical schemes for integrating PLS for 6G communication. This section applies Deep Learning (DL) in the context of PLS to jointly optimize and provision higher-tier security for control, data, and management channels. Specifically, the section describes an AI/ML integration in the shared key-based PLS model, contributing to an optimized approach toward codebook generation and intelligent shared secret key decoding schemes. In addition, in this section, we introduce, for the first time to our knowledge, an overview of the AI/ML-integrated PLS scheme workflow for 6G O-RAN. The new solution adds latency improvements within an O-RAN Alliance framework and enhanced schemes for PLS integration within 6G O-RAN. Section 5 of the chapter illustrates PLS models, protocols, simulations, and results demonstrating improved AI/ML-based security performance. Finally, Section 6 concludes with a discussion on future scope and prospects of this research topic.

2. Physical layer security for 6G

Physical Layer Security protocols overlay secure transmission schemes onto Physical Layer (PHY) data links with the goal of shared secret information exchange. Time Division Duplex (TDD) wireless channels, typical in 6G wireless, support uplink-downlink (UL-DL) channel reciprocity. The legitimate users exchange information over spatial channel statistics that non-legitimate users and eavesdroppers can approximate, but only if they reside near both ends of the link.

Physical-layer security classification categories generally consist of SINR-based (keyless) or key-based approaches. Keyless methods include beamforming, power allocation, and injection of artificial noise algorithms [19]. The second key-based category involves complexity-based schemes utilizing shared secret keys between legitimate users at the physical layer [20]. The shared key protocols represent a significant underpinning of advanced cryptographic engineering.

Keyless PLS schemes possess several drawbacks. Additive noise in Artificial Noise (AN) based PLS scheme degrades detection. Also, in beamforming-based PLS, the message signal is guided in the correct direction to the legitimate receiver using beamforming. Transmit power concentrates within the main lobe beam but does radiate in the antenna’s minor side lobes. The finite number of transmitting antennas only has a slight spatial directivity. Thus, this penetration allows nearby eavesdroppers to decipher the message signal [21].

Key-based physical layer security systems integrate the wireless transmission medium as a promising source of randomness. The rich scattering in wireless environments results in stochastically varying multipath fading at each mobile antenna. TDD channel reciprocity applies to legitimate users with channels defined by their joint spatial channel statistics. Non-proximate positioning by eavesdroppers results in uncorrelated channel statistics, preventing malicious users from duplicating secret key generation protocols. For this reason, shared key-based PLS schemes have gained significant research interest.

Hence, in this chapter, we adopted a key-based PLS scheme. To prevent eavesdroppers from being able to estimate the reference signals, we have adopted a PHY layer key generation scheme utilizing a precoding matrix index (PMI) and rotated reference signals [22]. The PMI method aligns with the 6G OFDM requirements. Precoding is an operation for the MIMO system to utilize the best subchannel gains. Codebook-based precoding balances the feedback overhead, the equalizer complexity, and the system performance [22]. In the shared key and codebook-based PLS model, a global codebook shared among the communication terminals contains a finite number of precoding matrices. Each precoding matrix in the codebook has an index, the PMI. The secret information from legitimate transmitters and receivers maps to a precoding matrix. The precoding matrix indices, in turn, map to secret keys transmitted from legitimate information sources. The method formulates codebook elements [23] by applying a DFT codebook. Operators drawn from the field of complex number unitary matrices generate the precoders. The PLS system’s complete secret key concatenates both transmitter’s and receiver’s private information. Hence, an eavesdropper cannot extract the secret key by simply placing itself closer to the transmitter or receiver solely.

3. Practical schemes for integrating physical layer security in 6G

Figure 1 illustrates the proposed security model for 6G cellular consisting of three users: a first user Alice represents the Radio Access Network (RAN). A second user, Bob, corresponds to the User Equipment (UE). A third illegitimate user, Eve, passively eavesdrops on the secret bidirectional information exchange between Alice and Bob (the legitimate users). The successful exchange of private information implies securely transmitting secret information. The scheme leverages machine learning-based share key-based PLS techniques for Bob (UE) and Alice (RAN) across 6G communication channels. Transmission performance for legitimate channels improves significantly, with AI/ML automatically mitigating the concern for eavesdropper channels.

Figure 1.

Shared key block ciphers, an important class of block ciphers, require one key for both encryption and decryption. The PLS system model secures the 6G wireless channel between Alice–bob–eve for shared key-based ciphers.

