Context: Cross-project defect prediction (CPDP) aims to predict software defects in a target project using data from related source projects, especially when defect data for the target project is limited or unavailable. A key challenge in CPDP is data heterogeneity and distributional differences across projects, which often lead to poor performance and unreliable predictions. Objectives: This study proposes a new CPDP model that improves prediction accuracy and robustness by introducing fuzzy embedding and deep learning to better capture similarities and differences across projects. The method is designed to mitigate mismatches in data distribution that hinder existing transfer learning and transformation-based approaches. Methods: The fuzzy embedding technique is built on fuzzy clustering and fuzzy set theory, which map each data point into a two-dimensional space of membership degrees across clusters. This representation models complex relationships with partial memberships and preserves contextual information that is often lost in conventional transformations. A deep learning model based on convolutional neural networks (CNN) processes the embedding matrices to learn discriminative defect patterns. The framework is evaluated against state-of-the-art CPDP models across multiple datasets, and sensitivity analysis is conducted on the number of clusters used in fuzzy embeddings. Results: Empirical evaluation shows that the proposed model consistently outperforms advanced CPDP approaches that rely on standard transformation or weighting methods. Improvements are observed in AUC and other key metrics across diverse datasets, demonstrating that fuzzy embeddings enhance the ability of deep learning to generalize knowledge across projects with varying characteristics. Conclusion: This work contributes a practical and effective solution for addressing heterogeneity in CPDP. By combining fuzzy embeddings with deep learning, the model not only achieves higher predictive accuracy but also improves reliability in real-world scenarios where project data distributions differ significantly. These findings highlight the potential of fuzzy embedding to support more resilient software quality assurance practices and provide actionable insights for practitioners dealing with limited or imbalanced defect data.

Frequency hopping (FH) is an effective distributed blind rendezvous approach used by secondary users (SUs) in dynamic spectrum access (DSA) networks to establish communication links. Several “quorum-based” FH approaches have been proposed in the literature to enable systematic time-asynchronous rendezvous with deterministic guarantees and without requiring role pre-assignment. This paper is concerned with securing such combinatorial FH rendezvous approaches against insider jamming attacks. We develop QUANTA, a Nested QUorum-based ANTi-jamming recursive game-theoretic rendezvous framework to analyze the interactions between a pair of legitimate SUs, who are trying to rendezvous, and a compromised SU, acting as a jammer, in a multi-channel DSA system.

Wireless network virtualization is emerging as a promising technology for cellular networks. A key advantage of introducing virtualization in cellular networks is that wireless services can be decoupled from network resources (e.g., infrastructure and spectrum) so that multiple virtual networks can be built using a shared pool of network resources. This paper develops an optimization framework for orchestrating virtualized cellular networks while enabling and exploiting statistical multiplexing. Our proposed framework has two phases: virtual network deployment (static) and statistical multiplexing (adaptive). In the virtual network deployment phase, network resources are aggregated, sliced, and allocated to the virtual networks considering the presence of uncertainty in user equipment (UE) locations and channel conditions, without knowing which realization of UE locations and channel conditions will occur. Once the virtual networks are deployed, each of the aggregated base stations (BSs) performs statistical multiplexing, i.e., allocates excess resources from the over-satisfied slices to the under-satisfied slices, according to the realized channel conditions of associated UEs. Our numerical results demonstrate that the proposed framework outperforms existing virtualization frameworks in terms of probabilistically satisfying virtual networks’ rate and coverage demands while minimizing resource over-provisioning, in the presence of uncertainty in UE locations and channel conditions.

Millimeter wave (mmW) systems typically use beamforming techniques to compensate for the high pathloss. However, directional communications in the presence of uncertainty in user equipment (UE) locations and channel conditions make maintaining coverage and connectivity challenging. In this context, we propose a joint optimization framework to determine the minimum number of required access points (APs), their optimal locations, their optimal beam directions, and their optimal assignments to individual UEs in order to maintain a network-wide signal-to-noise ratio (SNR) coverage and stable connections. The network deployment decisions (i.e., the required number of APs, their placements, and their beam directions) are static and are taken before UE locations and channel conditions are revealed. The UE assignment decisions are taken under each realization of UE locations and channel conditions considering the availability and stability of the mmW beams. We develop our joint optimization framework following a two-stage chance-constrained stochastic optimization model. Our numerical results demonstrate the gains brought by our proposed framework in terms of reducing cost of network deployment while ensuring a network-wide SNR coverage and stable connections under various UE distributions and system parameters.

