Hybrid beamforming (HB) with quality-of-service (QoS) provisioning per stream in millimeter waves is indispensable in 5G/6G networks. HB includes baseband and radio frequency (RF) beamforming, and requires error-free channel state information (CSI), which is erroneous in practice. So there is a need for efficient, feasible, robust, and QoS-aware HB. To achieve this, we mitigate CSI uncertainty via baseband beamforming, and we steer the RF beamformer by using the estimates of the channel’s eigenvectors. In doing so, we consider the effective channel’s uncertainty region instead of the uncertainty region of the channel itself, as the former is smaller than the latter, requiring less transmit power to satisfy the QoS constraint. We also detect and eliminate the infeasible data streams. Our iterative scheme (which is based on the cutting-set method) for baseband beamforming satisfies the mean-squared error (MSE) constraint per stream, where a limited number of constraints are considered instead of infinitely many constraints. In our low-complexity scheme, we derive a simple sufficient condition to check the feasibility of each stream, we diagonalize the effective channel at the baseband precoder, and we use minimum MSE combining at the baseband combiner. Extensive simulations validate our formulations and theoretical derivations.

Mobile crowdsensing (MCS) is becoming an increasingly important topic due to rapid proliferation of mobile apps where participants’ anonymity is a pivotal requirement with direct impacts on their safety and well being. There are two main challenges in crypto-based privacy-preserving aggregation in MCS, namely, participants joining or leaving the crowd randomly at will, and adversaries injecting fake data. The conventional approach for preserving privacy is to provide blanket anonymity to all, including adversaries, which enables the latter to cause harm without being identified. In addition, with the proliferation of edge servers, there is a need to develop edge-assisted MCS, which would be more efficient in terms of less back-haul traffic and less delay as compared to cloud-assisted-only MCS. In this paper, we present an efficient edge-assisted MCS scheme which preserves the participants’ privacy and anonymity, protects the service against adversaries, and can be used to verify that aggregation is free of anomalies. Our scheme is transparent to the join-and-leave problem; and its computational cost and communication overhead are small and fixed, i.e., it is insensitive to crowd count. We utilize group signature for source authentication to identify and block adversaries that cause harm while providing anonymity to ordinary participants.

Future 5G/6G wireless networks will be increasingly using millimeter waves (mmWaves), where fast and efficient beamforming is vital for providing continuous service to highly mobile devices in the presence of interference and signal attenuation, manifested by blockage. In this paper, we propose a novel and efficient method for mmWave beamforming in massive multiple-input multiple-output (MIMO) systems to achieve the aforementioned goals with low complexity in such scenarios. In doing so, we utilize deep reinforcement learning (DRL) to maximize the network’s energy efficiency subject to the quality of service (QoS) constraint for each user equipment (UE) and obtain its hybrid beamforming matrices. In doing so, we assume each UE is simultaneously associated with multiple access points (APs), i.e., simultaneous beamforming to/from multiple APs (coordinated multipoints) is needed for each UE. We also propose a low-complexity training algorithm, based on approximate message passing, which is well suited for the network edge. Besides, we develop a distributed scheme to reduce communications overhead via federated DRL. Extensive simulations show significant performance improvement over existing methods.

Peak‐to‐average power ratio (PAPR) is the main parameter affecting the implementation cost of wireless transmitters utilising carrier aggregation in the end‐users' channels. In each frequency band, single‐carrier frequency division multiple access (SC‐FDMA) is exploited as the interface from the transmitter to the receiver, and carrier aggregation is implemented by SC‐FDMA on each band. Hence, the power amplifier simultaneously amplifies signals of multiple bands, meaning that its PAPR is affected by the use of multiple SC‐FDMA channels. In this study, a novel method has been proposed for the delay and PAPR reduction in carrier aggregation systems. Also, a novel recursive method is proposed to reduce the computational complexity of the proposed partial transmit sequence (PTS) algorithm. Utilising computer simulations, it has been shown that the PAPR can be reduced by 3.5 dB for the time‐domain PTS method in mapping for the three‐band SC‐FDMA comparing to existing methods.

