This paper presents a novel opportunistic spectrum sharing scheme that applies ARQ protocol to achieve ultra reliability in the finite blocklength regime. A primary user shares its licensed spectrum to a secondary user, where both communicate to the same base station. The base station applies ARQ with the secondary user, which possess a limited number of trials to transmit each packet. We resort to the interweave model in which the secondary user senses the primary user activity and accesses the channel with access probabilities which depend on the primary user arrival rate and the number of available trials. We characterize the secondary user access probabilities and transmit power in order to achieve target error constraints for both users. Furthermore, we analyze the primary user performance in terms of outage probability and delay. The results show that our proposed scheme outperforms the open loop and non-opportunistic scenarios in terms of secondary user transmit power saving and primary user reliability.
In this work, we assess the viability of heterogeneous networks composed of legacy macrocells which are underlaid with self-organizing picocells. Aiming to improve coverage, cell-edge throughput and overall system capacity, self-organizing solutions, such as range expansion bias, almost blank subframe and distributed antenna systems are considered. Herein, stochastic geometry is used to model network deployments, while higher-order statistics through the cumulants concept is utilized to characterize the probability distribution of the received power and aggregate interference at the user of interest. A compre- hensive analytical framework is introduced to evaluate the performance of such self-organizing networks in terms of outage probability and average channel capacity with respect to the tagged receiver. To conduct our studies, we consider a shadowed fading channel model incorporating log-normal shadowing and Nakagami-m fading. Results show that the analytical framework matches well with numerical results obtained from Monte Carlo simulations. We also observed that by simply using almost blank subframes the aggregate interference at the tagged receiver is reduced by about 12dB. Although more elaborated interference control techniques such as, downlink bitmap and distributed antennas systems become needed, when the density of picocells in the underlaid tier gets high.
The fifth generation (5G) networks are expected to deliver high quality services to human and machine type users in specific locations. The recently proposed micro-operator concept aims at efficient and reliable local service delivery to complement traditional mobile network operators (MNOs) whose main objective has been to provide country-wide coverage. The uptake of 5G micro-operators requires local spectrum availability. In this paper, we propose a general framework for spectrum authorization and apply it in the recent micro-operator concept to develop local spectrum micro licensing model. To do this, we first identify the elements of a spectrum authorization model, and derive their interrelations to form a general framework for spectrum authorization. Then, we analyze the elements of the state-of-the-art spectrum authorization models including case examples of exclusive licensing, three-tier model for providing citizens broadband radio services (CBRS), and license-exempt model. Using the framework and the state-of-the-art models, we then propose and characterize the elements of the new micro licensing model to establish local 5G networks. Furthermore, our comprehensive investigation on the new micro licensing model supports to establish the micro-operator concept as a candidate solution for delivering localized 5G services. The proposed framework and its application to 5G micro-operators help to promote new market entry, encourage competition and innovation, and incentivize investment by facilitating the regulator to efficiently allocate 5G bands to the stakeholders.
This paper presents several fundamental concepts of game theory, and shows how they relate to the game formulation for a small cluster of opportunistic wireless nodes in cognitive radio networks. It is shown that in some resource allocation scenarios, it may be an optimal strategy for the wireless nodes to endure short-term losses for longer-term gains. In such scenarios, maximum total payoff may be achieved not by simultaneous mutual cooperation, but by taking turns defecting. The paper presents a novel idea of modeling cooperation and coordination for the opportunistic spectrum access as a turn-taking Prisoners dilemma. In our model, a small cluster of cognitive wireless nodes opportunistically access spectrum using spectrum shaped Oppermann sequences. Simulation results show that for the proposed resource allocation game turn-taking memory two strategies outperform traditional tit-for-tat and grim-trigger strategies.
In 5G and beyond networks, Unmanned Aerial Vehicles (UAV) are an attractive solution to enhance the secrecy of a wireless systems by exploiting their predominant LOS links and spacial manoeuvrability to introduce a friendly jamming. In this work, we investigate the impact of two cooperative UAV-based jammers on the secrecy performance of a ground wireless wiretap channel by considering secrecy-area related metrics, the jamming coverage and jamming efficiency. Moreover, we propose a hybrid metric, the so-called Weighted Secrecy Coverage (WSC) that can be used as a metric for gaining insights on the optimal deployments of the UAV jammers to provide the best exploration of jamming signals. For evaluating these metrics, we derive a closed-form position-based metric, the secrecy improvement, and propose an analogous computationally simpler metric. Our simulations show that a balanced power allocation between the two UAVs leads to the best performances, as well as a symmetrical positioning behind the line of sight between the legitimate transmitter and receiver. Moreover, there exist an optimal UAV height for the jammers. Finally, we propose a suboptimal and simpler problem for the maximisation of the WSC.