Millimeter wave (mmW) systems typically use beamforming techniques to compensate for the high pathloss. However, directional communications in the presence of uncertainty in user equipment (UE) locations and channel conditions make maintaining coverage and connectivity challenging. In this context, assuming that mmW access points (APs) use low complexity fixed directional antennas, we propose a joint optimization framework for AP deployment and UE assignment in indoor mmW networks. The goal of our optimization framework is to determine the minimum number of required APs, their optimal locations, their optimal beam directions, and their optimal assignments to individual UEs in order to ensure network-wide coverage and maximize the stability of mmW beams assigned to individual UEs. The network deployment decisions (i.e., the required number of APs, their placements, and their beam directions) are static and are taken before UE locations and channel conditions are revealed. The UE assignment decisions are taken under each realization of UE locations and channel conditions considering the availability and stability of the mmW beams. Our numerical results demonstrate the behavior of the proposed framework under various UE distributions and system parameters.

Indoor areas, such as offices and shopping malls, are a natural environment for initial millimeter-wave (mmWave) deployments. Although we already have the technology that enables us to realize indoor mmWave deployments, there are many remaining challenges associated with system-level design and planning for such. The objective of this article is to bring together multiple strands of research to provide a comprehensive and integrated framework for the design and performance evaluation of indoor mmWave systems. This article introduces the framework with a status update on mmWave technology, including ongoing fifth generation (5G) wireless standardization efforts and then moves on to experimentally validated channel models that inform performance evaluation and deployment planning. Together these yield insights on indoor mmWave deployment strategies and system configurations, from feasible deployment densities to beam management strategies and necessary capacity extensions.

Wireless network virtualization is emerging as a promising technology for next-generation (5G) cellular networks. A key advantage of introducing virtualization in cellular networks is that wireless services can be decoupled from network resources (e.g., infrastructure and spectrum) so that multiple virtual networks can be built using a shared pool of network resources. This paper develops a theoretical framework for optimizing the resource allocation in virtualized cellular networks with heterogeneous coverage requirements. Specifically, we first formulate a chance-constrained virtual resource allocation problem that aims at probabilistically guaranteeing virtual networks' downlink coverage and rate demand satisfaction while minimizing resource over-provisioning in the presence of uncertainty in user equipment (UE) locations and channel conditions. Thereafter, we derive a closed-form expression for the downlink rate coverage probability of a typical virtual network. With the closed-form expression, we design an efficient algorithm to solve the chance-constrained problem with affordable computation complexity. Furthermore, considering the possibility of lack of sufficient network resources to satisfy all virtual networks' demands, we design a prioritized virtual resource allocation scheme where virtual networks are built sequentially based on their given priorities. Our results demonstrate that the proposed stochastic virtualization framework outperforms existing deterministic virtualization frameworks in terms of probabilistically guaranteeing virtual networks' coverage and rate demand satisfaction.

Channel bonding is one promising approach to cope with rising WLAN data demand, given scarce spectrum resources. An access point (AP) can aggregate multiple contiguous channels to satisfy demand. We discuss how to optimally utilize available frequency bands under uncertainty in AP demands using two stochastic optimization frameworks: a static scheme which minimizes the total occupied bandwidth while satisfying the demand of each AP with probability at least β, and an adaptive scheme that allows adaptability of the bandwidth allocation in response to the AP demand variations. Given its complexity, we propose a novel framework to solve the adaptive stochastic optimization problem efficiently. The proposed framework exploits the special structure of the problem through decomposition into two subproblems. A particle swarm optimization (PSO)-based algorithm is tailored to the first-stage problem in order to obtain good solutions. The second-stage problem is further decomposed into several subproblems that can be solved independently in parallel. Our numerical results (i) demonstrate the advantages of stochastic compared to deterministic allocation, (ii) illustrate that the proposed framework reaches the optimal solution for the two-stage problem in few iterations, and (iii) explain the bandwidth-user satisfaction trade-off provided by the adaptive allocation approach.