We present a novel and efficient approach for robust estimation of displacement in real-time strain imaging for freehand ultrasound elastography by utilizing pre- and post-deformation ultrasound images. We define a quality factor for image lines and find the line with the highest value of quality factor to serve as the seed line for generating the displacement map. We also develop an analytical framework for coarse-to-fine displacement estimation, obtain an initial estimate of the seed line's displacement with subsample precision, and propagate it to the entire image to obtain a high quality strain image. Our fast strategy for estimating the seed line's displacement enables us to enhance the robustness without sacrificing the speed by identifying a new seed line when the quality falls below a given threshold. This is more efficient than the existing approaches that utilize multiple seed lines to improve robustness. Simulations, phantom experiments, and clinical studies show high signal-to-noise-ratio and contrast-to-noise-ratio values in our method for a wide range of average strain levels (1%-10%). Phantom experiments also demonstrate that our method is robust against corrupt and decorrelated data. Our method is superior to the existing real-time methods as it can produce high-quality strain images for up to 10% average strain levels at the rate of 20 frames/s on conventional CPUs.

We propose a novel approach for detecting precursors to epileptic seizures in intracranial electroencephalograms ( i EEGs), which is based on the analysis of system dynamics. In the proposed scheme, the largest Lyapunov exponent (LLE) of wavelet entropy of the segmented EEG signals are considered as the discriminating features. Such features are processed by a support vector machine classifier, whose outcomes (the label and its probability for each LLE) are post‐processed and fed into a novel decision function to determine whether the corresponding segment of the EEG signal contains a precursor to an epileptic seizure. The proposed scheme is applied to the Freiburg data set, and the results show that seizure precursors are detected in a time frame that unlike other existing schemes is very much convenient to patients, with the sensitivity of 100% and negligible false positive detection rates.

In this paper, the downlink capacity region of a secondary network in a multiuser spectrum sharing system is obtained for a hybrid underlay-interweave paradigm, where the current channel side information (CSI) of the channel between a secondary transmitter-receiver pair is assumed at the secondary transmitter, whereas the outdated CSI of the interference channel is available to the secondary transmitter. In the hybrid paradigm, the secondary network cycles through the interweave or the underlay paradigms depending on whether spectrum holes are detected or the primary network is active, respectively. The outdated CSI is described via a correlation model, and the interference outage probability constraint (IOPC) and the average interference power constraint are considered. In general, determining the boundary of this region is a nonconvex problem. To navigate around solving this nonconvex problem, we replace the IOPC with its equivalent constraint on the peak transmit power, and we propose a power allocation algorithm for solving the latter problem. Simulations show that utilizing the outdated CSI of the interference channel reduces the secondary network's capacity as compared with when the current CSI is utilized. This reduction is a function of the correlation between the current and outdated CSI values, the interference outage probability in the primary network, and imperfect spectrum sensing.

We develop a robust formalism for power control games in unlicensed bands between two groups of users competing for the spectrum: informed-users (leaders) who have advanced capabilities to extract side-information about other users and their strategies, and uninformed-users (followers) who can only observe the aggregate interference caused by others. Such nominal leader-follower games have been previously studied in the power control literature; however, these prior works fail to capture an important aspect of such interactions: the side-information and observations made by users may be uncertain, which has an important impact on users' strategies and network performance. Thus, in this paper we propose a new, robust game-theoretic formalism and solution which takes these uncertainties into account. Specifically, each group chooses its actions by solving its respective worst-case robust optimization problems. We show how various types of uncertainties affect the social utility of each group, and identify in which deployment scenarios the social utility of the robust game is higher than that of the nominal game. Importantly, we show that robust solutions in such games are more energy efficient. Finally, our theoretical formalism, analysis and solutions are complemented by simulations.

We develop a game theoretic framework for distributed resource allocation in the uplink of a cognitive radio network where secondary users (SUs) share the bandwidth with primary users (PUs). Each PU has a fixed data rate and applies power control to achieve its target SINR, while each SU jointly adjusts its data rate and transmit power by maximizing its utility. Since the SUs' total interference on the primary network is kept below a given threshold, there exist couplings between SUs' transmit power levels. Hence, the SUs' game belongs to the generalized Nash equilibrium (GNE) problems. We separately analyze the proposed resource allocation algorithm for SUs and the one utilized by PUs, and derive the condition under which the PUs' power control algorithm converges for SUs' fixed transmit power levels. We also derive the sufficient condition under which the SUs' joint data rate and power control algorithm converges for fixed transmit power levels of PUs. We then derive the sufficient condition for convergence of the algorithms when PUs and SUs simultaneously apply their resource allocation schemes. Simulations confirm our analysis and demonstrate that the proposed framework is energy efficient and provides PUs and SUs with their quality of service requirements.

