This paper investigates the security performance of a cooperative multicast-unicast system, where the users present the feature of high mobility. Specifically, we develop the non-orthogonal multiple access (NOMA) based orthogonal time frequency space (OTFS) transmission scheme, namely NOMA-OTFS, in order to combat Doppler effect as well as to improve the spectral efficiency. Further, we propose a power allocation method addressing the trade-off between the reliability of multicast streaming and the confidentiality of unicast streaming. Based on that, we utilize the relay selection strategy, to improve the security of unicast streaming. In the context of multicast-unicast streaming, our simulation findings validate the effectiveness of the NOMA-OTFS based cooperative transmission, which can significantly outperform the existing NOMA-OFDM in terms of both reliability and security.
Since orthogonal time-frequency space (OTFS) can effectively handle the problems caused by Doppler effect in high-mobility environment, it has gradually become a promising candidate for modulation scheme in the next generation of mobile communication. However, the inter-Doppler interference (IDI) problem caused by fractional Doppler poses great challenges to channel estimation. To avoid this problem, this paper proposes a joint time and delay-Doppler (DD) domain based on sparse Bayesian learning (SBL) channel estimation algorithm. Firstly, we derive the original channel response (OCR) from the time domain channel impulse response (CIR), which can reflect the channel variation during one OTFS symbol. Compare with the traditional channel model, the OCR can avoid the IDI problem. After that, the dimension of OCR is reduced by using the basis expansion model (BEM) and the relationship between the time and DD domain channel model, so that we have turned the underdetermined problem into an overdetermined problem. Finally, in terms of sparsity of channel in delay domain, SBL algorithm is used to estimate the basis coefficients in the BEM without any priori information of channel. The simulation results show the effectiveness and superiority of the proposed channel estimation algorithm.
Orthogonal time frequency space (OTFS), as a novel 2-D modulation technique, has been proposed to achieve better BER performances over delay-Doppler channels. In this paper, we propose two different power allocation (PA) algorithms in OTFS systems with zero forcing (ZF) or minimum mean square error (MMSE) equalization, where general formulas with PA are derived in advance under the condition of minimum BER (MBER) criterion. On one hand, a suboptimal MBER power allocation method is put forward to achieve better BER performances, and then analytical BER expressions are derived with proposed PA strategy. Considering the case of MMSE equalization, a combined subsymbol allocation (SA) and PA strategy is raised, where some subsymbols may be abandoned due to worse channel conditions, and then it is proven effectively to improve BER performances through theoretical and simulation results. Furthermore, BER performances with proposed joint SA and PA strategy are also investigated in delay-Doppler channels, where an improved message passing (MP) receiver based on equivalent channel matrix with PA is given.
Handover authentication in high mobility scenarios is characterized by frequent and short-term parallel execution. Moreover, the penetration loss and Doppler frequency shift caused by high speed also lead to the deterioration of network link quality. Therefore, high mobility scenarios require handover schemes with less handover overhead. However, some existing schemes that meet this requirement cannot provide strong security guarantees, while some schemes that can provide strong security guarantees have large handover overheads. To solve this dilemma, we propose a privacy-preserving handover authentication scheme that can provide strong security guarantees with less computational cost. Based on Orthogonal Time Frequency Space (OTFS) link and Key Encapsulation Mechanism (KEM), we establish the shared key between protocol entities in the initial authentication phase, thereby reducing the overhead in the handover phase. Our proposed scheme can achieve mutual authentication and key agreement among the user equipment, relay node, and authentication server. We demonstrate that our proposed scheme can achieve user anonymity, unlinkability, perfect forward secrecy, and resistance to various attacks through security analysis including the Tamarin. The performance evaluation results show that our scheme has a small computational cost compared with other schemes and can also provide a strong guarantee of security properties.
This paper addresses sparse channels estimation problem for the generalized linear models (GLM) in the orthogonal time frequency space (OTFS) underwater acoustic (UWA) system. OTFS works in the delay-Doppler domain, where time-varying channels are characterized as delay-Doppler impulse responses. In fact, a typical doubly spread UWA channel is associated with several resolvable paths, which exhibits a structured sparsity in the delay-Doppler domain. To leverage the structured sparsity of the doubly spread UWA channel, we develop a structured sparsity-based generalized approximated message passing (GAMP) algorithm for reliable channel estimation in quantized OTFS systems. The proposed algorithm has a lower computational complexity compared to the conventional Bayesian algorithm. In addition, the expectation maximum algorithm is employed to learn the sparsity ratio and the noise variance. Simulation and experimental results show that the proposed algorithm has superior performance and low computational complexity for quantized OTFS systems.
Orthogonal Time Frequency Space (OTFS) signaling with index modulation (IM) is a promising transmission scheme characterized by high transmission efficiency for high mobility scenarios. In this paper, we study the receiver for coded OTFS-IM system. First, we construct the corresponding factor graph, on which the structured prior incorporating activation pattern constraint and channel coding is devised. Then we develop a iterative receiver via structured prior-based hybrid belief propagation (BP) and expectation propagation (EP) algorithm, named as Str-BP-EP, for the coded OTFS-IM system. To reduce the computational complexity of discrete distribution introduced by structured prior, Gaussian approximation conducted by EP is adopted. To further reduce the complexity, we derive two variations of the proposed algorithm by using some approximations. Simulation results validate the superior performance of the proposed algorithm.
