Internet of things: A survey on enabling technologies, application and standardization

Wireless transceivers for mass-market applications must be cost effective. We may achieve this goal by deploying non-ideal low-cost radio frequency (RF) analog components. However, their imperfections may result in RF impairments, including phase noise (PN), carrier frequency offset (CFO), and in-phase (I) and quadrature-phase (Q) imbalance. These impairments introduce in-band and out-of-band interference terms and degrade the performance of wireless systems. In this survey, we present RF-impairment signal models and discuss their impacts. Moreover, we review RF-impairment estimation and compensation in single-carrier (SC) and multicarrier systems, especially orthogonal frequency division multiplexing (OFDM). Furthermore, we discuss the effects of the RF impairments in already-established wireless technologies, e.g., multiple-input multiple-output (MIMO), massive MIMO, full-duplex, and millimeter-wave communications and review existing estimation and compensation algorithms. Finally, future research directions investigate the RF impairments in emerging technologies, including cell-free massive MIMO communications, non-orthogonal multicarrier systems, non-orthogonal multiple access (NOMA), ambient backscatter communications, and intelligent reflecting surface (IRS)-assisted communications. Furthermore, we discuss artificial intelligence (AI) approaches for developing estimation and compensation algorithms for RF impairments.

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RF Impairments in Wireless Transceivers: Phase Noise, CFO, and IQ Imbalance – A Survey

While 5G is well-known for network cloudification with micro-service based architecture, the next generation networks or the 6G era is closely coupled with intelligent network orchestration and management. Hence, the role of Artificial Intelligence (AI) is immense in the envisioned 6G paradigm. However, the alliance between 6G and AI may also be a double-edged sword in many cases as AI's applicability for protecting or infringing security and privacy. In particular, the end-to-end automation of future networks demands proactive threats discovery, application of mitigation intelligent techniques and making sure the achievement of self-sustaining networks in 6G. Therefore, to consolidate and solidify the role of AI in securing 6G networks, this article presents how AI can be leveraged in 6G security, possible challenges and solutions.

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AI and 6G Security: Opportunities and Challenges

Defensive and offensive capabilities are both significant in communication confrontation games. By exploiting the above two capabilities, a new confrontation mechanism in the spectrum domain between two opposing teams denoted as the blue team (BT) and red team (RT), is designed. The basic idea is that by sacrificing parts of ally performance to severely deteriorate the opponent side communications. Specifically, a friendly and smart jammer (assuming in the BT) is deployed to weaken opponent (i.e., members in the RT) communications without causing great damages to other BT members, while the smart RT members try to evade the jamming and alleviate mutual interference. The interactions among the friendly jammer and other nodes are modeled as a Stackelberg game, with each player seeking for their respective utility maximization. We prove that each sub-game is an exact potential game. To efficiently search for the equilibrium solutions, a parallel log-linear learning algorithm is proposed, based on which each user intelligently decides their spectrum access policies. Numerical results demonstrate that: 1) RT communications are effectively suppressed; meanwhile, mutual interference among ally BT communication pairs is significantly alleviated; 2) the proposed algorithm achieves a close-to-optimal solution; 3) compared with the current state of solutions, i.e., random selection, stochastic learning automata, our algorithm performs better in terms of both utility and convergence.

A Learning-Based Spectrum Access Stackelberg Game: Friendly Jammer-Assisted Communication Confrontation

Non-orthogonal multiple access (NOMA) has been recently proposed as a viable technology that can potentially improve the spectral efficiency of fifth generation wireless networks and beyond. However, in practical communication scenarios, transceiver architectures inevitably suffer from radio-frequency (RF) front-end related impairments that can lead to degradation of the overall system performance with in-phase/quadrature-phase imbalance (IQI) constituting a major impairment in direct-conversion transceivers. In this paper, we quantify the effects of IQI on the performance of NOMA-based single-carrier (SC) and multi-carrier (MC) systems under multipath fading conditions. This is realized by first deriving analytic expressions for the signal-to-interference-plus-noise ratio and the outage probability of both SC and MC NOMA systems subject to IQI at the transmitter and/or receiver (RX) side. Furthermore, we derive asymptotic diversity orders for all considered impairment scenarios. Capitalizing on these results, we demonstrate that the effects of IQI differ considerably among NOMA users and depend on the underlying systems’ parameters. For example, for a target data rate and power allocation ratio satisfying a given condition, IQI does not affect the asymptotic diversity of SC NOMA systems, whereas the asymptotic diversity of MC NOMA systems, suffering from RX IQI, is always zero. Moreover, it is shown that for both SC and MC NOMA systems, the first sorted user appears more robust to IQI, which indicates that higher order users are more sensitive to the considered impairment.