Wireless network virtualization is emerging as an important technology for next-generation (5G) wireless networks. A key advantage of introducing virtualization in cellular networks is that service providers can robustly share virtualized network resources (e.g., infrastructure and spectrum) to extend coverage, increase capacity, and reduce costs. {However, the inherent features of wireless networks, i.e., the uncertainty in user equipment (UE) locations and channel conditions impose significant challenges on virtualization and sharing of the network resources.} In this context, we propose a stochastic optimization-based virtualization framework that enables robust sharing of network resources. Our proposed scheme aims at probabilistically guaranteeing UEs' Quality of Service (QoS) demand satisfaction, while minimizing the cost for service providers, with reasonable computational complexity and affordable network overhead.

Recently, tremendous progress has been made in self-interference cancellation (SIC) techniques that enable a wireless device to transmit and receive data simultaneously on the same frequency channel, a.k.a. in-band full-duplex (FD). Although operating in FD mode significantly improves the throughput of a single wireless link, it doubles the number of concurrent transmissions, which limits the potential for coexistence between multiple FD-enabled links. In this paper, we consider the coexistence problem of concurrent transmissions between multiple FD-enabled links with different SIC capabilities; each link can operate in either FD or half-duplex mode. First, we consider two links and formulate the interactions between them as a Bayesian game. In this game, each link tries to maximize its throughput while minimizing the transmission power cost. We derive a closed-form expression for the Bayesian Nash equilibrium and determine the conditions under which no outage occurs at either link. Then, we study the coexistence problem between more than two links, assuming that each link is only affected by its dominant interfering link. We show that under this assumption, no more than two links will be involved in a single game. Finally, we corroborate our analytical findings via extensive simulations and numerical results.

Software-defined cellular networks (SDCN) have been recently introduced to enable flexible cellular network design that facilitates fulfilling 5G design requirements. Placement of controllers within the SDCN plays a crucial role in optimizing its performance. In this paper, we study the controller placement problem in SDCN, considering the uncertainty in cellular user locations. Specifically, our contributions are as follows. First, we develop C3P2, a static joint stochastic controller placement and evolved node B (eNB)-controller assignment problem. The objective of C3P2is to minimize the number of controllers needed to control all eNBs, while ensuring that the response time to each eNB will exceed seconds with probability less than 1-β. Second, we develop CPPA, a joint stochastic controller placement and adaptive eNB-controller assignment problem. In contrast to C3P2, in CPPA the eNB-controller assignment adapts to variations in the eNB request rates, resulting from the variations in the cellular user locations. Finally, we use sample average approximation combined with various linearization techniques to solve and evaluate C3P2and CPPA under various system parameters. Our results demonstrate the advantages of (i) joint compared to sequential optimization, (ii) stochastic compared to deterministic optimization, and (iii) adaptive compared to static optimization.

Channel bonding is one promising approach to cope with rising WLAN data demand, given scarce spectrum resources. An access point (AP) can aggregate multiple contiguous channels to satisfy demand. Optimizing the network performance under deterministic demands has been well-studied. It is still an open question of how to optimally utilize available frequency bands under uncertainty in AP demands. We propose two approaches to tackle this problem using a stochastic optimization framework. First, we develop a static joint stochastic center frequency and bandwidth allocation scheme. The goal of this scheme is to minimize the total occupied bandwidth while satisfying the demand of each AP with probability at least ß. Second, we develop a two-stage joint stochastic center frequency and adaptive bandwidth allocation scheme. In contrast to the static scheme, the two-stage allocation scheme allows adaptability of the bandwidth allocated to each AP in response to its demand variation, while keeping the total occupied bandwidth fixed. Our numerical results demonstrate the advantages of (i) stochastic compared to naive allocation and (ii) adaptive compared to static allocation. They also explain the bandwidth-user satisfaction trade-off provided by the adaptive allocation approach.

To cope with increasing demand on wireless services, next-generation wireless systems are expected to use multiple radio access technologies, with different receive and transmit characteristics, operating over the same band of spectrum in a spatial-temporal neighborhood. This will make the RF front-ends susceptible to unprecedented adjacent-channel interference (Ad), which can jeopardize communication performance. In this paper, we propose a novel ACI-aware joint channel and power allocation framework that takes into account the receiver imperfections arising due to (i) imperfect image frequency rejection, and (ii) analog-to-digital converter aliasing. The proposed resource allocation framework aims at minimizing the number of allocated channels and the aggregate power transmitted while satisfying the rate demands of different links in a multi-RAT environment. The results demonstrate the criticality of receiver-characteristic awareness when designing resource allocation schemes for different types of networks. Also, the trade-off between channel allocation and power assignment is explained.

