This paper proposes FAS-LLM, a novel large language model (LLM)–based architecture for predicting future channel states in Orthogonal Time Frequency Space (OTFS)-enabled satellite downlinks equipped with fluid antenna systems (FAS). The proposed method introduces a two-stage channel compression strategy combining reference-port selection and separable principal component analysis (PCA) to extract compact, delay–Doppler–aware representations from highdimensional OTFS channels. These representations are then embedded into a Low Rank Adaptation (LoRA)-adapted LLM, enabling efficient time-series forecasting of channel coefficients. Performance evaluations demonstrate that FAS-LLM outperforms classical baselines including GRU, LSTM, and Transformer models, achieving up to 10 dB normalized mean squared error (NMSE) improvement and up to threefold root mean squared error (RMSE) reduction across prediction horizons. Furthermore, the predicted channels preserve key physical-layer characteristics, enabling near-optimal performance in ergodic capacity, spectral efficiency, and outage probability across a wide range of signal-to-noise ratios (SNRs). These results highlight the potential of LLM-based forecasting for delay-sensitive and energy-efficient link adaptation in future satellite IoT networks.

LEO satellite communication systems experience significant space-to-ground propagation delays, which lead to outdated channel state information (CSI). As a consequence, the CSI available at a given time instance reflects the channel conditions from several time instances earlier, a phenomenon known as channel aging. Therefore, accurate channel prediction is crucial. Existing channel prediction methods often rely on sequential models that predict the CSI at each future time instance based on the CSI of only the previous time instance. However, this approach tends to suffer from error accumulation over time, resulting in degraded prediction accuracy as the prediction horizon extends. To address this challenge, we propose a Transformer-Long Short-Term Memory (T-LSTM) channel predictor that combines the Transformer and LSTM architectures for future channel prediction. The model predicts the CSI at the current time instance by utilizing a sequence of outdated CSI from several instances earlier. Simulation experiments conducted on a practical doubly selective satellite channel model demonstrate that the proposed T-LSTM model effectively mitigates the impact of channel aging. Moreover, it improves the prediction accuracy of satellite communication channels affected by long propagation delays, specifically compared to LSTM and Transformer methods operating individually.

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In this paper, we study the problem of optimizing statistical beamforming design for each user in a two-antenna downlink multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) system. The transmitter only possesses statistical information in the form of covariance matrices for each user’s link. The statistical beamforming designs are derived in maximizing the ergodic sum rate considering both low and high signal-to-interference-plus-noise ratio (SINR) extreme scenarios. In addition to the analytical investigations, we also conduct MonteCarlo simulations in comparison and to affirm the accuracy of the derived ergodic sum rate expressions. Results show that the ergodic sum rate increases with the total transmit power and decreases with the power coefficient of near users. Comparing the optimized MISO-NOMA system with conventional MISO-OMA scheme, it is demonstrated that MISO-NOMA can significantly improve the ergodic sum rate.

In this work, we employ a constrained risk-sensitive deep reinforcement learning (CRS-DRL) approach for joint scheduling in a dynamic multiplexing scenario involving enhanced mobile broadband (eMBB) and ultra-reliable low-latency communications (URLLC). Our scheduling policy minimizes the adverse impact of URLLC puncturing on eMBB users while satisfying URLLC requirements. Conventional DRL-based algorithms for eMBB/URLLC scheduling prioritize maximizing the expected return. However, for URLLC mission-critical applications, it is crucial to explicitly avoid catastrophic scheduling failures associated with the long tail of the reward distribution. Therefore, robust management of such uncertainties and risks is imperative. Our proposed CRS-DRL algorithm incorporates the conditional Value-at-Risk (CVaR) as the risk criterion for optimization. A URLLC queuing mechanism is considered to decrease the URLLC drops and increase eMBB throughput compared to the instant scheduling policy. Our architecture is based on the actor-critic model but considers a transfer function to obtain feasible solutions of the unconstrained actor network, and the critic predicts the entire distribution over future returns instead of simply the expectation. Numerical results indicate that our CRS-DRL algorithm, under varying CVaR levels, achieves similar expected returns but reduces long-tail behavior for long-term rewards compared to the risk-neutral approach.

