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Orthogonal frequency division multiplexing (OFDM) is such a vital waveform scheme that is utilized in fifth-generation (5G) communication systems as well as the previous one. Since the usage scenarios of 5G are quite wide, the security of those systems is currently an open and critical issue. In this paper, a promising paradigm is proposed for enhancing physical layer (PHY) security in OFDM systems. Two independent secret keys are extracted by legitimate users quantizing the channel measurements. Those key sequences are exchanged via a novel artificial noise (AN) injection method which does not result in a key mismatch. Bit error rate (BER) versus signal-to-noise ratio (SNR) performance of the adversary is still low even if the malicious node has better channel than the legitimate user. After agreement upon the same key, both legitimate parties directly employ key sequence and inter-carrier interference matrix caused by the local oscillators to produce a secrecy matrix. This matrix enables secure communication by inserting it into the transmission design. Huawei ; Nokia ; Pix Moving ; Samsung ; Technology Innovation Institute (TII)

Mutual interference is a crucial issue for the visible light communication (VLC) systems to be addressed, including both co-channel interference (CCI) and inter-carrier interference (ICI). In this paper, to solve the CCI problem, a novel optical attocell network is introduced, which can be regarded as a hybrid network based on both single frequency network and multi-frequency network. Meanwhile, to eliminate the ICI, a pre-distorted enhanced asymmetrically clipped DC biased optical orthogonal frequency division multiplexing (PEADO-OFDM) approach is designed. In addition, the corresponding properties of PEADO-OFDM are analyzed, and different modulation schemes are employed for downlink and uplink in the same ADO-OFDM modulation framework to reduce the complexity of user processing. Simulation results indicate that the proposed PEADO-OFDM scheme outperforms the conventional ADO-OFDM approach in terms of bit error rate, while the processing latency and computational complexity are greatly reduced at the receiver.

This paper studies the application of physical-layer network coding (PNC) in vehicle-to-everything communications to accommodate the time-critical nature of vehicular ad-hoc networks (VANETs). The idea can theoretically reduce the transmission latency by 50%, thus alleviating the short contact time issues caused by high-speed vehicle motion. Conventional studies of PNC primarily considered static networks. In highly mobile networks like VANETs, the carrier frequency offsets (CFOs) due to high-speed motion will lead to inter-carrier interference (ICI) in orthogonal frequency division multiplexing (OFDM) systems. Moreover, the vehicular environment with time–frequency-selective channels further undermines accurate channel estimation for multiple users. It is also worth noting that the CFO that exists in OFDM modulated PNC cannot be completely eliminated through CFO tracking and equalization as in conventional point-to-point transmissions. These critical issues can significantly increase the bit error rate at the receiver. To address these challenges, this paper proposes an ICI-aware approach that jointly achieves accurate channel estimation, signal detection, and channel decoding. We express the channel estimation and detection and decoding as two optimization problems and resolve them with the expectation–maximization algorithm and the belief propagation algorithm, respectively. The proposed approach can efficiently mitigate the negative effect of ICI by exploiting both pilot and data tones in channel estimation, detection, and decoding. Both simulation and experiment are conducted to evaluate the proposed approach, and the results reveal that the proposed algorithm outperforms the benchmark that simply treats ICI as Gaussian noise.

Orthogonal time frequency space (OTFS) is a promising alternative to orthogonal frequency division multiplexing (OFDM) in high-mobility beyond 5G communications. In this paper, we consider the problem of radar sensing with OTFS joint radar-communications waveform and derive a novel OTFS radar signal model by explicitly taking into account the intersymbol interference (ISI) and inter-carrier interference (ICI) effects. On the basis of the new model, we show how ISI and ICI phenomena can be turned into an advantage to surpass the maximum unambiguous detection limits in range and velocity, arising in existing OFDM and OTFS radar systems. Moreover, we design a generalized likelihood ratio test based detector/estimator that can embrace ISI and ICI effects. Simulation results illustrate the potential of embracing ISI/ICI and demonstrate its superior detection and estimation performance over conventional baselines., Comment: Accepted to ICC 2021, Workshop on OTFS for 6G and Future High-Mobility Communications

For OFDM systems, the time variation of a multipath channel results in inter-carrier interference (ICI). It leads to performance degradations. In reducing the problem, current successive detection methods cost very high computationalcomplexities. Among them, the minimum-mean squareerror successive detection (MMSE-SD) method has the best performance. This work proposes an improved data detection method with low complexity by integrating the techniques of Newton’s iterative matrix inversion method and the MMSE-SD method which considers the effects of signal-to-interference-plusnoise ratio (SINR). In order to efficiently integrate Newton’s and MMSE-SD method, we develop an effective scheme with low complexity for generating the initial values required by Newton’s method. Based on the new initial value scheme, we are able tosimplify the criterion of maximum SINR determination to an equivalent one with lower complexity. As a result, the proposed algorithm has a much lower complexity of O(N2) than O(N3) of the MMSE-SD algorithm, where N is the number of subcarriers. Moreover, simulation results in different channel conditions show that performances of the proposed MMSE-SD method are very close to the MMSE-SD method.