Angle-of-arrival (AoA) has been utilized as a reliable feature for physical layer authentication (PLA), as impersonation attacks require restrictive spatial and signal conditions. However, in configurations where the legitimate and adversary users are spatially close or aligned, AoA-based authentication may fail. This paper investigates enhanced sensing-based authentication using channel state information (CSI). We develop a phase-correction pipeline to sanitize CSI and propose antenna subarray aggregation across frequencies, drastically improving AoA estimation. Moreover, we show that by leveraging sensing parameters such as time-of-flight (ToF), received signal strength indicator (RSSI), impersonation attacks can be accurately revealed where AoA performs poorly. Our proposed scheme is validated using a real dataset collected on the Nokia campus in Stuttgart, Germany. Experimental results show that, in addition to the highly accurate AoA estimation, proximal impersonation attacks can be detected with an accuracy of 94.4-100%. These findings demonstrate that spatial and frequency-domain processing of CSI enables effective, hardware-friendly, robust PLA through attacks detection.

In this paper, we investigate the pertinence of the angle of arrival (AoA) as a feature for robust physical layer authentication (PLA). While most of the existing approaches to PLA focus on amplitude-dependent features of the physical layer of communication channels, such as channel frequency response, channel impulse response, or received signal strength, the use of AoA in this domain has not yet been studied in depth, particularly regarding the ability to thwart spoofing (impersonation) attacks. In this work, we demonstrate that an impersonation attack targeting AoA-based PLA is only feasible under strict conditions on the attackers location, which highlights the AoA’s role as a strong feature for unspoofable PLA, especially when 2D AoA is employed.We extend previous works considering a single-antenna attacker to the case of a multiple-antenna attacker, and we develop a theoretical characterization of the conditions under which a successful impersonation attack can be mounted. Furthermore, we have performed extensive simulations in support of theoretical analyses, to validate the robustness of AoA-based PLA.

Accurate and reliable localization is a key requirement for 6G network operations, but it can be particularly challenging in outdoor environments. In this paper, we propose a machine learning (ML)–based localization framework that leverages angle of arrival (AoA) as a feature extracted from channel state information (CSI). The proposed approach employs high-resolution AoA estimation algorithms, including multiple signal classification (MUSIC) and estimation of signal parameters via rotational invariance techniques (ESPRIT), which feed a hierarchical, two-stage classifier to identify specific trajectories (hereafter referred to as tracks) in a given outdoor environment. The first stage of the classifier is a binary line-of-sight (LoS) / non-line-of-sight (NLoS) classifier, followed by region-specific multi-class classifiers for fine-grained identification of the specific LoS or NLoS tracks. We evaluate our approach using a real-world massive multiple-input multiple-output (mMIMO) orthogonal frequency division multiplexing (OFDM) outdoor CSI dataset collected at the Nokia campus in Stuttgart, Germany. Experimental results show that i) the LoS / NLoS identification accuracy can reach 100%, and ii) the proposed two-stage approach significantly outperforms a single-stage multi-class baseline, achieving accuracy over 98% in LoS regions and 95% in NLoS regions. These findings demonstrate the potential of combining AoA with ML for robust localization in outdoor mMIMO propagation environments.

Open radio access network (O-RAN) is one of the key solutions envisaged to meet the ever increasing and stringent quality of service (QoS) demands of emerging applications and services supported by beyond 5G networks. In addition to meeting these QoS demands, O-RAN also promotes vendor diversity and interoperability by disaggregating the radio access network (RAN) into different units, namely the central unit (CU), distributed unit (DU), and radio unit (RU). However, the disaggregation introduces a new challenge of ensuring seamless interoperability between the various units with a view to ensuring that the QoS of the user equipments (UEs) is met and the energy consumption of the system is managed efficiently. In this paper, we focus on jointly optimizing UE association and RU energy consumption. We express the problem as an optimization problem and propose a scheme that determines the optimal RU among potential RUs that a UE should be associated to while minimizing the total used RUs. Moreover, we employ a power control mechanism that enables idle RUs without associated UEs to be put in sleep mode. To show the efficiency of the proposed algorithm, we evaluated it against some state of the art (SoA) solutions. The results indicate that the proposed solution minimizes energy consumption by about 171 % and 32 % under low and high number of UEs, respectively, while incurring a paltry 3.2% increase in access network delay for UEs without violating their delay requirements.

In this paper, we study the performance of a practical secret key generation method under low-mobility scenarios. Instead of relying on traditional cryptographic methods or leveraging spatial diversity and reconfigurable intelligent surfaces to increase channel variations, we utilize locally generated pilots to add randomness to the system, thus in turn helping to increase the secret key rate. The results demonstrate significant improvements over the original channels, whose entropy source mainly relies on mobility and channel variations. More importantly, this scheme works well without extra helpers or multiple antennas, thus providing a potential for developing reliable, lightweight security solutions for resource-constrained devices in practice.