In Figure 1, the TDD channel between Alice and Bob, denoted as $H_{\mathit{AB}}$, and Bob to Alice channel, denoted as $H_{\mathit{BA}}$, have a well known transpose relationship. $H_{\mathit{AE}}$ and $H_{\mathit{BE}}$ denotes the channel between Alice-Eve and Bob-Eve, respectively. In the MIMO-OFDM transceiver, the transmitter, Alice, first sends out a reference signal for the legitimate receiver, Bob, to estimate the channel matrix $H_{\mathit{AB}}$, as illustrated in Figure 1. During signal transmit Alice and Bob obfuscate the channel matrix by applying a random channel sounding operator. Indirectly, they observe a singular vector obtained from performing Singular Value Decomposition (SVD) of the channel matrix. G represents a random reference signal operator in Figure 1. The subscripts represent the steps in the transmission process and their corresponding channels. In the secret key based PLS scheme, the transmitter and receiver each contribute their own secret information to a shared secret. Bob sends his secret information after encoding it using a codebook. If operating key-exchange protocols, this encoded secret information should be considered the ‘secret key.’ Bob sends $S_B$, his secrete contribution, to Alice over the channel. Alice estimates ${\widehat{S}}_B$ and similarly sends her encoded secret information or secret key, $S_A$ to Bob. Bob’s estimated version of $S_A$ is ${\widehat{S}}_A$. Concatenating ${\widehat{S}}_A$ and ${\widehat{S}}_B$ gives Alice the full secret information. The length of the secret key is pre-agreed, and also the codebook is known to all parties which makes it ‘universal’.

3.1 Framework of proposed shared key based PLS system model

The framework analyzes Alice and Bob, each equipped with multi-antenna systems. An elementary secrecy problems involves the wiretap channel [24] at the eavesdropper, a cascade second discrete memoryless channel. The universal codebook available to Alice, Bob, and Eve contains precoding matrices and the corresponding PMIs. The eavesdropper reconstruction of the full wireless environment represents an ongoing risk. Hence, the scheme adopts a rotation operator applied to the reference signal, instead of the unaltered reference signal. Embedding the secret information within the wireless channel conceals Alice’s and Bob’s wireless signal against Eve’s eavesdropping. The detailed formulation and procedure is explained in the later part of the chapter.

Prior PLS publications (see [25]) explained the shared key-based PLS framework and introduced the use of AI/ML within the decoding scheme. This chapter, describes improved security performance within the context of a MIMO wiretap channel. A passive eavesdropper Eve, monitors the channel between Alice and Bob. The protocol focuses on two primary areas to optimize with the help of ML algorithm: (a) in generation of an optimum codebook, (b) in decoding secret key at receiver end.

3.1.1 Codebook generation based on the optimum-PLS capacity using AI/ML

The optimum codebook selection process goes through a Feed Forward Neural Network (FFNN) algorithm. The reason for choosing FFNN algorithm is the simplified architecture of the algorithm and less computational time loss, otherwise processing delay would be a factor that can impact the performance of our model.

First, Alice sends out a rotated reference signal for Bob. As the legitimate receiver, Bob estimates the channel matrix. Bob finds the precoding matrix and its corresponding PMI from the codebook that maximizes the channel capacity shown in Eq. 1 [22]:

$$C_{H,F}={\mathit{log}}_2\mathit{det}\left[I_p+\frac{E_s}{n_s{\sigma }^2}{F}^{†}{H}^{†}\mathit{HF}\right]$$E1

where $I_p$ is the identity matrix with p denoting the number of transmit and receive antennas, $E_s$ is the total power of the transmitted signal vector, $n_s$ is the number of data, ${\sigma }^2$ is the noise variance, H is the channel between legitimate transmitter and receiver at any time of observation and F is the precoding matrix from the universal codebook, F constructed following [23], such that $F\in \mathrm{ℱ}$. The estimated optimum precoding matrix, denoted as $\widehat{F},$ would need to satisfy the maximum capacity requirements:

$$\widehat{F}=\underset{F\in \mathrm{ℱ}}{argmax}{C}_{H,F}$$E2

The FFNN model generates the optimum precoder matrices and the codebook. The parameters used are shown in algorithm 1 below where m is the number of codebook-bit and P is the number of antennas. The input data to the ML model is precoders from the universal, DFT codebook, F, such that $F\in \mathrm{ℱ}$, go through the ML model and are trained to choose the optimum codebook elements that satisfy the MIMO secrecy capacity represented in Eq. 1. The FFNN algorithm determines the optimum precoding matrix, $\widehat{F}$, that maximize the capacity as shown in 1.

3.1.2 Intelligent PLS decoding process using AI/ML

Alice and Bob exchange their secret keys that employs an optimum codebook obtained from and decoded by a Deep Neural Network (DNN) based algorithm. The sum-secret-rate maximization between the Bob-to-Alice transmitter simultaneously minimizes the eavesdropper’s signal capacity, a main goal. Achieving perfect secrecy occurs when the transmitter and the legitimate receiver communicate at some positive rate while ensuring that the eavesdropper receives zero bits of information. Our method successfully achieved optimal secrecy PLS detection. When maximum secrecy is obtained between legitimate transmitter and receiver, the threat from a passive eavesdropper drops.