This paper proposes an edge-assisted crowdsourced live video transcoding approach where the transcoding capabilities of the edge transcoders are unknown and dynamic. The resilience and trustworthiness of highly unstable transcoders in decision making are characterized with mean-variance-based measures to avoid making highly risky decisions. The risk level of each device’s situation is assessed and two upper confidence bounds of the variance of transcoding performance are presented. Based on the derived bounds and by leveraging the contextual information of devices, two risk-aware contextual learning schemes are developed to efficiently estimate the transcoding capabilities of the edge devices. Combining context awareness and risk sensitivity, a novel transcoding task assignment and viewer association algorithm is proposed. Simulation results demonstrate that the proposed algorithm achieves robust task offloading with superior network utility performance as compared to the linear upper confidence bound and the risk-aware mean-variance upper confidence bound-based algorithms. In particular, an epoch-based task assignment strategy is designed to reduce the task switching costs incurred in assigning the same transcoding task to different transcoders over time. This strategy also reduces the computational time needed. Numerical results confirm that this strategy achieves up to 86.8% switching costs reduction and 92.3% computational time reduction.

Robustness of physiological parameters obtained from photoplethysmographic (PPG) signals is highly dependent on a signal quality that is often affected by the motion artefacts (MAs) generated during physical activity. This study aims to suppress MAs and obtain reliable physiological readings using the part of the pulsatile signal, captured by a multi-wavelength illumination optoelectronic patch sensor (mOEPS), that minimizes the residual between the measured signal and the motion estimates obtained from an accelerometer. The minimum residual (MR) method requires the simultaneous collection of (1) multiple wavelength data from the mOEPS, and (2) motion reference signals from a triaxial accelerometer attached to the mOEPS. The MR method suppresses those frequencies associated with motion in a manner that is easily embedded on a microprocessor. The performance of the method in reducing both in-band and out-of-band frequencies of MAs is evaluated through two protocols with 34 subjects engaged in the study. The MA-suppressed PPG signal, obtained through MR, enables the calculation of the heart rate (HR) with an average absolute error of 1.47 beats/min for the IEEE-SPC datasets, and the calculation of HR and respiration rate (RR) to 1.44 beats/min and 2.85 breaths/min respectively for our in-house datasets. Oxygen saturation (SpO$_{2}$) levels calculated from the minimum residual wave forms were consistently$\geq \text{95}\%$. The comparison with the reference HR and RR show errors with an absolute accuracy of$< \text{5}\%$and the Pearson correlation ($R$) for HR and RR are 0.9976 and 0.9118, respectively. These outcomes demonstrate that MR is capable of effective suppression of MAs for a range of physical activity intensities and to achieve real-time signal processing for wearable health monitoring.

This paper studies the allocation of shared resources between ultra-reliable low-latency communication (URLLC) and enhanced mobile broadband (eMBB) in the emerging 5G and beyond cellular networks. In this paper, we design a unique queuing mechanism for the joint eMBB/URLLC system. The aim is to flexibly schedule URLLC traffic to enhance the total eMBB throughput and the reliability of URLLC packets (i.e., the probability of not dropping URLLC packets in each mini-slot) while maintaining a satisfactory transmission latency as per the 3GPP requirements. Precisely, by deriving the steady-state probabilities of URLLC queue backlog analytically, we formulate a stochastic optimization problem to maximize the total normalized eMBB throughput and the URLLC utility. Due to the stochastic nature of the objective function, it is expensive to evaluate it for any set of inputs, and thus the Bayesian optimization is applied to obtain the optimal results of such a black-box objective function. Numerical results demonstrate that the proposed queuing mechanism never violates the latency requirement of the URLLC services but improves the reliability. It also enhances the total normalized eMBB throughput as compared to the method without queuing.

Quality photoplethysmographic (PPG) signals are essential for accurate physiological assessment. However, the PPG acquisition process is often accompanied by spurious motion artefacts (MAs), especially during medium-high intensity physical activity. This study proposes a generative adversarial network (PPG-GAN) to create de-noised versions of measure PPG signals. The Adaptive Notch Filtration (ANF) algorithm, which enables the extraction of accurate heart rates (HR) and respiration rates (RR) from PPG signals, is used as the approximate reference signal to train the PPG-GAN. The generated PPG signals from test inputs provide a heart rate (HR) with a mean absolute error of 1.68 bpm for the IEEE-SPC dataset. A comparison with gold-standard HR and RR measurements, for our in-house dataset, show the errors in absolute value of less than 5%. The generated PPG signals, for the test clips, show a very strong correlation with their reference values, R ≈ 0.98. The results suggest that PPG-GAN could be a paradigm for MA-free PPG signal processing specifically for personal healthcare, even during high intensity activity.

For block fading channels with uncertainty in channel distribution knowledge, we propose and optimize a statistical measure as a way to surely assess reliability in finite-block communications regime. In particular, the confidence level in guaranteeing average block-error rate lower than a specific target is introduced and maximized to find the optimal blocklength, aiming to meet the strict requirements of ultra-reliable low latency communications (URLLC). In order to compute the confidence level, non-parametric learning algorithms are employed for channel modeling with a limited number of training samples. Bayesian optimization, i.e., the tool for black-box optimization, is applied to solve the problem in the absence of the closed form of the confidence level.