Cyber–physical multiagent systems are driving rapid technological advancements that automate a wide range of critical functions, thereby enabling safer, more accessible, and more efficient autonomous operations across diverse sectors. We refer to the capability of such systems to self-organize and coordinate toward accomplishing shared objectives as autonomy. The unique characteristics of these systems prompt a reevaluation of their security concepts, including their vulnerabilities, and mechanisms to mitigate these vulnerabilities. This survey article examines how advancements in wireless networking, coupled with sensing and computing capabilities, can foster novel security concepts for autonomous cyber–physical systems (CPSs). It delves into three main themes related to securing multiagent CPSs. First, we discuss the threats that are particularly relevant to multiagent CPSs, given the potential lack of trustworthiness between agents. Second, we present prospects for sensing, contextual awareness, and authentication, enabling the inference and measurement of a form of interagent “quantitative trust” or “cy-trust” for these systems. Third, we elaborate on the application of quantifiable trust notions to enable “resilient coordination,” where “resilient” signifies sustained functionality amid attacks on multiagent CPSs. This survey unveils the cyber–physical character of future interconnected systems as a pivotal catalyst for realizing robust autonomy.

In this paper, we investigate the secret key generation (SKG) for static scenarios utilizing the pilot randomization method. In fact, the majority have studied SKG under dynamic scenarios in which a high secret key rate is achievable due to sufficiently high randomness. In this study, we instead consider the static scenario which, though important, is not well-studied. More specifically, we utilize the pilot randomization method, which is known to prevent injection attacks effectively, to randomize the associated channels. More importantly, we derive the secret key rate of the system and demonstrate that the secret key rate of a static system can increase significantly due to the added randomness. The proposed approach is demonstrated against known methods to show its effectiveness in increasing the secret key rate in static environments.

User authentication in future wireless communication networks is expected to become more complicated due to their large scale and heterogeneity. Furthermore, the computational complexity of classical cryptographic approaches based on public key distribution can be a limiting factor for using in simple, low-end Internet of things (IoT) devices. This paper proposes physical layer authentication (PLA) expected to complement existing traditional approaches, e.g., in multi-factor authentication protocols. The precision and consistency of PLA is impacted because of random variations of wireless channel realizations between different time slots, which can impair authentication performance. In order to address this, a method based on error-correcting codes in the form of reconciliation is considered in this work. In particular, we adopt distributed source coding (Slepian-Wolf) reconciliation using polar codes to reconcile channel measurements spread in time. Hypothesis testing is then applied to the reconciled vectors to accept or reject the device as authenticated. Simulation results show that the proposed PLA using reconciliation outperforms prior schemes even in low signal-to-noise ratio scenarios.

In many countries, energy suppliers are not able to cover the whole country. As a result, the involvement of private individuals in the production of (renewable) energy seems essential to promote the economic development of remote areas. This paper proposes a system that allows individuals to buy and sell energy according to their needs and production, without the need to deploy a centralized server. It is based on Blockchain technology, Edge computing and a set of algorithms designed to automate decentralized peer- to- peer energy exchange. An Open Source implementation of the solution, based on the two most widely used blockchain technologies today (Hyperledger and Ethereum), is available online to demonstrate the relevance of this approach and enable its collaborative improvement.

Network slicing is a promising solution to handle the multiple use cases of the 5G system with their diverse requirements. However, applying a static slicing scheme could cause waste of resource and affect the performance. Therefore, dynamic slicing and resource management are necessary to manage the network's resources efficiently among different slices fulfilling their respective requirements. Network slicing approaches proposed in the literature usually consider a small number of slices and do not account for the numerous 5G services simultaneously. In this paper, we study the network slicing problem at the level of the network's computing and storage resources at the edge to support a large number of slices. We model the network and formulate the slicing problem as an integer linear program to maximize the estimated volume of accepted requests of diverse services. We propose a deep reinforcement learning (DRL) based approach to find a near-optimal solution for the NP-hard optimization problem. We assess the performance and the convergence of the proposed algorithm considering multiple configurations. Moreover, we apply the results to a heuristic task offloading that employs network slicing and compare the performance with static slicing. The simulation results show, i) the impact and importance of the parameters during the model's training and building, ii) the proposed algorithm's efficiency in presence of a large number of slices, iii) the importance of dynamic resource management in network slicing.

