Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userIndependence-Checking Coding for OFDM Channel Training Authentication: Protocol Design, Security, Stability, and Tradeoff Analysis
67
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
In wireless OFDM communications systems, pilot tones, due to their publicly known and deterministic characteristic, suffer significant jamming/nulling/spoofing risks. Thus, the convectional channel training protocol using pilot tones could be attacked and paralyzed, which raises the issue of anti-attack channel training authentication (CTA), i.e., verifying the claims of identities of pilot tones and channel estimation samples. In this paper, we consider one-ring scattering scenarios with large-scale uniform linear arrays (ULA) and develop an independence-checking coding (ICC) theory to build a secure and stable CTA protocol, namely, ICC-based CTA (ICC-CTA) protocol. In this protocol, the pilot tones are not only merely randomized and inserted into subcarriers but also encoded as diversified subcarrier activation patterns (SAPs) simultaneously. Those encoded SAPs, though camouflaged by malicious signals, can be identified and decoded into original pilots for high-accuracy channel impulse response (CIR) estimation. The CTA security is first characterized by the error probability of identifying legitimate CIR estimation samples. The CTA instability is formulated as the function of probability of stably estimating CIR against all available diversified SAPs. A realistic tradeoff between the CTA security and instability under the discretely distributed AoA is identified and an optimally stable tradeoff problem is formulated, with the objective of optimizing the code rate to maximize security while maintaining maximum stability for ever. Solving this, we derive the closed-form expression of optimal code rate. Numerical results finally validate the resilience of proposed ICC-CTA protocol.
rate research
Read More
Due to the publicly-known deterministic character- istic of pilot tones, pilot-aware attack, by jamming, nulling and spoofing pilot tones, can significantly paralyze the uplink channel training in large-scale MISO-OFDM systems. To solve this, we in this paper develop an independence-checking coding based (ICCB) uplink training architecture for one-ring scattering scenarios allowing for uniform linear arrays (ULA) deployment. Here, we not only insert randomized pilots on subcarriers for channel impulse response (CIR) estimation, but also diversify and encode subcarrier activation patterns (SAPs) to convey those pilots simultaneously. The coded SAPs, though interfered by arbitrary unknown SAPs in wireless environment, are qualified to be reliably identified and decoded into the original pilots by checking the hidden channel independence existing in subcarri- ers. Specifically, an independence-checking coding (ICC) theory is formulated to support the encoding/decoding process in this architecture. The optimal ICC code is further developed for guaranteeing a well-imposed estimation of CIR while maximizing the code rate. Based on this code, the identification error probability (IEP) is characterized to evaluate the reliability of this architecture. Interestingly, we discover the principle of IEP reduction by exploiting the array spatial correlation, and prove that zero-IEP, i.e., perfect reliability, can be guaranteed under continuously-distributed mean angle of arrival (AoA). Besides this, a novel closed form of IEP expression is derived in discretely- distributed case. Simulation results finally verify the effectiveness of the proposed architecture.
Due to the publicly known and deterministic characteristic of pilot tones, pilot authentication (PA) in multi-user multi-antenna orthogonal frequency-division multiplexing systems is very susceptible to the jamming/nulling/spoofing behaviors. To solve this, in this paper, we develop a hierarchical 2-D feature (H2DF) coding theory that exploits the hidden pilot signal features, i.e., the energy feature and independence feature, to secure pilot information coding which is applied between legitimate parties through a well-designed five-layer hierarchical coding model to achieve secure multiuser PA (SMPA). The reliability of SMPA is characterized using the identification error probability (IEP) of pilot encoding and decoding with the exact closed-form upper and lower bounds. However, this phenomenon of non-tight bounds brings about the risk of long-term instability in SMPA. Therefore, a reliability bound contraction theory is developed to shrink the bound interval, and practically, this is done by an easy-to-implement technique, namely, codebook partition within the H2DF code. In this process, a tradeoff between the upper and lower bounds of IEP is identified and a problem of optimal upper and lower bound tradeoff is formulated, with the objective of optimizing the cardinality of sub-codebooks such that the upper and lower bounds coincide. Solving this, we finally derive an exact closed-form expression for IEP, which realizes a stable and highly reliable SMPA. Numerical results validate the stability and resilience of H2DF coding in SMPA.
We present a deep learning based joint source channel coding (JSCC) scheme for wireless image transmission over multipath fading channels with non-linear signal clipping. The proposed encoder and decoder use convolutional neural networks (CNN) and directly map the source images to complex-valued baseband samples for orthogonal frequency division multiplexing (OFDM) transmission. The proposed model-driven machine learning approach eliminates the need for separate source and channel coding while integrating an OFDM datapath to cope with multipath fading channels. The end-to-end JSCC communication system combines trainable CNN layers with non-trainable but differentiable layers representing the multipath channel model and OFDM signal processing blocks. Our results show that injecting domain expert knowledge by incorporating OFDM baseband processing blocks into the machine learning framework significantly enhances the overall performance compared to an unstructured CNN. Our method outperforms conventional schemes that employ state-of-the-art but separate source and channel coding such as BPG and LDPC with OFDM. Moreover, our method is shown to be robust against non-linear signal clipping in OFDM for various channel conditions that do not match the model parameter used during the training.
Supporting reliable and seamless wireless connectivity for unmanned aerial vehicles (UAVs) has recently become a critical requirement to enable various different use cases of UAVs. Due to their widespread deployment footprint, cellular networks can support beyond visual line of sight (BVLOS) communications for UAVs. In this paper, we consider cellular connected UAVs (C-UAVs) that are served by massive multiple-input-multiple-output (MIMO) links to extend coverage range, while also improving physical layer security and authentication. We consider Rician channel and propose a novel linear precoder design for transmitting data and artificial noise (AN). We derive the closed-form expression of the ergodic secrecy rate of C-UAVs for both conventional and proposed precoder designs. In addition, we obtain the optimal power splitting factor that divides the power between data and AN by asymptotic analysis. Then, we apply the proposed precoder design in the fingerprint embedding authentication framework, where the goal is to minimize the probability of detection of the authentication tag at an eavesdropper. In simulation results, we show the superiority of the proposed precoder in both secrecy rate and the authentication probability considering moderate and large number of antenna massive MIMO scenarios.
We investigate joint source channel coding (JSCC) for wireless image transmission over multipath fading channels. Inspired by recent works on deep learning based JSCC and model-based learning methods, we combine an autoencoder with orthogonal frequency division multiplexing (OFDM) to cope with multipath fading. The proposed encoder and decoder use convolutional neural networks (CNNs) and directly map the source images to complex-valued baseband samples for OFDM transmission. The multipath channel and OFDM are represented by non-trainable (deterministic) but differentiable layers so that the system can be trained end-to-end. Furthermore, our JSCC decoder further incorporates explicit channel estimation, equalization, and additional subnets to enhance the performance. The proposed method exhibits 2.5 -- 4 dB SNR gain for the equivalent image quality compared to conventional schemes that employ state-of-the-art but separate source and channel coding such as BPG and LDPC. The performance further improves when the system incorporates the channel state information (CSI) feedback. The proposed scheme is robust against OFDM signal clipping and parameter mismatch for the channel model used in training and evaluation.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