As an important service for 5G communications, ultra-reliable low-latency communications (URLLC) support emerging mission-critical applications, such as factory automation and autonomous driving. For such applications, the probability of failing to successfully transmit URLLC packets should be below a certain threshold. However, in the case of limited knowledge of the channel distribution, achieving such a reliability target requires precise channel modeling. In this paper, we study applying a non-parametric statistical learning approach (i.e. kernel density estimation (KDE)) to estimate the information of the wireless transmission environment (i.e. the probability density function of the channel distribution). Based on the estimated cumulative distribution function, a transmission rate control technique has been developed and the corresponding reliability has been investigated using two measures representing the average performance and the confidence level. Moreover, this paper compares the performance of KDE and traditional empirical estimation scheme. The results show that KDE achieves a high level of confidence in guaranteeing the reliability constraint despite of the limited number of training data when choosing a suitable kernel bandwidth.

The classical multi-armed bandit (MAB) framework studies the exploration-exploitation dilemma of the decisionmaking problem and always treats the arm with the highest expected reward as the optimal choice. However, in some applications, an arm with a high expected reward can be risky to play if the variance is high. Hence, the variation of the reward should be considered to make the arm-selection process risk-aware. In this letter, the mean-variance metric is investigated to measure the uncertainty of the received rewards. We first study a risk-aware MAB problem when the reward follows a Gaussian distribution, and a concentration inequality on the variance is developed to design a Gaussian risk aware-upper confidence bound algorithm. Furthermore, we extend this algorithm to a novel asymptotic risk aware-upper confidence bound algorithm by developing an upper confidence bound of the variance based on the asymptotic distribution of the sample variance. Theoretical analysis proves that both proposed algorithms achieve the O(log(T)) regret. Finally, numerical results demonstrate that our algorithms outperform several risk-aware MAB algorithms.

This paper studies a resource allocation problem where a set of users within a specific region is served by cloud radio access network (C-RAN) structure consisting of a set of base-band units (BBUs) connected to a set of radio remote heads (RRHs) equipped with a large number of antennas via limited capacity front-haul links. User association to each RRH, BBU and front-haul link is essential to achieve high rates for cell-edge users under network limitations. We introduce two types of optimization variables to formulate this resource allocation problem: (i) C-RAN user association factor (UAF) including RRH, BBU and front-haul allocation for each user and (ii) power allocation vector. The formulated optimization problem is non-convex with high computational complexity. An efficient two-level iterative approach is proposed. The higher level consists of two steps where, in each step, one of these two optimization variables is fixed to derive the other. At the lower level, by applying different transformations and convexification techniques, the optimization problem in each step is broken down into a sequence of geometric programming (GP) problems to be solved by the successive convex approximation (SCA). Simulation results reveal the effectiveness of the proposed approach to increase the total throughput of network, specifically for cell-edge users. It outperforms the traditional user association approach, in which, each user is first assigned to the RRH with the largest average value of signal strength, and then, based on this fixed user association, front-haul link association and power allocation are optimized.

With the rapid development of social networks and high-quality video sharing services, the demand for delivering large quantity and high quality contents under stringent end-to-end delay requirement is increasing. To meet this demand, we study the content caching problem modelled as a Markov decision process in the network edge server when the popularity profiles are unknown and time-varying. In order to adapt to the changing trends of content popularity, a context-aware popularity learning algorithm is proposed. We prove that the learning error of this scheme is sublinear in the number of requests. In light of the learned popularities, a reinforcement learning-based caching scheme is designed on top of the state-action-reward-state-action algorithm with a function approximation. A reactive caching algorithm is also proposed to reduce the complexity. The time complexities of both the caching schemes are studied to demonstrate their feasibility in real time systems and a theoretical analysis is performed to prove that the cache hit rate of the reactive caching algorithm asymptotically converges to the optimal cache hit rate. Finally the simulations are presented to demonstrate the superiority of the proposed algorithms.

In this treatise, we derive tractable closed-form expressions for the outage probability of the single cell multi-carrier non-orthogonal multiple access (MC-NOMA) downlink, where the transmitter side only has statistical CSI knowledge. In particular, we analyze the outage probability with respect to the total data rates (summed over all subcarriers), given a minimum target rate for the individual users. The calculation of outage probability for the distant user is challenging, since the total rate expression is given by the sum of logarithmic functions of the ratio between two shifted exponential random variables, which are dependent. In order to derive the closed-form outage probability expressions both for two subcarriers and for a general case of multiple subcarriers, efficient approximations are proposed. The probability density function (PDF) of the product of shifted exponential distributions can be determined for the near user by the Mellin transform and the generalized upper incomplete Fox's H function. Based on this PDF, the corresponding outage probability is presented. Finally, the accuracy of our outage analysis is verified by simulation results.