Among all other ML techniques, the choice of DNN for decoding includes the following advantages: (i) Once the training process is finished, a deep neural network (DNN) provides the accuracy solutions within a very short computational time [26]; our time varying channel computational latency occurs in near real time and with a much reduced processing time. (ii) Other ML algorithms, like CNNs and LSTMs are used for classifying sequence data. However, in our case, the data is not sequential but rather consists of features which predict a class (the PMI). Hence, DNN is the best match for our model. Algorithm 2 presents the parameters for deep learning neural network codebook detection.

Using these two ML algorithms, a detailed formulation and working procedure of the shared key based PLS model is shown in Figure 2 and is described as follows:

Algorithm 1 Neural Network Parameters for Optimum Codebook Generation

Construct net for FFNN: $2\times m\times P$ input nodes, Hidden layer neurons: 10, Output node: 1; Input: Precoder from the DFT codebook constructed with the help of [23], Target: optimum precoder, $\widehat{F}$.; Neural Network Parameters: Levenberg-Marquardt optimization.; Train NN: Forward Propagation: for j layers do; for i neurons in the layer do; calculate ${\mathit{net}}_j={\sum }_1^i{W}_{j,i}\left[f_j\left(x_{j,i}{W}_{j,i}+b_{j,i}\right)\right]+W_{j,0};$ f = activation function x = input; calculate output, $y_j=f_j\left(x_{j,i}{W}_{j,i}+b_{j,i}\right);$ calculate slope, $s_j=\frac{\partial f_j\left({\mathit{net}}_j\right)}{\partial {\mathit{net}}_j}$; end for; end for; Backward Propagation: Calculate Jacobian of performance with respect to the weight and bias; $j=J^{\ast }J$; $e=J^{\ast }E$; $\mathit{dx}=\frac{-\left(j+\mathit{I\mu }\right)}{e}$; E = cumulative error; increase $\mu$; if dx reduced then; update network and decrease $\mu$; else if max epochs reached; performance minimized to the goal; $\mu$ exceeds ${\mu }_{\mathit{max}}$. then; Stop Training; end if

Algorithm 2 Deep Neural Network Parameters for Detection

Construct net for DNN: $2\times m\times P$ input nodes, 3 Hidden Layers: Activation function used: ReLU, $2^m$ output nodes; Input: Received input data processed by the ML algorithm outputs complex information data mapped according to the codebook elements. Step 1 of Bob-Alice transmission describes the generation of this data. Complex inputs are split into real and imaginary data-parts.; Output: Output node with maximum probability points to a predicted class, and the system applies the Softmax Activation Function.; Neural Network Parameters: Learning rate: 0.01, Optimizer: Stochastic Gradient Descent (sgdm).; Train DNN: for i = 1,…. number of training steps do; hidden vector, $\overline{h}=\mathit{\text{ReLU}}\left(\overline{x}.W\right);$ prediction vector, $\overline{y}=\mathit{\text{ReLU}}\left(\overline{h}.W\right);$ Compute loss function, $\mathrm{ℒ}\left(\times \right)=-{\sum }_{i=0}^N\widehat{\overline{y_i}}.\mathit{log}\left(\overline{y_i}\right),\widehat{\overline{y_i}}$ = entries in the ground truth label.; end for; if Loss function not smaller then; compute gradient descent and update weights.; end if

Figure 2.

6G signal privacy using AI/ML for secure decoding of information in physical layer security.

Step 0: AI Initialization stage Alice first initiates a request to the legitimate receiver for private information exchange. Alice transmits a reference signal r, rotated by a random unitary matrix G. This random unitary matrix, G obscures the channel and generates uniformly distributed signaling. It depends on number of antennas used.

TDD implies that the transmitter and receiver channels $(H_{\mathit{AB}}$ and $H_{\mathit{BA}})$ are transposes of each other such that $H_{\mathit{BA}}={\left(H_{\mathit{AB}}\right)}^T$. The protocol applies TDD reciprocity.

Step 1: Bob-to-Alice

1. Bob receives the signal, estimates the channel to obtain $H_{\mathit{AB}}G,$ and performs the following SVD: $H_{\mathit{AB}}G=U_B{\sum }_B\left(V_B^{†}G\right)$. According to the required secret key length, Bob generates an p-bit random secret key sequence $S_B$ and applies channel coding to it to generate $C_B$. Bob divides $C_B$ into $\frac{p}{m}$ groups of m-bit sequences and looks up each sequence in the optimum codebook indices and finds the corresponding codebook element $F_B$. Bob transmits a rotated reference signal $G_{1}r$ to Alice, where $G_1=U_B^{\ast }{F}_B^{†}$.