Software-defined networking (SDN) abstracts and centralizes the network control functions in a software entity that runs on a server, known as SDN controller. The controller needs to respond to its controlled elements in a strictly timely manner, and the controller placement has a prominent effect on its response time. Originally, all SDN architectures assumed a physical wired connection between the SDN controller and its controlled elements, and the controller placement problem (CPP) has been only studied under such wired settings. Recently, novel SDN architectures have been proposed in which a direct wireless connection is assumed between the controller and its controlled elements. In this paper, we consider the 'wireless CPP,' when the link between the controller and the controlled element is wireless. Specifically, our contributions are as follows. First, we propose two joint controller placement and assignment formulations, assuming wired links between the controllers and their controlled elements; the first formulation considers an average response time constraint, whereas the second one considers a per-link response time constraint. Then, using chance- constrained stochastic programming (CCSP), we extend our formulation to the case when the links between the controllers and their controlled elements are wireless. Finally, we evaluate our joint placement and assignment schemes under various system parameters. Our results demonstrate the advantage of our joint scheme, in terms of reducing the required number of controllers, compared to a recent sequential assignment and placement scheme in the literature. They also show the ability of our CCSP-based scheme in probabilistically satisfying the controllers response time constraints.

Since LTE in unlicensed spectrum (LTE-U) was proposed by Qualcomm, it has drawn considerable interest because of its potential to increase the capacity of existing LTE networks by utilizing existing infrastructure in the unlicensed band. But, Wi-Fi technology, already operating in the unlicensed 5 GHz band, creates several potential challenges for managing the activities of these two different technologies in the same band. In this context, we propose an adaptive coexistence scheme between LTE and WiFi by utilizing almost blank subframes (ABS). An ABS is an LTE subframe of duration 1 ms (containing two time slots of 0.5 ms duration) with reduced downlink activity. LTE allocates ABSs over 20 MHz channels in 5 GHz band to allow WiFi to access the spectrum. In the proposed coexistence scheme, each LTE cell optimally distributes ABSs over the frame to provide certain quality of service (QoS) guarantees for WiFi traffic while ensuring the performance of its own users.

Millimeter wave (mmWave) communication is a promising solution for providing high capacity wireless access to regions with high traffic demands. The main challenge of mmWave communications is the availability of directional line of sight links between access points and mobile devices which stochastically change due to high attenuation in mmWave propagation and severe blockage of mmWave links with obstacles such as human bodies. In this paper, a novel framework for optimizing the deployment of mmW access points, while being cognizant of the mobile devices orientation, is proposed. In the studied model, the locations of potential access points and users are assumed as predefined parameters while the orientation of the users is changing stochastically. To minimize the number of access points while satisfying the line of sight coverage of mobile devices, first, a joint access point placement and mobile device assignment problem is proposed, assuming that the orientation of each user is deterministically known. This formulation is then extended to the case in which the orientation of the user is stochastic. Finally, the proposed deterministic and stochastic joint access point placement and mobile device assignment schemes are evaluated under various system parameters. Simulation results demonstrate the advantage of the proposed stochastic scheme to the deterministic scheme, in terms of reducing the load on access points. Moreover, on average, the proposed stochastic scheme can increase the probability of user satisfaction up to 24% for 0.95 requested coverage probability compared to the deterministic case.

Resource sharing in mobile wireless networks has been employed to reduce costs, extend coverage, and ease the entry of new players in the market. The introduction of programmability and virtualization is expected to amplify these benefits of resource sharing. In this paper, we study a new virtualization-based paradigm for resource sharing in mobile wireless networks. Specifically, we consider the problem of resource allocation, particularly when user demands are uncertain. We formulate several two-stage sequential stochastic allocation schemes that provide tradeoffs between cost and user satisfaction. These allocation schemes are studied under different resource provider pricing models. Our simulations demonstrate that: First, while reducing cost significantly, virtualization considerably improves user satisfaction, and virtualization gains increase with the number of operators that share resources. Second, the improvements in cost, user satisfaction, and resource usage increase substantially with the level of user clustering.