In this paper, we analyze the outage probability of the multi-user multiple-input single-output (MISO) downlink system by combining the non-orthogonal multiple access (NOMA) scheme. We derive tractable closed-form outage expressions given a minimum target rate for the individual users for the case of two antennas, by modeling cumulative distribution function (CDF) of received signal-to interference plus noise ratio (SINR). Simulation results illustrate the outage performance for different power allocation scenarios and verify the accuracy of our outage probability analysis.

In this paper, we propose a multiple access scheme for machine-type communications for which high spectral efficiency and massive connectivity are demanded. To meet these requirements, we enhance the proposed access scheme with the reconfigurability feature to properly divide each time frame to three segments of grant-based NOMA, grant-based OMA, and random access. To obtain the length of each segment, an optimization problem is formulated which is solved by dividing it into two sub-problems. The first sub-problem nominates devices for the NOMA transmissions, while the second sub-problem derives the length of each segment as well as the the parameter of the random access-based scheme.

In this paper, a novel framework for dynamic multiple access technology selection among orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) techniques is proposed. For this setup, a joint resource allocation problem is formulated in which a new set of access technology selection parameters along with power and subcarrier are allocated for each user based on each user's channel state information. Here, a novel utility function is defined to take into account the rate and costs of access technologies. This cost reflects both the complexity of performing successive interference cancellation and the complexity incurred to guarantee a desired bit error rate. This utility function can inherently capture the tradeoff between OMA and NOMA. Due to the non-convexity of the proposed resource allocation problem, a successive convex approximation is developed in which a two-step iterative algorithm is applied. In the first step, called access technology selection, the problem is transformed into a linear integer programming problem, and then, in the second step, a nonconvex problem, referred to power allocation problem, is solved via the difference-of-convex-functions (DC) programming. Moreover, the closed-form solution for power allocation in the second step is derived. For diverse network performance criteria such as rate, simulation results show that the proposed new dynamic access technology selection outperforms single-technology OMA or NOMA multiple access solutions.

In this paper, we present a reconfigurable MAC scheme where the partition between contention-free and contention-based regimes in each frame is adaptive to the network status leveraging reinforcement learning. In particular, to support a virtualized wireless network consisting of multiple slices, each having heterogeneous and unsaturated devices, the proposed scheme aims to configure the partition for maximizing network throughput while maintaining the slice reservations. Applying complementary geometric programming and monomial approximations, an iterative algorithm is developed to find the optimal solution. For a large number of devices, a scalable algorithm with lower computational complexity is also proposed. The partitioning algorithm requires the knowledge of the device traffic statistics. In the absence of such knowledge, we develop a learning algorithm employing Thompson sampling to acquire packet arrival probabilities of devices. Furthermore, we model the problem as a thresholding multi-armed bandit and propose a threshold-based reconfigurable MAC algorithm, which is proved to achieve the optimal regret bound.

In this paper, we study power allocation for downlink multi-carrier non-orthogonal multiple access (MC-NOMA)systems and examine the effects of residual cancellation errors resulting from imperfect successive interference cancellation (SIC) on the system performance. In the presence of random SIC errors, we study outage probability of minimum reserved rate for individual user and formulate outage-constrained robust optimization to minimize the total transmit power. Since the problem is non-convex due to probabilistic constraints, complementary geometric programming (CGP) and arithmetic geometric mean approximation (AGMA) technique are employed to transform it into a convex form. An efficient iterative algorithm with low computational complexity is developed to solve the optimization problem. Simulation results demonstrate the performance of robust MC-NOMA with imperfect SIC and compare that to non-robust MC-NOMA and orthogonal multiple access (OMA) schemes.

We study a resource allocation problem for the uplink of a virtualized massive multiple-input multiple-output system, where the antennas at the base station are priced and virtualized among the service providers (SPs). The mobile network operator (MNO) who owns the infrastructure decides the price per antenna, and a Stackelberg game is formulated for the net profit maximization of the MNO, while the minimum rate requirements of SPs are satisfied. To solve the bi-level optimization problem of the MNO, we first derive the closed-form best responses of the SPs with respect to the pricing strategies of the MNO, such that the problem of the MNO can be reduced to a single-level optimization. Then, via transformations and approximations, we cast the MNO's problem with integer constraints into a signomial geometric program (SGP), and we propose an iterative algorithm based on the successive convex approximation (SCA) to solve the SGP. Simulation results show that the proposed algorithm has performance close to the global optimum. Moreover, the interactions between the MNO and SPs in different scenarios are explored via simulations.