Physical layer security (PLS) is seen as the means to enhance physical layer trustworthiness in 6G. This work provides a proof-of-concept for one of the most mature PLS technologies, i.e., secret key generation (SKG) from wireless fading coefficients during the channel's coherence time. As opposed to other works, where only specific parts of the protocol are typically investigated, here, we implement the full SKG chain in four indoor experimental campaigns. In detail, we consider two legitimate nodes, who use the wireless channel to extract secret keys and a malicious node placed in the immediate vicinity of one of them, who acts as a passive eavesdropper. To estimate the final SKG rate we evaluate the conditional min-entropy by taking into account all information available at the eavesdropper. Finally, we use this paper to announce the first ever physical layer security challenge, mirroring practices in cryptography. We call the community to scrutinize the presented results and try to “break” our SKG implementation. To this end, we provide, i) the full dataset observed by the eavesdroppers, ii) 20 blocks of 16 - byte long ciphertexts, encrypted using one-time pad with 20 distilled secret keys, and, iii) all codes and software used in our SKG implementation. An attack will be considered successful if any part(s) of the plaintext are successfully retrieved.

Authenticating the communications among drones operating as a network (or a swarm) is crucial for the control of the network. When drones are in turn supporting communications with other ground devices (e.g., in non-terrestrial networks), all nodes in the network need to be authenticated for end-to-end security. The absence of a reliable fixed network architecture among drones, which are only connected by wireless links, calls for new authentication mechanisms that can complement or be used as alternatives to those offered by cryptography. We propose a challenge-response (CR) physical-layer authentication (PLA) mechanism, where, upon a transmission request from a transmitting drone, referred to as Alice, Bob either asks Alice to move in a specific (randomly chosen) position or moves to a (randomly chosen) position: in both cases, changes in the propagation environment are controlled by Bob. Then, the message is transmitted and Bob estimates the channel from the received signal and verifies that it is compatible with the positions assumed by Alice and Bob. Note that Bob may represent a group of drones that cooperate for authentication. We discuss several security challenges to this CR PLA mechanism and compare them with existing approaches. Preliminary results on the performance of the proposed authentication scheme are presented, showing the advantage of the CR PLA approach.

The massive deployment of low-end wireless Internet of things (IoT) devices opens the challenge of finding de-centralized and lightweight alternatives for secret key distribution. A possible solution, coming from the physical layer, is the secret key generation (SKG) from channel state information (CSI) during the channel's coherence time. This work acknowledges the fact that the CSI consists of deterministic (predictable) and stochastic (unpredictable) components, loosely captured through the terms large-scale and small-scale fading, respectively. Hence, keys must be generated using only the random and unpredictable part. To detrend CSI measurements from deterministic components, a simple and lightweight approach based on Kalman filters is proposed and is evaluated using an implementation of the complete SKG protocol (including privacy amplification that is typically missing in many published works). In our study we use a massive multiple input multiple output (mMIMO) orthogonal frequency division multiplexing outdoor measured CSI dataset. The threat model assumes a passive eavesdropper in the vicinity (at 1 meter distance or less) from one of the legitimate nodes and the Kalman filter is parameterized to maximize the achievable key rate.

In this paper, we study the use of the angle of arrival (AoA) as a feature for performing robust, machine learning (ML)-based physical layer authentication (PLA). In fact, whereas most previous research on PLA relies on physical properties such as channel frequency/impulse response or received signal strength, the use of the AoA in this context has not yet been studied in depth as a means of providing resistance to impersonation (spoofing) attacks. In this study, we first prove that an effective impersonation attack on AoA-based PLA can only succeed under very stringent conditions on the attacker in terms of location and hardware capabilities, and thus, the AoA can in many scenarios be used as a robust feature for PLA. In addition, we exploit machine learning in our study to perform lightweight, model-free, intelligent PLA. We show the effectiveness of the proposed AoA-based PLA solutions by testing them on experimental outdoor massive multiple input multiple output data.

In this paper, we investigate the optimal power allocation at the MAC layer in both the asymptotic and the finite blocklength regimes in a two-user NOMA uplink network, under statistical delay constraints captured through the link-layer rate. Using the link-layer effective rate analytic expressions, we provide closed-form expressions of the optimal power coefficients in low and high signal-to-ratio (SNR) regimes. These are validated by an extensive set of simulations, showing that that the proposed power allocation policy optimizes the performance of NOMA compared to OMA at Layer 2.

Vehicular Edge Computing (VEC) technology allows vehicles demanding significant computation and storage resources to offload their demands to the nearest edge computing node, aiming at reducing data transfer latency and enhancing the Quality of Service (QoS). Moreover, the heterogeneous applications of Vehicle-to-Everything (V2X) communications need an efficient management of the nodes' resources to satisfy the diverse requirements of the vehicles' demands. To this end, network slicing could be a promising solution. Task offloading algorithms in VEC proposed in the literature usually rely on offloading to a 5G base station (gNodeB) or a Road Side Unit (RSU) and do not differentiate between the various vehicular demands. In this paper, we study the task offloading problem with network slicing in V2X communications from vehicles to edge computing nodes hosted at gNodeBs, RSUs, and nearby vehicles. We model the network and formulate the problem as an integer linear program, with the objective of maximizing the volume of offloaded tasks from diverse services. We propose a heuristic algorithm and slicing schemes to find a near-optimal solution to the NP hard optimization problem. The simulation results show that considering offloading at nearby vehicles in addition to RSUs and gNodeBs yields better results in terms of acceptance ratio and resource utilization. Furthermore, it is found that it is beneficial to use an adaptive slicing scheme instead of relying on a fixed slicing; in particular, when the number of slices is large.