We propose a robust distributed uplink power allocation algorithm for underlay cognitive radio networks (CRNs) with a view to maximizing the social utility of secondary users (SUs) when channel gains from SUs to primary base stations, and interference caused by primary users (PUs) to the SUs' base station are uncertain. In doing so, we utilize the worst case robust optimization to keep the interference caused by SUs to each primary base station below a given threshold, and satisfy each SU's quality of service in terms of its required SINR for all realizations of uncertain parameters. We model each uncertain parameter by a bounded distance between its estimated and exact values, and formulate the robust power allocation problem via protection values for constraints. We demonstrate that the convexity of our problem is preserved, and converts into a geometric programming problem, which we solve via a distributed algorithm by using Lagrange dual decomposition. To reduce the cost of robustness, defined as the reduction in the social utility of SUs and the increase in message passing, we utilize the D-norm approach to trade off between robustness and optimality, and propose a distributed power allocation algorithm with infrequent message passing. Simulation results validate the effectiveness of our proposed approach.

In a dynamic spectrum sharing system, secondary users (SUs) attempt to maximise their throughput while preserving a given level of quality of service (QoS) for primary users (PUs). In doing so, the secondary network monitors the spectrum and decides on spectrum availability, which at times and for various reasons, may be erroneous. If spectrum is sensed as available, the secondary network selects an appropriate spectrum access strategy, and adjusts other communication parameters such as transmit power levels. In this study, the authors analyse primary service outage degradation caused by secondary access with non-ideal spectrum sensing. For different spectrum access strategies, namely, the overlay and the modified underlay spectrum access (MUSA), the authors derive a closed-form expression for the maximum outage probability degradation (MOD) in the presence of spectrum sensing errors and fading and show how adjustments by secondary service can result in attaining the desired QoS for PUs. Besides, for a given acceptable MOD in the primary network, the authors obtain a lower bound on the achievable expected rate of the secondary service in Nakagami-m and Rayleigh fading channels. Simulations confirm the analysis and show that MUSA considerably increases the secondary service rate, particularly when the activity of primary service is above 50%.

We obtain the robust Nash equilibrium (RNE) for a wide range of multi-user communications networks under uncertainty by utilizing the robust optimization theory for the worst-case uncertainties. To do so, we consider the uncertainty as a distance between the estimated and the actual values of the system parameters as a general norm function, and utilize the finite-dimensions variational inequalities (VI) to derive the conditions for existence and uniqueness of RNE. Two effects of uncertainty on the performance of the system are investigated: the difference between the achieved social utility at the RNE and the Nash equilibrium (NE) of the nominal game, and the distance between the deployed strategies of users at the RNE and at the NE. We quantify these two effects for the cases of unique NE and multiple NEs, and show that when the NE is unique, the achieved social utility at the RNE is always less than that of the NE. Interestingly, the worst-case robustness approach may lead to a higher social utility at the RNE in the multiple NEs scenario. Considering uncertainty at RNE introduces coupling between users, and hence, developing distributed algorithms for reaching RNE is more challenging as compared to the NE in the nominal game. However, for some special forms of utilities and norm functions, we propose simultaneous and sequential distributed algorithms; and investigate the performance of the robust game for power control in interference channels, and for flow control in Jackson networks.

We formulate the resource allocation problem for the uplink in code division multiple access (CDMA) networks using a game theoretic framework, propose an efficient and distributed algorithm for a joint rate and power allocation, and show that the proposed algorithm converges to the unique Nash equilibrium (NE) of the game. Our choice for the utility function enables each user to adapt its transmit power and throughput to its channel. Due to users' selfish behavior, the output of the game (its NE) may not be a desirable one. To avoid such cases, we use pricing to control each user's behavior, and analytically show that similar to the no-pricing case, our pricing-based algorithm converges to the unique NE of the game, at which, each user achieves its target signal-to-interference-plus-noise ratio (SINR). We also extend our distributed resource allocation scheme to multi-cell environments for base station assignment. Simulation results confirm that our algorithm is computationally efficient and its signalling overhead is low. In particular, we will show that in addition to its ability to attain the required QoS of users, our scheme achieves better fairness in allocating resources and can significantly reduce transmit power as compared to existing schemes.

In the well-known distributed target-SIR-tracking power control algorithm (TPC), when the system is infeasible (a constrained power vector does not exist to attain target-SIRs), all non-supported users (those who cannot obtain their target-SIRs) transmit at their maximum power. Such users inefficiently consume their energy, and cause interference to others, which increases the number of non-supported users. To deal with this, some non-supported users should decrease their transmit power (the gradual removal problem). We present a distributed power control scheme with gradual soft removal, by which either TPC or OPC (opportunistic power control) is used, depending on whether the ratio of interference-to-path-gain is below or above a threshold that is chosen by each user in a distributed manner. We show that our algorithm converges to a unique fixed-point in both feasible and infeasible systems, and that when the system is infeasible, it results in less outage with significantly less consumed power, as compared to TPC. We also provide a game theoretic analysis of our algorithm by introducing a new pricing when users are selfish. As our algorithm is fully distributed and requires only local information, it can be applied to both cellular and ad hoc networks. Simulation results confirm our analysis.