We thoroughly investigate the downlink beamforming problem of a two-tier network in a reversed time-division duplex system, where the interference leakage from a tier-2 base station (BS) toward nearby uplink tier-1 BSs is controlled through pricing. We show that soft interference control through the pricing mechanism does not undermine the ability to regulate interference leakage while giving flexibility to sharing the spectrum. Then, we analyze and demonstrate how the interference leakage is related to the variations of both the interference prices and the power budget. Moreover, we derive a closed-form expression for the interference leakage in an asymptotic case, where both the charging BSs and the charged BS are equipped with a large number of antennas, which provides further insights into the lowest possible interference leakage that can be achieved by the pricing mechanism.

Long-term evolution (LTE) operation in the unlicensed spectrum is a promising solution to address the scarcity of licensed spectrum for cellular networks. Although this approach brings higher capacity for LTE networks, the Wi-Fi performance operating in this band can be significantly degraded. To address this issue, we consider a coordinated structure, in which both networks are controlled by a higher level network entity. In such a model, LTE users can transmit in the assigned time-slots, while Wi-Fi users can compete with each other by using p-persistent carrier sense multiple access (CSMA) in their exclusive timeshare. In an unsaturated network, at each duty cycle, the timedivision multiple access (TDMA) scheduling for LTE users and p values for Wi-Fi users should be efficiently updated by the central controller. The corresponding optimization problem is formulated and an iterative algorithm is developed to find the optimal solution using complementary geometric programming and monomial approximations. Aiming to address the qualityof-service assurance for LTE users, an upper bound for average delay of these users is obtained. This analysis could be a basis for the admission control of LTE users in unlicensed bands. The simulation results reveal the performance gains of the proposed algorithm in preserving the Wi-Fi throughput requirement.

With dual connectivity, a mobile user can be served by a macro base station (MBS) and a pico base station (PBS) simultaneously. In this paper, we address the problem of optimizing user-PBS association and power allocation in the uplink such that the network can serve the users' demand at the minimum cost, where the PBSs are subject to backhaul capacity limitations and minimum rate requirements of users. We show that this non-convex problem can be formulated as a signomial geometric programming (SGP) whose solution can be found by solving a series of geometric programming (GP) problems. Simulation results are provided to demonstrate traffic offloading trend to PBSs for different cost and backhaul capacity settings, confirming the effectiveness of the proposed iterative algorithm. They also show that the output of the proposed algorithm closely matches the global optimal solution with affordable complexity.

Due to the increasing demand for mobile traffic, the unlicensed band operation for LTE is proposed by mobile operators. Although by using this approach higher capacity can be achieved for LTE, performance of other wireless technologies operating in this band such as WiFi can be degraded significantly. In order to enable efficient LTE/WiFi coexistence, we consider a coordinated structure via a virtual network entity. LTE users can transmit in the assigned time-slots, while WiFi users can compete with each other by using p-persistent CSMA in their exclusive time-share. In an unsaturated network, at each duty cycle, the TDMA scheduling for LTE users and p values for WiFi users are updated to maximize the overall network throughput subject to a constraint on the minimum acceptable throughput for WiFi. The corresponding optimization problem is formulated and an iterative algorithm is developed to find the optimal solution using complementary geometric programming (CGP) and monomial approximations. The simulation results reveal the performance gains of the proposed algorithm in preserving the WiFi throughput requirement.

In this work, we investigate the uplink resource allocation problem for virtualized wireless networks (VWNs) supported by multi-carrier non-orthogonal multiple access (MC-NOMA) and present a sensitivity analysis of such a system to imperfect successive interference cancellation (SIC) and various system parameters. The proposed algorithm for power and sub-carrier allocation is derived from the non-convex optimization minimizing power subject to rate and sub-carrier reservations, for which an optimal solution is NP-hard. To develop an efficient solution, we decompose the optimization into separate power and sub-carrier allocation problems and propose an iterative algorithm based on successive convex approximation and complementary geometric programming. Simulation results demonstrate that compared to orthogonal multiple access, for imperfect SIC with residual interference even up to 10%, the proposed algorithm for MC-NOMA can offer significant improvement in spectrum and power efficiency.

In this paper, we address a power-efficient resource allocation problem in virtualized wireless networks (VWNs) using non-orthogonal multiple access (NOMA). In this set-up, the resources of one base station (BS) are shared among different service providers (slices), where the minimum reserved rate is considered for each slice for guaranteeing their isolation. The formulated resource allocation problem aiming to minimize the total transmit power subject to the isolation constraints is non-convex and suffers from high computational complexity. By applying complementary geometric programming (CGP) to convert the non-convex problem into the convex form, we develop an efficient iterative approach with low computational complexity to solve the proposed problem. Illustrative simulation results on the performance evaluation of VWN using OFDMA and NOMA indicate significant performance improvement in the VWN when NOMA is used.