Orthogonal time frequency space (OTFS) modulation has been proven to be superior to traditional orthogonal frequency division multiplexing (OFDM) systems in high-speed communication scenarios. However, the existing channel estimation sche- mes may results in poor peak to average power ratio (PAPR) performance of OTFS system or low spectrum efficiency. Hence, in this paper, we propose a low PAPR channel estimation scheme with high spectrum efficiency. Specifically, we design a multiple scattered pilot pattern, where multiple low power pilot symbols are superimposed with data symbols in delay-Doppler domain. Furthermore, we propose the placement rules for pilot symbols, which can guarantee the low PAPR. Moreover, the data aided iterative channel estimation was invoked, where joint channel estimation is proposed by exploiting multiple independent received signals instead of only one received signal in the existing scheme, which can mitigate the interference imposed by data symbols for channel estimation. Simulation results shows that the proposed multiple scattered pilot aided channel estimation scheme can significantly reduce the PAPR while keeping the high spectrum efficiency.
Recently, orthogonal time frequency space (OTFS) was presented to alleviate severe Doppler effects in high mobility scenarios. Most of the current OTFS detection schemes rely on perfect channel state information (CSI). However, in real-life systems, the parameters of channels will constantly change, which are often difficult to capture and describe. In this paper, we summarize the existing research on OTFS detection based on data-driven deep learning (DL) and propose three new network structures. The presented three networks include a residual network (ResNet), a dense network (DenseNet), and a residual dense network (RDN) for OTFS detection. The detection schemes based on data-driven paradigms do not require a model that is easy to handle mathematically. Meanwhile, compared with the existing fully connected-deep neural network (FC-DNN) and standard convolutional neural network (CNN), these three new networks can alleviate the problems of gradient explosion and gradient disappearance. Through simulation, it is proved that RDN has the best performance among the three proposed schemes due to the combination of shallow and deep features. RDN can solve the issue of performance loss caused by the traditional network not fully utilizing all the hierarchical information.
The internet of things (IoT) has been widely considered to be integrated with high-speed railways to improve safety and service. It is important to achieve reliable communication in IoT for railways (IoT-R) under high mobility scenarios and strict energy constraints. Orthogonal time frequency space (OTFS) modulation is a two-dimensional modulation technique that has the potential to overcome the challenges in high Doppler environments. In addition, OTFS can have lower peak-to-average power ratio (PAPR) compared to orthogonal frequency division multiplexing, which is especially important for the application of IoT-R. Therefore, OTFS modulation for IoT-R is investigated in this paper. In order to decrease PAPR of OTFS and promote the application of OTFS modulation in IoT-R, the peak windowing technique is used in this paper. This technique can reduce the PAPR of OTFS by reducing the peak power and does not require multiple iterations. The impacts of different window functions, window sizes and clipping levels on PAPR and bit error rate of OTFS are simulated and discussed. The simulation results show that the peak windowing technique can efficiently reduce the PAPR of OTFS for IoT-R.
Unmanned aerial vehicles (UAVs) have attracted growing research interests in recent years, which can be used as cost-effective aerial platforms to transmit collected data packets to ground access points (APs). Thus, it is crucial to investigate robust air-to-ground (A2G) wireless links for high-speed UAVs. However, the A2G wireless link is unstable as it suffers from large path-loss and severe Doppler effect due to the high mobility of UAVs.In order to meet these challenges, we propose an orthogonal time frequency space (OTFS)-based UAV communication system to relief the Doppler effect. Besides, considering that the energy of UAV is limited, we optimize the trajectory planning of UAV to minimize the energy consumption under the constraints of bit error rate (BER) and transmission rate, where the Doppler compensation is taken into account. Simulation results show that the performance of OTFS-based UAV system is superior to orthogonal frequency division multiplexing (OFDM)-based UAV systems, which can accomplish transmission tasks over shorter distances with lower energy consumption.
Integrating the blockchain technology into mobile-edge computing (MEC) networks with multiple cooperative MEC servers (MECS) providing a promising solution to improving resource utilization, and helping establish a secure reward mechanism that can facilitate load balancing among MECS. In addition, intelligent management of service caching and load balancing can improve the network utility in MEC blockchain networks with multiple types of workloads. In this paper, we investigate a learning-based joint service caching and load balancing policy for optimizing the communication and computation resources allocation, so as to improve the resource utilization of MEC blockchain networks. We formulate the problem as a challenging long-term network revenue maximization Markov decision process (MDP) problem. To address the highly dynamic and high dimension of system states, we design a joint service caching and load balancing algorithm based on the double-dueling Deep Q network (DQN) approach. The simulation results validate the feasibility and superior performance of our proposed algorithm over several baseline schemes.