We formally define the gradual removal problem in wireless networks, where the smallest number of users should be removed due to infeasibility of the target-SIR requirements for all users, and present a distributed power-control algorithm with temporary removal and feasibility check (DFC) to address it. The basic idea is that any transmitting user whose required transmit power for reaching its target-SIR exceeds its maximum power is temporarily removed, but resumes its transmission if its required transmit power goes below a given threshold obtained in a distributed manner. This enables users to check the feasibility of system in a distributed manner. The existence of at least one fixed-point in DFC is guaranteed, and at each equilibrium, all transmitting users reach their target-SIRs consuming the minimum aggregate transmit power. Furthermore, in contrast to the existing algorithms, no user is unnecessarily removed by DFC, i.e., DFC is Pareto and energy-efficient. We also show that when target-SIRs are the same for all users, DFC minimizes the outage probability. Simulation results confirm our analytical developments and show that DFC significantly outperforms the existing schemes in addressing the gradual removal problem in terms of convergence, outage probability, and power consumption.

We propose a scheme for maintaining the requested SIR of each user under uncertainty of system parameters in the power control of interference limited wireless networks. In doing so, we keep the outage probability of users below their predefined threshold with minimal power consumption. To reduce the complexity, we apply the notion of chance constraint robust optimization to the outage probability. This approach preserves the convexity of the problem and maintains its tractability. For solving the reformulated problem, a distributed probabilistic robust power algorithm is developed based on the standard interference function and local convergence, which utilizes infrequent message passing. We derive the conditions for the convergence of our algorithm, and prove the optimality of the equilibrium.

Exact calculation of all-terminal network reliability is a hard problem; its computational complexity grows exponentially with the number of nodes and links in the network. We propose the Recursive Truncation Algorithm (RTA), a bounding approximation algorithm, to estimate the all-terminal reliability of a given network with a pre-specified accuracy. RTA scans all minimal cutsets of the graph representing the network, and finds the weak cutsets of the graph by comparing failure probabilities of cutsets to an adaptive threshold which depends on the approximation accuracy. We calculate the unreliability of the network versus the probabilities of occurrence of failure in the weak cutsets, and the probabilities of co-occurrence of failure in several weak cutsets, simultaneously. In addition to the all-terminal reliability, the RTA computes an upper, and a lower bound for the estimated reliability. We demonstrate that the estimated all-terminal reliability of a given network is within a pre-specified accuracy, and is more precise than those obtained by existing methods.

In the opportunistic power control algorithm (OPC), designed in each user tries to keep the product of its transmit power and its experienced effective interference to a constant, called the target signal-interference product (SIP). This increases the transmit power when the channel is good and reduces it when the channel is poor (opportunism). It has been shown that the OPC always converges to a fixed point irrespective of whether the power is constrained (where there is no upper bound on transmit power) or unconstrained (where an upper bound on transmit power is taken into account). It has also been shown via simulation that the throughput achieved by the unconstrained OPC is significant (as compared to other existing distributed schemes), and is an increasing function of the target-SIP set by users, in the sense that a higher target-SIP results in a higher throughput. In this paper, we show that in contrast to the unconstrained OPC, when the constrained OPC is applied, not only the throughput is not necessarily increased as the target-SIPs increase, but it may even decrease if some users set their target-SIPs at high values. Furthermore, we propose a heuristic solution to determine the target-SIPs by users in a distributed manner. Our simulation results show that the throughput achieved by our distributed setting of the target-SIPs very closely approaches the maximum achievable throughput by the constrained OPC, and is very close to the global optimum value.