Two promising solutions have been recently proposed to address the massive growth in mobile traffic and wireless devices: LTE-U and in-band full-duplex (FD) wireless. LTE-U extends the benefits of LTE-A to the unlicensed 5 GHz band, used mainly by Wi-Fi users. However, the uncertainty in Wi-Fi user activities makes provisioning QoS guarantees to LTE-U users challenging. On the other hand, FD wireless can double spectrum efficiency by enabling simultaneous transmission and reception over the same frequency band. Our objective in this paper is to exploit excess capacity of deployed Wi-Fi networks (operating in the 5 GHz band) to orchestrate a ‘robust’ virtual LTE-U network from a hybrid set of half-duplex (HD) and FD Wi-Fi access points (APs). Although the orchestrated LTE-U network does not support deterministic QoS guarantees, it is designed to provide prespecified probabilistic QoS guarantees (hence, it is robust). Towards achieving our goal, we develop novel stochastic resource allocation formulations that optimally orchestrate a virtual LTE-U network from a hybrid set of HD/FD APs with the minimum cost. We first consider the single small-cell problem and propose a stochastic formulation, which we refer to as CCLTEUsingle. Then, we study the multi-cell stochastic allocation problem and develop another formulation, which we refer to as CCLTEUmulti. Our formulations adopt a ‘chance-constrained stochastic programming’ approach. We derive the deterministic equivalent programs of CCLTEUsingleand CCLTEUmultiand evaluate them numerically under various system parameters.

Recently, tremendous progress has been made in self-interference cancellation (SIC) techniques that enable a wireless device to transmit and receive data simultaneously on the same frequency channel, a.k.a. in-band full-duplex (FD) communications. Although operating in a FD mode significantly improves the throughput of a single wireless link, it doubles the number of concurrent transmissions, which limits the potential for coexistence between multiple FD-enabled links. In this paper, we consider the problem of concurrent transmissions between two FD-enabled links with different SIC capabilities; each link can operate in either FD or half-duplex (HD) mode. Following a game-theoretic framework, we aim to determine the stable behavior (FD or HD) for the two coexisting links. To achieve this objective, we first analyze a simple normal form game between the two links, which provides some insight into the coexistence problem. It turns out that the outcome of this game depends on two factors: The amount of residual self-interference (due to imperfect SIC) and the external interference from one link on the other. To capture the impact of residual self-interference, we formulate a Bayesian game between two links with heterogeneous SIC capabilities. In this game, each link (player) tries to maximize its throughput while minimizing the transmission power cost. We derive the Bayesian Nash equilibrium for this game. Furthermore, we determine the conditions on the external interference under which no outage occurs at both links. Finally, we conduct simulations and USRP hardware experiments to corroborate our analytical findings.

Wireless transmissions are inherently vulnerable to jamming attacks. Frequency hopping (FH) and transmission rate adaptation (RA) have been separately used to mitigate jamming. When RA is used alone, it has been shown that a jammer who randomizes its power levels can force the transmitter toalwaysoperate at the lowest rate, by maintaining the average jamming power above a certain threshold. On the other hand, when only FH is used, a high throughput overhead is incurred due to frequent channel switching. In this paper, we propose to mitigate jamming by jointly optimizing the FH and RA techniques. This way, the transmitter can escape the jammer by changing its channel, adjusting its rate, or both. We consider a power-constrained “reactive-sweep” jammer who aims at degrading the throughput of the wireless link. The jammer sweeps through the set of channels, jamming a subset of them at a time, using the optimal jamming power. We model the interactions between the legitimate transmitter and jammer as a constrained zero-sum Markov game. The transmitter’s optimal defense strategy is derived by obtaining the equilibria of the constrained Markov game. This policy informs the transmitter when to hop to another channel and when to stay on the current channel. Furthermore, it gives the best transmission rate to use in both cases (hop or stay). The structure of the transmitter’s optimal policy is shown to be threshold type, whereby the transmitter stays on the same channel up to a certain number of time slots after which it hops. We analyze the “constrained Nash equilibrium” of the Markov game and show that the equilibrium defense strategy of the transmitter is deterministic. Numerical investigations show that the new scheme improves the average throughput and provides better jamming resiliency.