2. Alice receives the noisy information signal over the channel and estimates $H_{\mathit{BA}}{G}_1$. Alice inputs the noisy received information into the AI/ML model, as illustrated in Alice’s AI/ML detector in Figure 2. The AI/ML model takes the noisy signal information as input. Alice obtains estimated version of Bob’s signal information, ${\widehat{S}}_B$ as model output. Alice has her own random p-bit secret key $S_A$ and concatenates $S_A$ and the estimated ${\widehat{S}}_B$ to get the secret information. The model algorithm updates it’s bias weights according to the loss calculation in back propagation stage while training.

Step 2: Alice-to-Bob

1. Alice performs an SVD, such that $H_{\mathit{BA}}{G}_1=V_A^{\ast }{\sum }_A\left(U_A^T{G}_1\right)$ and applies channel coding to $S_A$ to generate $C_A$. Alice looks up $C_A$ in the optimum codebook and finds the corresponding codebook element $F_A$. Next, she transmits a rotated reference signal $G_{2}r$ to Bob, where $G_2=V_A{F}_A^{†}$.

2. Bob estimates $H_{\mathit{AB}}{G}_2$ (see Step 1) channel parameters and feeds the noisy received information into the AI/ML model, denoted as the Bob AI/ML detector in Figure 2. Bob obtains estimated version of Alice’s signal information as model output, ${\widehat{S}}_A$, concatenates $S_B$, and estimates ${\widehat{S}}_A$.

Bob and Alice inject additive random noise into the received secret key. The PLS procedure tests the performance of the model by comparing the estimated version of transmitted random signal information to the actual secret key and conclude the performance analysis based on Key BER results. Bob repeats his steps again, sends new secret information and starts transmitting to Alice’s for detection. The process iteratively repeats.

Both Alice and Bob have half of the information which they generated themselves and the other half which they estimated in the form of the PMIs. Even as Eve moves into the spatial proximity of Alice, she eavesdrops only $S_B$ rather than $S_A$ and $S_B$.

3.2 Integration of open RAN solutions with key based PLS

This section described the proposed PLS scheme in the context of 6G communications. A vital capability enabled by PLS involves extensible adaption for the new technologies in 6G cellular. Emergign technologies include massive-MIMO (massive Multiple Input, Multiple Output) [27], millimeter wave communications, sub-terahertz communications [28], network-based sensing [29], network slicing [30], and ML-based digital signal processing. Managing and optimizing these new network systems require flexible security solutions that integrate across the Radio Access Network (RAN). Open RAN (O-RAN), the most prominent 6G RAN configuration, disaggregates, virtualizes and enables the” softwarization” of infrastructure resources and components via open standards, open interfaces, and interoperability across private vendors and open source software communities [31]. Disaggregation and virtualization enable flexible deployments, based on cloud-native principles. Reliance on cloud frameworks increases the resiliency and reconfigurability of Open RAN and allows operators to aggregate technology across different sizes and varieties of equipment vendors.

The O-RAN Alliance formed a next Generations Research Group (nGRG) with the aim of carrying out research about O-RAN and future 6G networks [32]. The research community focus has a strong incentive to pair 6G O-RAN with strong security. Authors in [33] introduced the O-RAN building blocks and architecture, with use cases related to the application of ML to the RAN. To our knowledge, no work has demonstrated how Layer-1 PLS will practically operate with embedded intelligence in a 6G O-RAN architecture context. The security scheme discussed aims to extend the functional disaggregation paradigm proposed by 3GPP for 6G PLS gNBs [34]. RAN disaggregation splits base stations into different functional units: (a) High Layer Split, (b) Low Layer Splits and (c) Double Splits. The functional splits are represented in Figure 3. While considering the functional split concept defining a fronthaul interface, there are two competing interests

1. keep O-RAN Radio Unit (O-RU) as simple as possible while minimizing size, weight, and power draw. For instance, the more complex an O-RU, the larger, heavier and more power-hungry the O-RU tends to be.

2. Benefits accrue for interfaces at a higher protocol stack layers, but tends to reduce the interface throughput relative to a lower-level interface. Higher-level the interface have more complexity impact on the O-RU.

Figure 3.

Overview of functional split for O-RAN.

Many groups have standardized upon a subset of allowable O-RAN split points. The “7−2x” [35] function option split allows a variation, with the precoding function to be located either “above” the interface in the O-DU or “below” the interface in the O-RU. Following the O-RAN split option 7−2x shown in Figure 4, the O-RU logically hosts radio frequency (RF) processing and the low-PHY layer consists of the D/A conversion, cyclic prefix, and IFFT insertion. The O-DU within the O-RAN Distributed Unit which is a logical node for hosting a high-PHY layer consisting of Resource Element (RE) Mapping, PLS Precoding, Layer Mapping, Modulation, Scrambling, Coding, along with other layers- MAC and RLC layers. A centralized Unit (CU) runs SDAP/RRC and PDCP layers. The interface between O-DU and O-RU is known as Open Fronthaul (O-FH) interface. O-RAN has defined and standardized the F1 interface for communication between the O-CU and O-DU.