This paper proposes hybrid TDMA-CSMA for virtualized wireless networks, aiming to meet their isolation requirements. In this scheme, high-load users with non-empty queues are proper and potential candidates for TDMA, while others can compete using p-persistent CSMA. At each superframe, AP decides on TDMA-CSMA scheduling by taking into account traffic parameters of users and slice reservations to maximize the network utilization, while maintaining slice isolation. The corresponding optimization problem is formulated to dynamically schedule users for TDMA phase and optimally pick p parameter for remaining CSMA users. Using complementary geometric programming (CGP) and monomial approximations, an iterative algorithm is developed to find the optimal solution. The simulation results reveal the performance gains of the proposed algorithm in improving the throughput and keeping isolation in a virtualized wireless network.

This paper investigates the resource allocation problem for a virtualized wireless network (VWN) in which each base station (BS) is equipped with a large number of antennas and due to the pilot contamination error, the perfect estimation of channel state information (CSI) is not available. In this case, the duration of pilot sequence transmission plays a critical role on the achieved VWN throughput. Therefore, we consider this parameter as a new optimization variable and propose a novel utility function for the resource allocation problem. The proposed optimization problem is non-convex with high computational complexity. To address this issue, by applying relaxation and variable transformation techniques, we propose a two-step iterative algorithm in which the allocation of power, sub-carrier and number of antennas is first established and then used to optimize the pilot duration. Simulation results reveal that proper pilot duration design improves the VWN performance.

This paper proposes a delay-aware resource provisioning policy for virtualized wireless networks (VWNs) to minimize the total average transmit power while holding the minimum required average rate of each slice and maximum average packet transmission delay for each user. The proposed cross-layer optimization problem is inherently non-convex and has high computational complexity. To develop an efficient solution, we first transform cross-layer dependent constraints into physical layer dependent ones. Afterwards, we apply different convexification techniques based on variable transformations and relaxations, and propose an iterative algorithm to reach the optimal solution. Simulation results illustrate the effects of the required average packet transmission delay and minimum average slice rate on the total transmission power in VWN.

This paper investigates the dynamic resource provisioning with queue stability in wireless virtualized networks (WVN). Aiming to maximize the total average rate of WVN over a transmission frame, a dynamic resource provisioning policy is proposed, while a minimum average required rate of each slice and a stable-queue constraint of WVN are preserved. Based on Lyapunov drift-plus-penalty algorithm and variable transformation techniques, an iterative algorithm is proposed for joint power and sub-carrier allocation. Performance of the proposed algorithm is evaluated by simulations performed investigate the effects of various system parameters on the average rate of WVN and queue stability.

This paper presents an adaptive hybrid TDMA-CSMA MAC protocol to improve network performance and isolation among service providers (SPs) in a virtualized 802.11 network. Aiming to increase network efficiency, wireless virtual-ization provides the means to slice available resources among different SPs, with an urge to keep different slices isolated. Hybrid TDMA-CSMA can be a proper MAC candidate in such scenario benefiting from both the TDMA isolation power and the CSMA opportunistic nature. In this paper, we propose a dynamic MAC that schedules high-traffic users in the TDMA phase with variable size to be determined. Then, the rest of active users compete to access the channel through CSMA. The objective is to search for a scheduling that maximizes the expected sum throughput subject to SP reservations. In the absence of arrival traffic statistics, this scheduling is modeled as a multi-armed bandit (MAB) problem, in which each arm corresponds to a possible scheduling. Due to the dependency between the arms, existing policies are not directly applicable in this problem. Thus, we present an index-based policy where we update and decide based on learning indexes assigned to each user instead of each arm. To update the indexes, in addition to TDMA information, observations from CSMA phase are used, which adds a new exploration phase for the proposed MAB problem. Throughput and isolation performance of the proposed self-exploration-aided index-based policy (SIP) are evaluated by numerical results.

This paper presents a channel-aware access scheme for Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) aiming to take advantage of multi-user diversity and improve throughput, while supporting distributed and asynchronous operation. By dynamically adjusting the contention window of each station (STA) according to its channel state, this method prioritizes STAs that gain most from using a channel, and hence, enhances channel utilization in comparison with a simple random access scheme. To model the proposed Adaptive CSMA/CA (A-CSMA/CA) protocol, a three-dimensional Markov chain is developed. With the aid of such model, performance of the proposed A-CSMA/CA is analytically studied in terms of saturation throughput. Furthermore, illustrative results confirm that A-CSMA/CA significantly improves the throughput, specifically in a large network.