In this paper, we devise preprocessing schemes to disentangle channel state information (CSI) into predictable and unpredictable components to simultaneously provide two cornerstone security operations. The predictable components are used for node authentication and the unpredictable components for secret key generation (SKG). For the case of SKG, to prevent Eve from exploiting potential spatial, frequency or time correlations with the legitimate users, which would reduce the effective key space through a decrease in the brute force attack size, in this work, we emphasise the need for reducing the spatial correlation (SC) at different transmitter locations. We also study the trade-off between SC and reconciliation in the uplink and the downlink. Furthermore, we discuss the importance of a more robust criterion - independence - over decorrelation between the legitimate users and eavesdroppers. Finally, we propose a metric for quantifying uniqueness in the predictable components for node authentication, using the total variation distance (TVD).

Software-defined networking (SDN) was devised to simplify network management and automate infrastructure sharing in wired networks. These benefits motivated the application of SDN in resource-constrained wireless networks to leverage solutions for complex applications. However, some of the core SDN traits expose the networks to Denial-of-Service (DoS) attacks. There are proposals in the literature to detect DoS in wireless SDN networks; however, not without shortcomings: there is little focus on resource constraints, high detection rates have been reported mostly for small networks and the detection is disengaged from the identification of the type of attack or the attacker. Our work targets these shortcomings by introducing a lightweight, online change point detector to monitor performance metrics that are impacted when the network is under attack. A key novelty is that the proposed detector is able to operate in either centralized or distributed mode. The centralized detector has very high detection rates and can further distinguish the type of attack from a list of known attacks. In turn, the distributed detector can be useful to identify the nodes launching the attack. Our proposal is tested over IEEE 802.15.4 networks. The results show detection rates exceeding 96% in networks of 36 and 100 nodes and identification of the type of attack with a probability exceeding 89% when using the centralized approach.

We consider a semi-deterministic wiretap channel where the main channel is noiseless and the eavesdropper’s channel is a binary erasure channel (BEC). We provide a lower bound for the achievable secrecy rates of polar and Reed-Muller codes, and compare it to the second order coding rate. To the best of our knowledge, this is the first work which demonstrates the secrecy performance of polar and Reed-Muller codes in short blocklengths. The results show that under a total variation secrecy metric, Reed-Muller codes can achieve secrecy rates very close to the second order approximation rate. On the other hand, we observe a significant gap between the lower bound for the achievable rates of polar codes and the the second order approximation rate for short blocklengths.

In the fifth generation and beyond (B5G), delay constraints emerge as a topic of particular interest, e.g. for ultra-reliable low latency communications (URLLC) such as autonomous vehicles and enhanced reality. In this paper, we study the performance of a two-user uplink NOMA network under statistical quality of service (QoS) delay constraints, captured through each user's effective capacity (EC). We propose novel closed-form expressions for the EC of the NOMA users and show that in the high signal to noise ratio (SNR) region, the “strong” NOMA user has a limited EC, assuming the same delay constraint as the “weak” user. We demonstrate that for the weak user, OMA achieves higher EC than NOMA at small values of the transmit SNR, while NOMA outperforms OMA in terms of EC at high SNRs. On the other hand, for the strong user the opposite is true, i.e., NOMA achieves higher EC than OMA at small SNRs, while OMA becomes more beneficial at high SNRs. This result raises the question of introducing “adaptive” OMA/NOMA policies, based jointly on the users' delay constraints as well as on the available transmit power.

Wireless secret key generation (W-SKG) from shared randomness (e.g., from the wireless channel fading realizations), is a well established scheme that can be used for session key agreement. W-SKG approaches can be of particular interest in delay constrained wireless networks and notably in the context of ultra reliable low latency communications (URLLC) in beyond fifth generation (B5G) systems. However W- SKG schemes are known to be malleable over the so called "advantage distillation" phase, during which observations of the shared randomness are obtained at the legitimate parties. As an example, an active attacker can act as a man-in- the-middle (MiM) by injecting pilot signals and/or can mount denial of service attacks (DoS) in the form of jamming. This paper investigates the impact of injection and reactive jamming attacks in W-SKG. First, it is demonstrated that injection attacks can be reduced to - potentially less harmful - jamming attacks by pilot randomization; a novel system design with randomized QPSK pilots is presented. Subsequently, the optimal jamming strategy is identified in a block fading additive white Gaussian noise (BF-AWGN) channel in the presence of a reactive jammer, using a game theoretic formulation. It is shown that the impact of a reactive jammer is far more severe than that of a simple proactive jammer.