Performance Analysis of Non-Orthogonal Multiple Access Under I/Q Imbalance

The open nature of the wireless communication medium renders it vulnerable to jamming attacks by malicious users. To detect their presence and to avoid such attacks, several techniques are present in the literature. Most of these techniques aim to reduce the effect of the jamming signals by increasing the transmission power or by using complex coordination schemes. However, the implementation of such power consuming techniques might be challenging or not feasible in limited resources Internet-of-Things (IoT) devices. Therefore, a defending strategy against jamming attacks in health monitoring IoT networks is proposed in this article. This strategy operates in orthogonal frequency-division multiplexing channels and takes into consideration the effect of slow fading channels in the strategy design. Specifically, the jamming combating problem is formulated as a Colonel Blotto game where the equilibrium defines the minimization of the worst case jamming effect on the IoT sensors communications. Then, the optimal power allocation strategy for all the potential jammer power ranges is derived by investigating the Nash equilibrium of the game. This proposed strategy is shown to be efficient in combating jamming attacks while minimizing the IoT sensors power consumption.

Game Theory for Anti-Jamming Strategy in Multichannel Slow Fading IoT Networks

In this contribution, we investigate the outage performance of OFDM-based Decode and Forward (DF), Amplify and Forward (AF) and Controlled DF (CDF) cooperative systems under IQ Imbalance (IQI) In particular, tractable and compact approximate outage probability expressions are derived and the effect of the different IQI parameters is analyzed for different SNR ranges Furthermore, by localizing the error floor in terms of IQI and SNR for each technique, we demonstrate when it is more beneficial to invest in increasing the transmission power or in compensating the imbalance Moreover, we prove that the IQI compensation should be concentrated in the relay for some techniques and in the destination for some others A comparative study between AF, DF, CDF and direct link transmission techniques is also conducted for different IQI parameters, SNR ranges, transmission rates and relay's position Hence, this work may create a paradigm for future studies of more effective adaptive IQI compensation techniques that concentrate the compensation on the right IQI, SNR ranges, transceivers depending on the used transmission technique

Outage Performance of Cooperative Systems Under IQ Imbalance

Recently, machine learning algorithms have been proposed to design new security systems for anomalies detection as they exhibit fast processing with real-time predictions. However, one of the major challenges in machine learning-based intrusion detection methods is how to include enough training examples for all the possible classes in the model to avoid the class imbalance problem and accurately detect the intrusions and their types. in this article, we propose a novel weighted classes classification scheme to secure the network against malicious nodes while alleviating the problem of imbalanced data. in the proposed system, we combine a supervised machine learning algorithm with the network node past information and a specific designed best effort iterative algorithm to enhance the accuracy of rarely detectable attacks. The machine learning algorithm is used to generate a classifier that differentiates between the investigated attacks. The system stores these decisions in a private database. Then, we design a new weight optimization algorithm that exploits these decisions to generate a weights vector that includes the best weight for each class. The proposed model enhances the overall detection accuracy and maximizes the number of correctly detectable classes even for the classes with a relatively low number of training entries. The UNSW dataset has been used to evaluate the performance of the proposed model and compare it with state of the art techniques.

Machine Learning Based Cloud Computing Anomalies Detection

The Internet of things (IoT) is one of the most exposed networks to attackers due to its widespread and its heterogeneity. In such networks, jamming attacks are widely used by malicious users to compromise the private and secure communications. Many techniques are proposed in the literature to secure the network from malicious jamming attacks. However, most of these techniques require either the implementation of complex coordination schemes or the use of high transmission power and are therefore challenging to implement in limited resources IoT networks. In this paper, a low complexity anti-jamming defending strategy using smart power allocation under limited power constraints is proposed for health monitoring IoT networks. This strategy is designed by formulating the worst case jamming effect minimization problem as a Colonel Blotto game while considering the slow channel fading effect. By analyzing the Nash Equilibrium (NE) of the game, making use of efficient and fast equilibrium approximation techniques, designing a fast numerical solving approach, training an artificial neural network (ANN) to enhance the accuracy of the estimation, an anti-jamming power allocating strategy is proposed and is shown to be effective in reducing the power consumption and in combating jamming attacks with less resources. A data population scheme is also proposed to make the proposed ANN exploit as much possible the available data to provide accurate NE estimation.

IoT Anti-Jamming Strategy Using Game Theory and Neural Network

In this contribution, we investigate Decode and Forward (DF) OFDM Ad-hoc Routing strategy with bottleneck maximization We derive the exact outage probability and diversity order for the different scenarios: with and without subcarrier grouping and with and without joint selection, where joint selection refers to the selection of the last two hops together When joint selection is performed, numerical results show that a power gain can be obtained when using subcarrier grouping technique

Ala Gouissem

Papers

Game Theory for Anti-Jamming Strategy in Multichannel Slow Fading IoT Networks

Outage Performance of Cooperative Systems Under IQ Imbalance

Machine Learning Based Cloud Computing Anomalies Detection

IoT Anti-Jamming Strategy Using Game Theory and Neural Network

Outage Performance of OFDM Ad-Hoc Routing with and without Subcarrier Grouping in Multihop Network