Rendezvous is a vital process for connection establishment and recovery in dynamic spectrum access (DSA) networks. Frequency hopping (FH) is an effective rendezvous method that does not rely on a predetermined control channel. Recently, quorum-based FH approaches have been proposed for enabling asynchronous rendezvous between two or more secondary users (SUs). In this paper, we consider two collocated secondary networks, each represented by a pair of SUs. Both networks try to rendezvous concurrently, each aiming at maximizing its rendezvous performance, as measured by the average time-to-rendezvous and the number of rendezvous opportunities. To study this form of coexistence rendezvous, we follow a non-cooperative combinatorial game-theoretic framework, which we refer to as CORE. In this framework, SUs have different preferences towards various available licensed channels. Assuming first that SUs are time-synchronized, we formulate the interactions between the two networks as a two-player symmetric combinatorial game. We show the existence and uniqueness of a finite-population evolutionary stable strategy for this game. Furthermore, we conjecture that the game attains a pure-strategy Nash equilibrium (NE) for a wide range of design parameters. We also show that when SU pairs have the same preference towards all available channels, our game is an exact potential game, and hence the sequential best-response update is guaranteed to converge to a pure-strategy NE. We then study the time-asynchronous rendezvous game when SU pairs have the same preference towards all available channels. In this case, the game is also shown to be an exact potential game.

Establishing communications between devices in a dynamic spectrum access (DSA) system requires the communicating parties to “rendezvous” before transmitting data packets. Frequency hopping (FH) is an effective rendezvous method that does not rely on a predetermined control channel. Previous FH-based rendezvous designs mainly target unicast rendezvous, and do not intrinsically support multicast rendezvous, where a group of nodes need to rendezvous simultaneously. Furthermore, these designs do not account for fast-primary user (PU) dynamics, leading to long time-to-rendezvous (TTR). In this paper, we exploit the uniform k-arbiter and Chinese Remainder Theorem quorum systems to develop three FH-based multicast rendezvous algorithms, which provide different tradeoffs between rendezvous efficiency (e.g., low TTR) and security (e.g., robustness to node compromise). Our rendezvous algorithms are tailored for asynchronous and spectrum-heterogeneous DSA systems. To account for fast PU dynamics, we develop an algorithm for adapting the proposed FH designs on the fly. This adaptation is done through efficient mechanisms for channel ordering and quorum selection. Our simulations validate the effectiveness of the proposed rendezvous algorithms, their PU detection accuracy, and their robustness to insider attacks.

Fading and shadowing along with the primary user dynamics make channel quality in dynamic spectrum access networks uncertain. Furthermore, the imperfect design of filters and amplifiers in wireless devices motivates the need for guard-bands (GBs) to prevent adjacent-channel interference. In this paper, we develop novel stochastic GB-aware sequential and batch channel assignment schemes that aim at maximizing the spectrum efficiency. In line with recent IEEE 802.11 and LTE standards, our schemes support bonding and aggregation. We propose two assignment models for each of the sequential and batch schemes: a static single-stage and an adaptive two-stage. In the static model, channel assignment is performed once such that the rate demands are probabilistically met. The adaptive model is a two-stage model, where the initial assignment may be corrected once uncertainties are partially revealed. We refer to our formulations of the sequential and batch static assignments as chance-constrained stochastic subset-s um problem (CSSP) and chance-constrained stochastic multiple s ubset-sum problem (CMSSP), respectively. Moreover, we develop stochastic formulations for the sequential and batch adaptive assignments, which we refer to as two-stage CSSP with recourse (CSSPR) and two-stage CMSSP with recourse (CMSSPR), respectively. Finally, we present computationally efficient simplified versions of CSSP and CSSPR with near-optimal performance.

Wireless transmissions are inherently broadcast and are vulnerable to jamming attacks. Frequency hopping (FH) and transmission rate adaptation (RA) have been used to mitigate jamming. However, recent works have shown that using either FH or RA (but not both) is inefficient against smart jamming. In this paper, we propose mitigating jamming by jointly optimizing the FH and RA techniques. We consider a power constrained “reactive-sweep” jammer who aims at degrading the goodput of a wireless link. We model the interaction between the legitimate transmitter and jammer as a zero-sum Markov game, and derive the optimal defense strategy. Numerical investigations show that the new scheme improves the average goodput and provides better jamming resiliency.