In computational complexity and latency con- strained emerging 5G applications, e.g., autonomous vehicles, haptic communications and enhanced reality, secret key generation (SKG) at the physical layer could be considered as an alternative to currently used key agreement schemes. In this framework, we study the optimal subcarrier scheduling in multicarrier systems when a subset of the subcarriers are used for SKG and the rest for data transmission, under both security and power constraints. The amount of data that can be transmitted with a single key is determined by the cryptographic suites used, so that realistic key rate constraints can be identified. This allows us to formulate the subcarrier allocation as a subset-sum 0-1 knapsack optimization problem that we solve using i) the standard dynamic programming approach and ii) a greedy heuristic approach of linear complexity. We show that the proposed heuristic induces virtually no loss in performance. Furthermore, a comparison with a baseline scheme in which SKG and data transfer are performed sequentially, shows that the proposed parallel approach offers gains in terms of efficiency.

Due to computational complexity and latency constraints in the nodes of many IoT systems, alternatives to public key encryption are sought for session key generation schemes. In this work we investigate novel cross-layer security protocols in which session keys are generated at the physical layer using standard techniques of secret key generation (SKG) from shared randomness. In this framework, we study the optimal power allocation in block-fading additive white Gaussian noise (BF-AWGN) channels, with short-term power constraints, when a subset of the subcarriers are used for SKG and the rest for data transmission. Fixing the amount of data that can be transmitted with a single key, allows us to first identify the optimal subset of subcarriers that should be devoted to SKG and the respective power allocation policy which, depending on the available overall power, might not be unique. Subsequently, a further step is taken in our analysis to account for the impact of the proposed power allocation with long-term power-constraints.

In this paper, a novel approach is introduced to study the achievable delay-guaranteed secrecy rate, by introducing the concept of the effective secrecy rate (ESR). This study focuses on the downlink of a non-orthogonal multiple access (NOMA) network with one base station, multiple single-antenna NOMA users and an eavesdropper. Two possible eavesdropping scenarios are considered: 1) an internal, unknown, eavesdropper in a purely antagonistic network; and 2) an external eavesdropper in a network with trustworthy peers. For a purely antagonistic network with an internal eavesdropper, the only receiver with a guaranteed positive ESR is the one with the highest channel gain. A closed-form expression is obtained for the ESR at high signal-to-noise ratio (SNR) values, showing that the strongest user’s ESR in the high SNR regime approaches a constant value irrespective of the power coefficients. Furthermore, it is shown the strongest user can achieve higher ESR if it has a distinctive advantage in terms of channel gain with respect to the second strongest user. For a trustworthy NOMA network with an external eavesdropper, a lower bound and an upper bound on the ESR are proposed and investigated for an arbitrary legitimate user. For the lower bound, a closed-form expression is derived in the high SNR regime. For the upper bound, the analysis shows that if the external eavesdropper cannot attain any channel state information (CSI), the legitimate NOMA user at high SNRs can always achieve positive ESR, and the value of it depends on the power coefficients. Simulation results numerically validate the accuracy of the derived closed-form expressions and verify the analytical results given in the theorems and lemmas.

Video content is responsible for more than 70% of the global IP traffic. Consequently, it is important for content delivery infrastructures to rapidly detect and respond to changes in content popularity dynamics. For flexible and highly adaptive solutions, the capability for a quick response should be driven from early (real-time) and low-complexity content popularity detection schemes. In this paper, we focus on the early and low-complexity detection of video content popularity, which we address as a statistical change point (CP) detection problem. Our proposed methodology estimates in real-time the existence, the number, the magnitude and the direction of changes in the average number of video visits by combining: (i) off-line and on-line CP schemes; (ii) an improved measurements window segmentation heuristic for the detection of multiple CPs; and (iii) a variation of the moving average convergence divergence (MACD) indicator to detect the direction of changes. We evaluated the proposed framework using a large database of real youtube video visits. The proposed algorithm is shown to accurately identify CPs and the direction of change in the off-line phase. Finally, a few illustrative examples of two variations of the on-line algorithm are also included.

Secret key generation (SKG) from shared randomness at two remote locations has been shown to be vulnerable to denial of service attacks in the form of jamming. Typically, such attacks are alleviated with frequency hopping/spreading techniques that rely on expansion of the system bandwidth. In the present study, energy harvesting (EH) is exploited as a novel counter-jamming approach that alleviates the need for extra bandwidth resources. Assuming the legitimate users have EH capabilities, the idea is that part of the jamming signal can potentially be harvested and converted into useful communication power. In this framework, the competitive interaction between a pair of legitimate users and a jammer is formulated as a zero-sum game. A critical transmission power for the legitimate users is identified which allows to completely characterize the unique NE of the game in closed form. Remarkably, this threshold also provides the option to effectively neutralize the jammer, i.e., prevent the jammer from carrying out the attack altogether. Through numerical evaluations, EH is shown to be a counter-jamming approach that can offer substantial gains in terms of relative SKG rates.