Figure 4.

Distributed O-RAN with physical layer security for 6G.

In O-RAN, control, optimization, and AI/ML algorithms can be trained and deployed in two logical functions: the non-real-time (RT) Radio Intelligent Controller (RIC) and the near-real-time (RT) Radio Intelligent Controller (RIC), shown in Figure 4. The non-RT RIC is a logical function internal to the Service Management and Orchestration (SMO) and complements the near-RT RIC for intelligent RAN operation and optimization on a time scale larger than 1 s. It hosts Data management and Exposure, and supports AI/ML models for training within applications denoted as rApps. The near-RT RIC is a logical function deployed at the edge of the network and operates control loops with a periodicity between 10 ms and 1 s. It interacts with the O-DU and O-CU and consists of multiple applications supporting custom logic, called xApps, which are microservices used to provide Near-RT controllable operation in the O-DU RAN for PLS through the O-RAN E2 interface. The xAPPs receive measurements from the DU node and respond with control actions.

However, to ensure SURLLC, the control decisions and execution needs to be realized in real time. Limiting the execution of control applications to the near-RT and non-RT RICs prevents the use of data-driven solutions where control decisions and inference must be made in real time, or within temporal windows shorter than the 10 ms supported by near-RT control loops [33, 36, 37] as the RICs have limited access to low level information. The near-RT RIC brings network control closer to the edge, but it primarily executes in cloud facilities [38]. Therefore, data needs to travel from the DUs to the near-RT RIC, and the output of the inference needs to go back to the DUs/RUs. The additional communication results increased latency and overhead over the E2 interface to support data collection, inference, and control. To mitigate this challenge, authors in [39] introduced the notion of dApps, custom and distributed applications that complement xApps/rApps by implementing RAN intelligence at the CUs/DUs for real-time use cases outside the timescales of the current RICs. The authors have demonstrated that using dApps can result in a 3.57× reduction in overhead.

Although dApps has been introduced and proposed in [39], required interfaces are yet to be standardized by ORAN Alliance. In this article, we have adopted dApps to implement low latency security scheme for operating in the lower layer of protocol stack and extended dApps to propose a functional integration and working procedure overview for the use case of PLS, which will be the first to our knowledge, to open the paradigm of a low latency and secure, intelligent, Layer-1 security scheme design proposal proposed for 6G ORAN. We call it ‘security Apps’ or ‘sApps’- an application for exchanging low latency information securely between the UE and O-RAN network over physical layer security channels.

The proposed PLS scheme integrates with O-RAN interfaces. This section describes extensions for the management, deployment and execution of sApps:

1. Authors in [39] proposed that southbound interfaces allow frequency domain samples to be carried over from the RU to DU via open fronthaul. Similarly, the southbound interfaces will allow dApps to be executed at the CU to perform inference. This protocol proposes using the E2 interface as the south bound interface. 6G-PLS enables communication between the near-RT RIC, the user plane of CU (O-CU-UP), control plane of CU (O-CU-CP) and the O-DU. It also enables adapting and extending the E2 setup procedure and E2 service model (SM) to the sApps as illustrated in Figures 5 and 6, respectively.

2. Authors in [39] have proposed that dApps can receive enforcement information from near-RT RIC via the E2 interface, acting as the northbound interface. In this article, we have adopted this proposal and brought forward the proposal that sApps in O-CU-CP will communicate with near-RT RIC via E2 interface for AI/ML model training, which is elaborated in algorithm 3.

3. To mitigate potential conflict of intent between sApps and xApps, near-RT RIC hosts an sApp controller and monitor.

4. The RAN and RICs communicate via O1 interface between each other. The near-RT RIC and non-RT RIC communicate via A1 interface.

Figure 5.

Procedure for the setup of an E2 session in the E2 node.

Figure 6.

Flow diagram of communication in E2 nodes in O-RAN.

In the 6G-PLS scheme, Alice (RAN) needs to initiate the first step: ML Initialization Stage, and send the rotated reference signal, Gr to Bob (UE). This is proposed to take place in a sApp located in the E2 nodes. The E2 set up procedure is illustrated in Figure 5. E2 interface runs on top of SCTP protocol. The E2 node (in this case the O-DU) transmits an E2 setup request that lists the RAN functions and configurations it supports. In combination with the identifiers for the node, the CU node processes this information and replies it with an E2 setup response.