This paper studies joint resource provisioning and admission control in wireless virtualized networks (WVN), where one base station of an OFDMA-based wireless network is virtualized into two types of slices with resource-based and rate-based reservations. Aiming to maximize the total rate of WVN, first, the resource provisioning optimization problems are formulated by guaranteeing a minimum requirement for each slice. Via constraint relaxation and variable transformations, an iterative algorithm is developed for power and sub-carrier allocation. Due to the channel variations, WVN suffers from non-zero outage probability, i.e., slice requirements cannot always be met. To prevent this issue, we present an admission control algorithm in which slice requirements are dynamically adjusted based on channel state information. The simulation results demonstrate the effectiveness of our proposed algorithms.

Advanced Distribution Automation (ADA) is one of the key applications of the Smart Grid (SG). Since Narrow-band Power Line Communications (NB-PLC) can offer a cost-effective communication infrastructure supporting data acquisition and automation, it has appeared to be one of the most promising technologies to enable ADA. However, the applicability of NB-PLC needs to be investigated carefully since this technology still exhibits some limitations related to throughput, signal attenuation/distortion and reliability in ADA scenarios. This paper attempts to give a reasonable data rate estimation by considering realistic ADA infrastructures/applications and IEC 61850 as the primary data modeling and communications standard for ADA. Effects of channel contention at Medium Access Control (MAC) layer on the achievable throughput of existing NB-PLC technologies are investigated. The data rate estimation and throughput analysis are then used to evaluate the feasibility of NB-PLC in supporting ADA applications.

In a dense WLAN deployment, the interfering wireless access points (APs) need to efficiently share the spectrum, and hence, avoid low-performance experience of users due to high collision rates and long backoff overheads. In this paper, we aim to propose a channel assignment scheme in which APs can self-configure their channel choices to mitigate interference and thereby maximize network throughput. Aiming to minimize interference sum utility, a channel assignment problem is formulated as a non-convex optimization problem and solved using difference-of-convex-functions (DC) programming. Subsequently, two distributed algorithms are developed in which each AP independently adapts its channel selection to the optimal values over time by measuring the received interference in different channels. The convergence, complexity, and efficiency of the developed algorithms are studied by simulation results.

This paper presents an adaptive access scheme for Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) aiming to take advantage of multi-user diversity and improve throughput, while supporting distributed and asynchronous operation. By assigning channel-adaptive access probabilities to different users, this method prioritizes users who gain most from using a channel, and hence, improves channel utilization in comparison with a simple random access scheme. Furthermore, in this method, access probabilities are designed to achieve long-term fairness by keeping a same average access probability for all users. Performance of the proposed adaptive CSMA/CA is evaluated in terms of collision probability and saturation throughput by analysis and simulation. Illustrative and analytical results show that A-CSMA/CA significantly improves the throughput by controlling contention among users and decreasing the collision probability, specifically in a large network.

This paper presents a CSMA-based opportunistic spectrum access for secondary users (SUs) from a game-theoretic perspective. A key requirement for efficient design of CSMA-based access schemes for SUs is to address competition among SUs. Thus, to enable contention control in consideration of competition among SUs, an adaptive SU access approach based on a modified CSMA scheme is presented in which each SU accesses multiple idle frequency-slots of a licensed frequency band with different probabilities. The problem of finding optimal access probabilities of SUs is cast in a game-theoretic framework to highlight the issues of competition and cooperation among SUs. Subsequently, the existence, uniqueness and efficiency of Nash Equilibrium (NE) are investigated. To improve the efficiency of the unique NE in the competitive design, the game is transformed into a more cooperative framework exploiting a pricing mechanism. Finally, an algorithm based on the best response dynamics is developed in which each SU independently updates its access probabilities until convergence to the unique NE.

This paper presents a study on opportunistic spectrum access for secondary users (SUs) from a game-theoretic learning perspective. In consideration of the random return of primary users, it is assumed that a SU dynamically hops over multiple idle frequency-slots of a licensed frequency band, each with an adaptive activity factor. The problem of finding optimal activity factors of SUs is cast in a game-theoretic framework and is formulated as a potential game. Subsequently, the existence, feasibility and optimality of Nash Equilibrium (NE) are investigated analytically. Furthermore, an algorithm is developed in which each SU independently adjusts its activity factors based on the best response dynamics by learning other SUs' behavior from locally available information. Aiming to establish stability for the proposed algorithm, the convergence with probability 1 to an arbitrarily small neighborhood of the globally optimal solution is investigated with analysis and simulation.