Secret key generation (SKG) schemes have been shown to be vulnerable to denial of service (DoS) attacks in the form of jamming. In this paper, a comprehensive study on the impact of correlated and uncorrelated jamming in wireless SKG systems is presented. First, optimal signalling schemes for the legitimate users and jamming approaches for an active adversary launching a DoS attack on the SKG system are derived. It is shown that the legitimate users should employ constant signalling. On the other hand, the jammer should inject either correlated jamming when imperfect channel state information (CSI) regarding the main channel is at their disposal, or, uncorrelated jamming when the main channel CSI is completely unknown. In both cases, optimal power allocation policies are studied under short-term power constrains for M block fading additive white Gaussian noise (BF-AWGN) channels. Numerical evaluations demonstrate that equidistribution of the jamming power is near-optimal in the case of uncorrelated jamming.

Jamming attacks have been shown to disrupt secret key generation (SKG) in systems that exploit the reciprocity of the wireless medium to generate symmetric keys at two remote locations through public discussion. In this study, the use of frequency hopping/spreading in Rayleigh block fading additive white Gaussian noise (BF-AWGN) channels is investigated as a means to counteract such attacks. The competitive interaction between a pair of legitimate users and a jammer is formulated as a zero-sum game and the corresponding Nash equilibria (NE) are characterized analytically and in closed form. It is found that the jammer's optimal strategy is to spread its power across the entire spectrum. On the contrary, the pair of legitimate users should use frequency spreading only in favorable transmission conditions, and frequency hopping otherwise (e.g., low signal to jamming power ratio). Numerical results show that frequency hopping/spreading in BF-AWGN channels is an effective technique for combating jamming attacks in SKG systems; a modest increase of the system bandwidth can substantially increase the SKG rates.

Jamming attacks represent a critical vulnerability for wireless secret key generation (SKG) systems. In this paper, two counter-jamming approaches are investigated for SKG systems: first, the employment of energy harvesting (EH) at the legitimate nodes to turn part of the jamming power into useful communication power, and, second, the use of channel hopping or power spreading in block fading channels to reduce the impact of jamming. In both cases, the adversarial interaction between the pair of legitimate nodes and the jammer is formulated as a two-player zero-sum game and the Nash and Stackelberg equilibria are characterized analytically and in closed form. In particular, in the case of EH receivers, the existence of a critical transmission power for the legitimate nodes allows the full characterization of the game's equilibria and also enables the complete neutralization of the jammer. In the case of channel hopping versus power spreading techniques, it is shown that the jammer's optimal strategy is always power spreading while the legitimate nodes should only use power spreading in the high signal-to-interference ratio (SIR) regime. In the low SIR regime, when avoiding the jammer's interference becomes critical, channel hopping is optimal for the legitimate nodes. Numerical results demonstrate the efficiency of both counter-jamming measures.

Physical-layer network coding (PNC) has been proposed for next generation networks. In this paper, we investigate PNC schemes with embedded perfect secrecy by exploiting structured interference in relay networks with two users and a single relay. In a practical scenario where both users employ finite and uniform signal input distributions, we establish upper bounds (UBs) on the achievable perfect secrecy rates and make these explicit when pulse amplitude modulation modems are used. We then describe two simple, explicit encoders that can achieve perfect secrecy rates close to these UBs with respect to an untrustworthy relay in the single antenna and single relay setting. Last, we generalize our system to a multiple-input multiple-output relay channel, where the relay has more antennas than the users and study optimal precoding matrices, which maintain a required secrecy constraint. Our results establish that the design of PNC transmission schemes with enhanced throughput and guaranteed data confidentiality is feasible in next generation systems.

The optimal power allocation for block fading (BF) networks with confidential messages is investigated under anM-block delay and power constraint. First, we study networks without channel state information (CSI) feedback to the transmitter and demonstrate that the optimal power allocation is the equidistribution of the power budget, denoted as the “blind policy.” In blind scenarios secrecy can be achieved through receiver diversity; the probability of secrecy outage (PSO) is shown to decay exponentially with the diversity order of the legitimate user. Then, we investigate networks with CSI feedback. For comparison purposes, we restate the acausal secure waterfilling algorithm with full CSI before moving to the causal feedback scenario. In the latter, an approximate “threshold policy” for the low SNR and an approximate “high power policy” for the high SNR regimes are derived. Furthermore, a novel universal transmission policy is proposed across all SNRs, denoted as the “blind horizon approximation” (BHA). Through numerical results, the BHA policy is shown to outperform both the threshold and high power policies when the legitimate user has an SNR advantage with respect to the eavesdropper, while it also compares well with the secure waterfilling policy.