After the connection is established, an E2 Service Model (SM) RAN Controller implements the 6G PLS protocol. To send the rotated reference signal to Bob (UE), Alice (RAN) publishes data to the CU node to initialize the PLS procedures. Algorithm 3 illustrated in Figure 6 describes the PLS control service message exchange between the RAN CU node and the DU node. For transmission or Downlink (DL), the O-DU node computes resource element (e.g., 5G RE or subcarrier) mapping, PLS Precoding, Layer mapping, modulation, scrambling, coding and eventually sends Gr to Bob (UE). After receiving the reference signal, Bob initiates the Step 1 transmission as described in part A of this section. For the Uplink (UL) transmission, Bob sends his encoded secret key to Alice. Also, the O-DU performs PLS decoding instead of PLS precoding in the case of Uplink (UL) as well as all the other steps mentioned.

Algorithm 3 PLS procedure between the CU node and DU node for Bob-to-Alice transmission

$\mathit{\text{Alice}}\Leftarrow \mathit{RAN},\mathit{Bob}\Leftarrow \mathit{UE}$; a. ML Initialization Stage: An sApp in O-CU subscribes to the DU node for the exposure of PLS control service.; The sApp specifies a triggering PLS event or timer.; if DU node detects PLS event trigger/timer expires then; 1. Send insert message signaling initialization of PLS; 2. Start wait timer; if sApp responds back with PLS control request: Initialize PLS then; 1. Cancel wait timer and resume PLS procedure; 2. Send back control ACK; else if Wait timer expires then; PLS procedure may continue autonomously; or; PLS procedure is halted; end if; end if; b. At UE: Bob receives $H_{\mathit{AB}}G$.; SVD: $H_{\mathit{AB}}G=U_B{\sum }_B\left(V_B^{†}G\right)$.; Generate optimum codebook and secret key $S_B$; encodes it using the codebook elements and corresponding PMI.; Precoder from codebook, $F_B$.; Transmit: $G_{1}r=U_B^{\ast }{F}_B^{†}r$.; c. At RAN: Alice generates $S_A$.; Alice receives $H_{\mathit{BA}}{G}_1+\text{noise}=\text{Data}.$; i. Data Collection: $\mathit{SMO}\leftarrow O-\mathit{DU};$ Data Pre-processing: $\mathit{SMO}\leftarrow \mathit{SMO}$.; ii. AI/ML Training Data: sApps in $O-\mathit{CU}-\mathit{CP}$ or xApps in Near-RT RIC $\leftarrow \mathit{SMO}$; Train the AI/ML Model.; iii. Deployment: sApps in $O-\mathit{CU}-\mathit{CP}\leftarrow$ sApps in $O-\mathit{CU}-\mathit{CP}$ or sApps in $O-\mathit{CU}-\mathit{CP}\leftarrow \mathit{\text{Near}}-\mathit{\text{RTRIC}}$.; iv. Inference: sApps in $O-\mathit{CU}-\mathit{UP}\leftarrow$ sApps in $O-\mathit{CU}-\mathit{CP}$; AI/ML Output: $O-\mathit{DU}\leftarrow O-\mathit{CU}-\mathit{UP}$; Alice estimates ${\widehat{S}}_B$ and concatenates with $S_A$

Alice receives Bob’s secret information and decodes and estimates S_B. The AI/ML training data collected in SMO applies training protocols in x-Apps of the Near-RT RIC or in sApps in the O-CU-CP. The location of process depends on the operator’s intent, as explained in algorithm 3. Both xApps and sApps reside as cloud native containers (e.g., Docker). In the former case, data for inference is received from xApp via the E2 interface, while in the latter, data is locally available at the sApp in E2 node (in this case the CU node). An operator’s intent determines how to split and distribute intelligence among xApps and sApps, and dispatch them. To orchestrate this better and also to mitigate any possible conflict, there is an sApp Controller and Monitor hosted in near-RT RIC. The AI/ML life cycle consists of following steps which follow guideline provided by the O-RAN Alliance (see WG1 [40]):

1. The secret information collected over the O1 interface in SMO from the O-DU is pre-processed before the data is fed to near-RT RIC for training via A1 interface. If the training occurs in sApp inside O-CU-CP, then data will be availed from O-DU locally.

2. Offline/Online training of the AI/ML model occurs in this stage facilitated by xApps or sApps- based on operator’s intent.

3. The trained AI/ML model goes to an sApp in the O-CU-UP for deployment-over E2 interface if the training took place in xApp in Near-RT RIC and over E1 interface if the training took place in O-CU-CP.

4. The trained model is now deployed and implemented in this stage. Next, the model is deployed in the inference host node (in this case, in O-CU-UP). After successful deployment, data is fed to the Inference Host model to perform designated classification or prediction tasks. The output is then fed to O-DU. Eventually, Alice estimates the version of Bob’s signal information, ${\widehat{S}}_B$ as model output. By concatenating $S_A$ and ${\widehat{S}}_B$, Alice now has the entire information. Similar process follows in case of Alice-to-Bob transmission.