In the well-known distributed target-SIR tracking power control algorithm, when the target-SIR requirements are not reachable for all users, all non-supported users (those who do not reach their target SIRs) transmit at their maximum power. Such users inefficiently consume their energies, and introduce unnecessary interference to others, which in turn unnecessarily increases the number of non-supported users. To deal with this, the smallest number of users should be removed due to infeasibility of the system (gradual removal problem). We present a new distributed constrained power control (DCPC) algorithm to address the gradual removal problem. The basic idea is that any transmitting user whose required transmit power for reaching its target-SIR exceeds its maximum power is temporarily removed. Each temporarily removed user resumes its transmission if its required transmit power for reaching its target-SIR goes below a given threshold (lower than its maximum power). This threshold is determined by each removed user in a distributed manner using only local information. We will show that our proposed algorithm has at least one-fixed point (i.e., its convergence can be guaranteed), and at the equilibrium where the algorithm converges, all transmitting users (the users whose transmit powers are greater than zero) reach their target SIRs consuming the minimum aggregate transmit power. Furthermore, in contrast to the existing DCPC algorithms, no user is unnecessarily removed in our proposed scheme, i.e., it is efficient. Our simulation results confirm our analytic developments and show that our scheme outperforms the existing DCPCs in addressing the gradual removal problem, in terms of convergence, outage probability and power consumption.

A novel formal model is introduced that is tailored to the treatment of the service availability problem in concurrent systems. This model, called hierarchical services of interacting processes (HSIP), incorporates the hierarchy of services, processes, shared variables, and dense time to reason about services quantitatively. The underlying concept is that a service, via its hierarchy, relates to a number of so-called ‘atomic services’ at the bottom of the hierarchy that are the outcomes of actions having no subaction within. The services in a hierarchy are the results M̄of processes acting and interacting with each other to provide the service at the top of the hierarchy. These processes can completely be described by actions resulting in atomic services. The main features of HSIP are twofold. First, through this model, a wide range of quasi-isolated problems such as mutual exclusion, system reconfiguration, fault tolerance, resource allocation, denial of service, and quality of service can be stated and analyzed in a cohesive manner. Second, it retains the use of shared variables, but resolves the main shortcoming of the shared-variable approach, which is the implicitness of interactions among system components.

The problem of service availability is analyzed using the model developed in Part I. It is shown that the general service availability problem is undecidable, i.e. there is no single algorithm to determine whether a given service in a given system is available at a given time. In restricted cases, it is shown that the problem is decidable, but is NP-complete. The problem of service availability is then extended to nondeterministic systems. Finally, it is shown that a number of important cases can be studied in a cohesive manner based on our proposed formalism.

Motivated by the results in (K. Navaie et al., 2006) on the self-similarity of the downlink interference in heterogenous service DS-CDMA networks, in this paper, we propose a model-based linear adaptive-predictive method to estimate the level of interference for optimizing the system throughput and minimizing the delay for non-real-time data transmission. We use a fractional Gaussian noise (fGn) model in an appropriate time-scale to represent the self-similarity in the downlink interference. The estimated interference is utilized to allocate the available power to non-real-time services. In doing so, we use a utility-based optimization scheme and dynamic programming for time-domain optimal scheduling of non-real-time traffic. Simulation results validate the fGn model and show a substantial improvement in the delay fairness and a significant increase in the average cell throughput using our proposed scheme; and confirm that the interference model is valid for a broad range of arrival rates of non-real time traffic.

In this paper, we show that the total downlink interference in heterogeneous wireless DS-CDMA networks follows an asymptotically self-similar (as-s) process. The as-s model is valid for the interference under certain conditions on channel variations and traffic characteristics that cover a range of practical situations. We derive these conditions and generalize earlier results, obtained for data-centric cellular networks, to heterogeneous cellular networks. Simulation results for actual cases confirm analytical results, and show that non-uniform spatial distribution of users and their soft-hand-off status do not affect the nature of this self-similar process. Furthermore, we discuss the impact of the analysis developed in this paper in designing appropriate mechanisms for controlling radio resources in such networks.

We present a novel nonparametric multiuser detector for non-Gaussian channels that is based on the signed-rank norm for linear regression. Analytical and simulation results show that the proposed detector offers similar or better performance as compared to the minimax robust detector, but without requiring any a priori information on the noise. The complexity of this detector is lower than that of the pseudo-norm nonparametric detector stated previously by the authors. This is due to the fact that in contrast to the latter, it is not necessary to compute the intercept parameter for the signed-rank detector proposed in this correspondence. We analyze the behavior of the blind version of this detector and show that it outperforms the blind minimax detector. We also show that this detector has a bounded influence function and hence it is robust.

We develop a framework for the analysis of Denial of Service (DoS) attacks in computer networks. In this framework, we identify the components and resources that collectively and in time, construct a service that may become unavailable due to the unavailability of those resources as a result of a DoS. We also construct the notion of service resilience pre-ordering to compare the effectiveness of solutions proposed to improve a system's resilience against DoS. We apply our framework and methods to some cases, and show their usefulness and applicability in practice.