The optimal power allocation that maximizes the secrecy capacity (SC) of block fading Gaussian (BF-Gaussian) networks with causal channel state information (CSI), M-block delay tolerance and a frame based power constraint is examined. In particular, the SC maximization is formulated as a dynamic program. First, the SC maximization without any information on the CSI is studied; in this case the SC is maximized by equidistribution of the power budget, denoted as the "blind policy". Next, extending earlier results on the capacity maximization of BF-Gaussian channels without secrecy constraints, transmission policies for the low SNR and the high SNR regimes are proposed. When the available power resources are very low the optimal strategy is a "threshold policy". On the other hand when the available power budget is very large a "constant power policy" maximizes the frame secrecy capacity. Subsequently, a novel universal transmission policy is introduced, denoted in the following as the "blind horizon approximation" (BHA), by imposing a blind policy in the horizon of unknown events. Through numerical results, the novel BHA policy is shown to outperform both the threshold and constant power policies as long as the mean channel gain of the legitimate user is distinctively greater than the mean channel gain of the eavesdropper. Furthermore, the secrecy rates achieved by the BHA compare well with the secrecy rates of the secure waterfilling policy in the case of acausal CSI feedback to the transmitter.

In this paper, the impact of network-state knowledge is studied in the context of decentralized active non-colluding eavesdropping. The main contribution is a formal proof of a paradoxical effect that might appear when increasing the available knowledge at each of the network components. Using a broadcast channel similar to the time-division downlink of a single-cell cellular system, it is shown that providing more knowledge to both the transmitter and the receivers negatively affects their performance. Eavesdroppers become more conservative in their attacks, which makes them harmless in terms of information leakage, whereas the transmitter becomes more careful and less willing to transmit, which reduces the expected secrecy capacity of this channel. Finally, it is shown that this counter-intuitive effect vanishes in the high SNR regime, in which the system becomes resilient to active attacks.

Physical layer security can provide alternative means for securing the exchange of confidential messages in wireless applications. In this paper, the resilience of wireless multiuser networks to passive (interception of the broadcast channel) and active (interception of the broadcast channel and false feedback) eavesdroppers is investigated under Rayleigh fading conditions. Stochastic characterizations of the secrecy capacity (SC) are obtained in scenarios involving a base station and several destinations. The expected values and variances of the SC along with the probabilities of secrecy outages are evaluated in the following cases: (i) in the presence of passive eavesdroppers without any side information; (ii) in the presence of passive eavesdroppers with side information about the number of eavesdroppers; and (iii) in the presence of a single active eavesdropper with side information about the behavior of the eavesdropper. This investigation demonstrates that substantial secrecy rates are attainable on average in the presence of passive eavesdroppers as long as minimal side information is available. On the other hand, it is further found that active eavesdroppers can potentially compromise such networks unless statistical inference is employed to restrict their ability to attack. Interestingly, in the high signal to noise ratio regime, multiuser networks become insensitive to the activeness or passiveness of the attack.

Security is becoming an increasingly important issue in wireless communications, to which physical layer approaches can contribute by providing addition resources for securing confidential messages. In this paper, the resilience of multi-user networks to passive and active eavesdropping is investigated. In particular, average secrecy capacities are evaluated in scenarios involving a base station and several terminals, some of which constitute passive or active eavesdroppers. Network resources (e.g. power) are allocated by the base station based on the available channel state information. The average secrecy capacity of such a network is evaluated in the following cases: (i) in the presence of passive eavesdroppers when no side information is available to the base station; (ii) in the presence of passive eavesdroppers with side information available; and (iii) in the presence of a single active eavesdropper with side information available. This investigation demonstrates that substantial secrecy rates are attainable in the presence of passive eavesdroppers as long as minimal side information, e.g. a statistical characterization of the number of potential eavesdroppers, is available to the base station. On the other hand, it is further found that active eavesdroppers can potentially compromise such networks unless statistical inference is employed to restrict their ability to attack.

This paper investigates the transmission in time dispersive channels of Spectrally Efficient Frequency Division Multiplexed (SEFDM) signals, where carrier orthogonality is intentionally violated in order to increase bandwidth efficiency. Sufficient statistics of the transmitted SEFDM signal can be obtained by projecting the received signal onto an orthonormal base generated at the receiver using an Iterative Modified Gram Schmidt (IMGS) procedure. In order to reduce the computational complexity resulting from Inter-Carrier Interference (ICI), detection has been implemented based on a Regularized Sphere Decoding (RSD) algorithm. The proposed scheme was previously tested in Additive White Gaussian Noise (AWGN) for various SEFDM signal parameters. In the present work, these results are extended to account for the effect of time dispersive channels. Randomly generated SEFDM symbols are used as pilots to provide estimates of the channel impulse response in systems with or without cyclic prefixes. A joint equalization-detection is subsequently performed in a RSD stage. We show that it is possible to detect optimally SEFDM signals of small dimensionality (e.g. N = 32), with up to 20% bandwidth gain with respect to OFDM systems of the same symbol-rate. This indicates that the wireless transmission of non orthogonal SEFDM signals is tangible.