The uncertainty in channel quality due to fading and shadowing along with the unpredictability of primary user (PU) activity make channel assignment in opportunistic spectrum access (OSA) networks quite challenging. In this paper, we propose two per-link channel assignment models under channel uncertainty: a static single-stage and an adaptive two-stage. In the static model, channel assignment is performed once, such that the rate demands are met with a probability greater than a certain threshold. This model is appropriate for a distributed network with no centralized spectrum manager. The adaptive model is a two-stage assignment model, where the initial assignment may be corrected once the uncertainties are partially revealed, such that the excess spectrum is returned back to the spectrum manager. This adaptive model is more appropriate when a centralized spectrum manager is available. Our channel assignment algorithms account for adjacent channel interference (ACI) by introducing guard-bands between adjacent channels that are assigned to different links. These algorithms aim at maximizing the spectral efficiency, considering the impact of guard-bands. The static ACI-aware channel assignment problem is formulated as a chance-constrained stochastic subset-sum problem (CSSP), and the adaptive assignment problem is formulated as a two-stage chance-constrained stochastic subset-sum problem with recourse (CSSPR). We develop heuristic algorithms for both models and test their performance. Preliminary results demonstrate that the proposed heuristic algorithms are highly efficient.

The operation of a wireless network relies extensively on exchanging messages over a universally known channel, referred to as the control channel. The network performance can be severely degraded if a jammer launches a denial-of-service (DoS) attack on such a channel. In this paper, we design quorum-based frequency hopping (FH) algorithms that mitigate DoS attacks on the control channel of an asynchronous ad hoc network. Our algorithms can establish unicast as well as multicast communications under DoS attacks. They are fully distributed, do not incur any additional message exchange overhead, and can work in the absence of node synchronization. Furthermore, the multicast algorithms maintain the multicast group consistency. The efficiency of our algorithms is shown by analysis and simulations.

This paper makes preliminary steps at exploring a novel application of cognitive radios (CRs) for reliable satellite communications. We propose the use of dynamically adjusted frequency hopping (FH) sequences for satellite transmissions. Such sequences are more robust against smart eavesdropping and targeted interference than fixed FH sequences. In our approach, FH sequence is adjusted according to the outcome of out-of-band sensing, carried out by a CR module that resides in the satellite itself or at the receiving ground station. Our protocol, called OSDFH, relies on exploiting the spectrum sensing capabilities of CRs for proactive detection of channel quality. We analyze the characteristics of the proposed OSDFH using a finite state Markov chain (FSMC) framework. Level crossing rate (LCR) analysis is used to determine the transition probabilities of the Markov chain. These probabilities are then used to measure the “channel stability,” a metric that reflects the freshness of sensed channel interference. We use simulations to study the effects of different system parameters on the performance of our proposed protocol.

Wireless communications are prone to both unintentional and intentional RF interference. Such interference has significant impact on the reliability of packet transmissions. In this paper, we consider interference mitigation in frequency hopping (FH) systems. We employ cognitive radios (CRs) for proactive interference sensing in such systems. Through this proactive approach, we propose a scheme for dynamic adjustment of the FH sequence. Our protocol, called SSDFH, relies on exploiting the spectrum sensing capabilities of CRs for proactive detection of channel quality. We analyze the characteristics of the proposed SSDFH using a continuous-time Markov chain framework. Level crossing rate (LCR) analysis is used to determine the transition rates for the Markov chain, which are then used to measure the “channel stability,” a metric that reflects the freshness of sensed channel interference. The selection of different protocol parameters is studied by means of analysis. In particular, we provide a numerical procedure for determining the “optimal” total sensing time that minimizes the probability of “black holes.” We run simulations to study the performance of our proposed protocol.

We consider the problem of intelligent mobile target detection in a mobile sensor network, where targets have higher sensing ranges and speeds than the sensors. (i) We propose novel algorithms for the sensors to detect targets with larger sensing ranges and speeds than the sensors. Also, our algorithms can achieve much faster detection for the targets than the most recent algorithm in the literature. (ii) In case of targets with lower speeds than the sensors, we propose a simple coordination framework between the targets. (iii) We propose new performance measures to evaluate target detection systems, namely, the coverage area of the targets and the collected information by the targets about the surveillance area. According to these metrics, we show by simulation that our sensors movement strategies, and by using a comparable number of sensors, can solve the open problem and no target can evade from being detected irrespective of its sensing range and speed, and within an adaptable detection time. Our simulation also shows that our simple targets coordination strategy gives much better performance than the independent targets in terms of the above metrics.