In the O-DU, real time KPIs report observations from the environment, performance evaluation, and ML performance feedback. Based on the results, sApp make a control decision whether to initiate an AI/ML Agent re-training trigger. When re-training is required, based on these real time data, the AI/ML model retrains in the O-DU, or based on operator’s intent via the O1 interface. Real time data is fed to SMO to be trained in xApp in the Near RT-RIC. The AI/ML lifecycle iterates.

The integration of sApps occurs in lower layer of the RAN architecture. The scheme further proposes realizing ‘AI at the edge’ to significantly improve network performance and latency.

4. Simulation results

Analysis of the simulation results in Matlab Key Bit Error Rate (BER) of the AI/ML-based PLS scheme confirm the efficacy of the approach. Bob and Alice both transmit secret keys under noise-limited scenarios in 2 × 2 and 4 × 4 MIMO channels with Rayleigh fading. The model applies a 960 kHz sampling rate and a 130 Hz Doppler shift. The simulations applied a Rayleigh fading channel model for 2, 4 and 5-bit codebooks across a Monte Carlo transmission model containing 5000 information bits. The PLS scheme applies a secret key agreement protocol. Analysis employs Key Bit Error Rate (BER) metrics and measures the detector Key BER probability at the receiver.

In the ML algorithm, the data split used was 70–30% for training and validation, while the training epoch chosen was 40. As a result, the validation accuracy was 98.33% for Bob-to-Alice (UL) Tx and 95% for Alice-to-Bob Tx (DL). The results prove that the model neither underfits nor overfits and is well-trained to perform pattern recognition and classification.

We first compare our proposed model with a traditional shared key based PLS model with DFT codebook where no ML-algorithm was adopted. 5G systems employ polar codes for the control channel and LDPC codes for the data channel. Figures 7–10 illustrate the raw BER prior to error correction decoding. From Figure 7, for a 2 × 2 MIMO system, it is observed that when SNR changes from 10 dB to 50 dB, Key BER decreases with the signal power increase and noise variance decreases for both non-ML and ML models. However, for a 4-bit codebook, the dB-change in Key BER performance is higher than that of a 2-bit codebook due to higher detection precision requirements for a higher-order codebook. For example, for an SNR of 10 dB, Key BER for a 2-bit codebook is 17.5% for the ML model and Key BER for 2-bit codebook is 32.8% for the non-ML model for Alice-to-Bob transmission. But, for a 4-bit codebook, Key BER is 19.5% for the ML model whereas Key BER is 31.96% for the non-ML model for Alice-to-Bob (DL) transmission. Our analysis proves that compared to the non-ML, existing shared key based PLS model, the proposed ML model with an optimum codebook as well as ML based decoding scheme can perform better and can guarantee a better Key BER performance with security and hence reliability.

Figure 7.

Plot for key BER (raw BER prior to error correction decoding) vs. SNRdB for non-ML and ML based PLS for bob and Alice transmission in presence of eve using a 2 × 2 MIMO for 2-bit and 4-bit codebooks.

Figure 8.

ML model performance for bob and Alice transmission: Plot for key BER (raw BER prior to error correction decoding) vs. SNRdB in 2 × 2 and 4 × 4 MIMO system, using 2-bit codebooks.

Figure 9.

ML model performance for bob and Alice transmission: Plot for key BER (raw BER prior to error correction decoding) vs. SNRdB in 2 × 2 and 4 × 4 MIMO system, using 4-bit codebooks.

Figure 10.

ML model performance for bob-to-Alice transmission: Plot for key BER (raw BER prior to error correction decoding) vs. SNRdB using 5-bit codebooks in 2 × 2 and 4 × 4 MIMO system.

Figure 7 also demonstrates an analysis of Eve’s performance in comparison with Alice to Bob’s transmission for a 2 × 2 MIMO system and a multi-codebook system. The assumption is that Eve has the knowledge of the same, universal, DFT codebook that Alice and Bob uses and that Eve also has an uncorrelated channel. The figure clearly shows that Eve cannot perfectly decode the secret key just by placing herself closer to Bob or Alice. For example, if we focus on a comparative overlook of Alice, Bob and Eve’s performance for 4-bit codebook, it is evident that at a SNR of 25 dB, Alice to Bob transmission achieves perfect secrecy (0 BER), whereas Eve has a Key BER of 38.80% by placing herself closer to Bob and a Key BER of 41.28% by placing herself closer to Alice. Generally, a signal with an SNR value of 20 dB to 25 dB is recommended for data network. It is evident from this analysis that perfect security has been achieved.

Reliability promised by 6G system can also be manifested by achieving improved BER in UL and DL transmission sessions. This can be achieved by increasing antenna number, which is illustrated in Figures 8–10.