This paper considers opportunistic spectrum access for secondary users (SUs) from an adaptive learning perspective. A SU dynamically hops over multiple idle frequency-slots of a licensed frequency band, each with an adaptive activity factor. Aiming to determine the optimal activity factors of SUs, an algorithm is developed, in which each SU independently adjusts its activity factors by learning other SUs' behavior from locally available information. Due to the error-prone learning procedure, the proposed algorithm is interpreted as a stochastic gradient descent method. In order to establish stochastic stability for the proposed algorithm, the convergence with probability of 1 and also convergence rate are investigated with analysis and simulation.

This paper presents an adaptive hopping transmission strategy for secondary users (SUs) to access temporarily idle frequency-slots of a licensed frequency band in consideration of the random return of primary users (PUs), aiming to maximize the overall SU throughput. A SU dynamically hops over multiple idle frequency-slots, each with an adaptive activity factor to avoid high-risk data loss due to possible PU return. SU activity factor optimization problems are formulated to develop the optimal opportunistic spectrum access (OSA) algorithms for SUs based on the Lagrange dual decomposition method. Subsequently, a fully distributed learning-based OSA algorithm is developed in which each SU independently adapts its activity factors to the optimal values over time by learning other SUs' behavior from locally available information. The convergence and convergence rate that characterize its asymptotic behavior and efficiency are analyzed. It is shown that the proposed learning-based OSA algorithm converges with probability of 1 to the optimal solution. Illustrative results confirm its effectiveness and performance gain as compared to existing OSA schemes.

This paper presents an adaptive hopping transmission strategy for secondary users (SUs) to access temporarily idle frequency-slots of a licensed frequency band in consideration of the random return of primary users (PUs), aiming to maximize the overall SU throughput. A SU dynamically hops over multiple idle frequency-slots, each with an adaptive activity factor so that possible PU return in a frequency-slot may destroy only a small fraction of the SU transmission that can be recovered by erasure-correction coding. SU activity factor optimization problems are formulated to develop adaptive SU access algorithms for both centralized and distributed structures. Numerical results confirm the effectiveness and demonstrate performance gains of the proposed approach as compared to existing schemes.

This paper proposes efficient cooperative cyclostationary spectrum sensing schemes in which each secondary-user (SU) performs single-cycle (SC) cyclostationary detection for fast and simple implementation, while collaboration between SUs in final decision on the presence or absence of the primary-user (PU) is explored to improve its performance. As the SUs simultaneously measure the spectral correlation functions at different cycle-frequencies (CF) and exchange their information regarding the measured results, a sufficient number of CFs are effectively examined with parallel searching, which makes the proposed cooperative spectrum sensing more reliable. This paper presents another look at performance evaluation of cyclostationary detectors in terms of deflection coefficients. Outage probability of deflection coefficient is defined as a measure to compare the performance of different cyclostationary detectors in a fading channel. Furthermore, performance of the proposed schemes in terms of false-alarm and detection probabilities is evaluated by analysis and simulation in AWGN and fading channels. Illustrative and analytical results show that the proposed schemes outperform both SC and multi-cycle (MC) cyclostationary detectors, especially in fading channels.

The paper proposes efficient cooperative cyclostationary spectrum sensing schemes in which each secondary user (SU) performs single-cycle cyclostationary detection for fast and simple implementation, while collaboration between SUs in final decision on the presence or absence of the primary user (PU) is explored to improve its performance. As the SUs simultaneously measure the spectral correlation functions at different cycle frequencies (CF) and exchange their information regarding the measured results, a sufficient number of CFs are effectively examined in a short period of time because of parallel searching, which makes the proposed cooperative spectrum sensing more reliable and faster. Performance of the proposed schemes in terms of false-alarm and detection probabilities and deflection coefficients is evaluated by analysis and simulation in AWGN, fading and shadowing channels. Illustrative results show that the proposed schemes outperform both single-cycle (SC) and multi-cycle (MC) cyclostationary detectors, especially in fading and shadowing channels.

This paper presents a study on interference caused by Secondary Users (SUs) due to miss-detection and its effects on the capacity-outage performance of the Primary User (PU) in a cognitive network for two scenarios of beacon transmitter placement: beacon transmitter located at PU transmitter or at PU receiver. Interference analysis shows that aggregate interference power from SUs has a Gamma distribution when beacon transmitter is located at PU receiver, while it can be approximated as a shifted-Gamma distributed random variable for the case of beacon transmitter located at PU transmitter. Based on statistical model for the interference distribution, closed-form expressions of the capacity-outage probability of the PU are developed to examine the effects of various system parameters on the performance of the PU in presence of interference from SUs. Simulation results confirm the validity of the developed analytical models. It is shown that beacon transmitter at PU receiver offers lower interference and hence better capacity-outage probability to the PU than beacon transmitter at PU transmitter.