This paper investigates the transmission of frequency division multiplexed (FDM) signals, where carrier orthogonality is intentionally violated in order to increase bandwidth efficiency. In analogy to conventional OFDM, signal generation relies on an inverse fractional Fourier transform (IFRFT) that can be implemented with O(N log2N) algorithmic complexity. Optimal maximum likelihood (ML) detection is overly complex due to the presence of substantial intercarrier interference (ICI). Consequently, we investigate an alternative detection mechanism based on the generalized sphere decoding (GSD) algorithm. We examine the bandwidth efficiency and the error performance in additive white gaussian noise (AWGN), for various FDM signal parameters. In particular, we show that it is possible to detect optimally and efficiently FDM signals, with 25% bandwidth gain with respect to analogous OFDM signals. This indicates that the transmission of spectrally efficient non orthogonal FDM signals is tangible.

This paper investigates the secure transmission of Orthogonal Frequency Division Multiplexing (OFDM) signals, masked under non-orthogonal FDM signals of approximately the same overall bandwidth. Carrier orthogonality of traditional OFDM is intentionally violated in order to generate an encrypted signal at the physical layer, without any loss in bandwidth efficiency or extra overhead. A symmetric secret key is used for the modulation of part of the FDM signal while the original information symbol modulates the remaining carriers. The encrypted FDM signal generation takes place in an IFFT stage by simple truncation of part of its output. At the receiver, a straightforward suppression of the induced Inter-Carrier Interference (ICI) is performed before the OFDM demodulator, based on knowledge of the secret key. The key holder subsequently uses the traditional OFDM FFT demodulator to extract the information symbol. We demonstrate that an eavesdropper cannot demodulate the transmitted FDM signal by reduced complexity detection methods due to its severe ill-conditioning. Conversely, they have to rely on vectorial Maximum Likelihood detection of exponential complexity. Furthermore, in real noisy environments the eavesdropper detection capability is further affected by the abrupt shrinking of the FDM demodulator detection regions.

Recent years have witnessed some interest in Spectrally Efficient Frequency Division Multiplexing (SEFDM) communications systems, where subcarrier orthogonality is intentionally violated to improve the spectral efficiency at the expense of system complexity. This paper investigates reliable polynomial-time hard detection techniques for SEFDM systems, by relaxing the optimal combinatorial Maximum Likelihood (ML) detection to a Semidefinite Program (SDP). SDP can be solved in almost cubic complexity over the number of the SEFDM subcarriers, N. However, the relaxation results into a degradation of the system error performance. In particular, we study the effect of the number of SEFDM subcarriers, N, and the subcarrier separation, ¿f, on the SDP relaxation gap in the presence of Additive White Gaussian Noise (AWGN). We find that as N increases and/or ¿f decreases, the SDP estimate gradually diverges from the optimal solution. To overcome this problem, we propose the use of a boxed ML procedure around the SDP estimate. We show by simulation that the SDP-ML combination approximates the optimum detection for N ¿ 32 subcarriers and up to 20% of bandwidth reduction with respect to an equivalent Orthogonal FDM (OFDM). Our SDP results show a small error penalty when compared to optimal Sphere Decoders (SD), whose computational effort is random and noise dependant, and thereby indicate that our proposed technique is useable in practical SEFDM systems with a moderate number of subcarriers.

In this paper, we investigate the possibility of reliable and computationally efficient detection for spectrally efficient non-orthogonal Multiplexing (FDM) system, exhibiting varying levels of intercarrier interference. Optimum detection is based on the Maximum Likelihood (ML) principle. However, ML is impractical due to its computational complexity. On the other hand, linear detection techniques such as Zero Forcing (ZF) and Minimum Mean Square Error (MMSE) exhibit poor performance. Consequently, we explore the combination of MMSE estimation with ML estimation around a neighborhood of the MMSE estimate. We evaluate the performance of the different schemes in Additive White Gaussian Noise (AWGN), with reference to the number of FDM carriers and their frequency separation. The combined MMSE-ML scheme achieves a near optimum error performance with polynomial complexity for a small number of BPSK FDM carriers. For QPSK modulation the performance of the proposed system improves for a large number of ML comparisons. In all cases, the detectability of the FDM signal is bounded by the signal dimension and the carriers frequency distance.

In this paper, we derive an expression for the near-carrier power spectral density of an oscillator having 1/f phase noise. Motivated by empirical metrics such as the Allan variance, we develop a rigorous mathematical analysis and derive a closed-form expression for the oscillator autocorrelation function in the case of exactly 1/f phase noise that is smoothed using a rectangular time window. We show that this smoothed 1/f phase noise results in a finite variance noise process and preserves oscillator stationarity. Furthermore, in agreement with experimental data, we explain how a quadratic and a logarithmic term appear in the autocorrelation function and establish the relationship between the logarithmic term and the 1/f characteristics of the oscillator random process.