Comparing Figures 8 and 9 as well as Figure 10, it is evident that BER in Alice to Bob transmission is compromised with the use of higher codebook as SNR changes from 10 dB to 35 dB, which was also demonstrated in Figure 7. The reason is, higher order codebooks provide with higher number of precoders, hence the precision requirement. Figure 10 demonstrates that for the case of 2 × 2 MIMO, a 5-bit codebook, at SNR = 10 dB, DL and UL Key BER are 30% and 21.01% respectively. When codebook bit is reduced to 4-bit and eventually to 2-bit as shown in Figures 8 and 9, for the same number of antenna, 2 × 2 MIMO, DL and UL Key BER reduces to 21.80%, 12.10% for 4 bit-codebook and to 20.10%, 10.20% for 2-bit codebook respectively, at SNR = 10 dB. For a 5-bit codebook, 0 Key BER is achieved at 30 dB whereas, for a 4-bit codebook and 2-bit codebook, 0 Key BER can be achieved at even lower SNRs, such as 20 dB and 15 dB respectively.

Key BER can be compensated by increasing the number of MIMO antennas. Figure 8 shows that for a 2-bit codebook, in both UL and DL transmission, increasing transmit and receive antenna number from 2 to 4 decreases Key BER significantly from 10.20% to 0.71% for UL transmission and from 20.10% to 1.43% for DL transmission respectively, at SNR = 10 dB. Similarly, if we compare the results demonstrated in Figures 9 and 10, Key BER improves significantly as we increase number of both transmit and receive antenna arrays, proving better security and reliability of the proposed ML-based PLS model. Figure 11 represents a summarized overview of the ML based PLS system performance over multi-codebooks and MIMO antenna systems at 10 dB SNR:

Figure 11.

Comparison of proposed ml-based system performance demonstrating improved security and reliability in terms of key BER (raw BER prior to error correction decoding) over multi-codebook and MIMO antenna systems.

Figure 11 comparatively analyzes the BER performance for an OFDM system with 64 subchannels. The L subchannels have a 15 kHz subchannel spacing and sampling frequency, $f_s$ of 960 KHz. The bandwidth, $W<f_s$ and cyclic prefix (CP) have been selected to be 25% overhead. The symbol rate, $1/T_{\mathit{\text{symb}}}$ formula can be defined as follows: $\frac{L+\mathit{CP}}{f_s}$. The models focus on a 4-bit codebook scenario, as shown in Figure 11. For the same codebook size, higher MIMO arrays transmits lower rates of data resulting in less spectral efficiency, $\eta$; however higher MIMO sizes also result in improved BER, for uplink and downlink. For a codebook size of 16, a 2 × 2 MIMO results in a BER of 21.80%, 12.10% with a spectral efficiency of 0.76 bits/sec/Hz whereas a 4 × 4 MIMO results in a much improved BER of 10.20%, 9.80% with a spectral efficiency of 0.18 bits/sec/Hz, for downlink and uplink transmission cases respectively.

5. Conclusions

In conclusion, AI/ML-based Physical Layer Security is a promising candidate for a secure 6G. AI/ML can optimize the parameters and contribute to superior transmission and secrecy performance, resulting in high levels of security and reliability in 6G communication. The proposed sApps contribute significantly in reducing latency by operating in the lower layer of communication. The model realized the goal of 6G SURLLC. Future paradigm of our work includes implementing the structure for time varying channel models, higher mobile velocity, and higher levels of security by employing intelligent PLS infrastructures.

Acknowledgments

This research was supported by the National Science Foundation REU program for AI Powered Robotics in 5G Networks, OUSD R&E FutureG Advanced Research, and the UTSA Klesse College of Engineering at the University of Texas at San Antonio.

Acronyms definitions

PLS	Physical Layer Security
ORAN	Open Radio Access Network
AI	Artificial Intelligence
ML	Machine Learning
BER	Bit Error Rate
6G	Sixth Generation Wireless
XR	eXtended Reality
5G	Fifth Generation Wireless
IoT	Internet of Things
IoE	Intelligence of Everything
mmWave	Milimeter Wave
eMBB-Plus	Enhanced Mobile Broadband Plus
uHSLLC	Ultra-High-Speed with Low Latency Communication
SURLLC	Secure Ultra-Reliable Low-Latency Communication
M2M	Machine-to-Machine
ITU	International Telecommunication Union
3GPP	Third Generation Partnership Project
DL	Deep Learning
TDD	Time Division Duplex
AN	Artificial Noise
FFNN	Feed Forward Neural Network
PMI	Precoding Matrix Index
DNN	Deep Neural Network
SVD	Singular Value Decomposition
nGRG	next Generations Research Group
O-RU	O-RAN Radio Unit
O-DU	O-RAN Distributed Unit
O-CU	O-RAN Centralized Unit
RE	Resource Element
O-FH	Open Fronthaul
RIC	Radio Intelligent Controller
SMO	Service Management and Orchestration
O-CU-CP	O-CU Control Plane
O-CU-UP	O-CU User Plane
SM	Service Model
DL	Downlink
UL	Uplink