We consider the problem of recovering an image using block compressed sensing (BCS). Traditional BCS algorithms recovers each image block independently and utilizes post-processing methods for removing the blocking artifacts. In contrast, we propose an image recovery method free of post-processing, where we utilize a lapped transform (LT) for the sparse representation of the image in order to reduce the blocking artifacts. Specifically, we derive an iterative image reconstruction method, where a small number of adjacent measurement blocks are jointly processed for recovering an image block. For this purpose, we propose a novel sparse Bayesian learning (SBL) algorithm.
In this paper, a novel transmit antenna selection scheme for multiple input multiple output (MIMO) wiretap channels is proposed. This new scheme achieves higher secrecy performance by exploiting eavesdropper's channel state information. The key idea behind our proposal is to perform the antenna selection aiming to maximize the overall secrecy rate instead of maximizing the instantaneous signal-to-noise ratio of the main channel. In our analysis, we assume that the legitimate receiver and the eavesdropper employ a maximal-ratio combining technique to combine the received signals from the transmitter. Considering Nakagami-m fading channels, a closed-form expression for the secrecy outage probability is derived, which can be used as a quality of service metric. Further, an asymptotic analysis is carried out and the diversity/array gains are obtained. The ergodic secrecy rate is also investigated for the case of multiple input single output wiretap channels. Representative numerical examples are plotted and validated through Monte Carlo simulations. Insightful discussions are drawn from the proposed analysis.
Interference prediction and resource allocation are critical challenges in mission-critical applications where stringent latency and reliability constraints must be met. This paper proposes a novel Gaussian process regression (GPR)-based framework for predictive interference management and resource allocation in future 6G networks. Firstly, the received interference power is modeled as a Gaussian process, enabling both the prediction of future interference values and their corresponding estimation of uncertainty bounds. Differently from conventional machine learning methods that extract patterns from a given set of data without any prior belief, a Gaussian process assigns probability distributions to different functions that possibly represent the data set which can be further updates using Bayes' rule as more data points are observed. For instance, unlike deep neural networks, the GPR model requires only a few sample points to update its prior beliefs in real-time. Furthermore, we propose a proactive resource allocation scheme that dynamically adjusts resources according to predicted interference. The performance of the proposed approach is evaluated against two benchmarks prediction schemes, a moving average-based estimator and the ideal genie-aided estimator. The GPR-based method outperforms the moving average-based estimator and achieves near-optimal performance, closely matching the genie-aided benchmark.
End-to-end learning of a communications system using the deep learning-based autoencoder concept has drawn interest in recent research due to its simplicity, flexibility and its potential of adapting to complex channel models and practical system imperfections. In this paper, we have compared the bit error rate (BER) performance of autoencoder based systems and conventional channel coded systems with convolutional coding (CC), in order to understand the potential of deep learning-based systems as alternatives to conventional systems. From the simulations, autoencoder implementation was observed to have a better BER in 0-5 dB $E_{b}/N_{0}$ range than its equivalent half-rate convolutional coded BPSK with hard decision decoding, and to have only less than 1 dB gap at a BER of $10^{-5}$. Furthermore, we have also proposed a novel low complexity autoencoder architecture to implement end-to-end learning of coded systems in which we have shown better BER performance than the baseline implementation. The newly proposed low complexity autoencoder was capable of achieving a better BER performance than half-rate 16-QAM with hard decision decoding over the full 0-10 dB $E_{b}/N_{0}$ range and a better BER performance than the soft decision decoding in 0-4 dB $E_{b}/N_{0}$ range.
Coming cellular systems are envisioned to open up to new services with stringent reliability and energy efficiency requirements. In this paper we focus on the joint power control and rate allocation problem in Single-Input Multiple-Output (SIMO) wireless systems with Rayleigh fading and stringent reliability constraints. We propose an allocation scheme that maximizes the energy efficiency of the system while making use only of average statistics of the signal and interference, and the number of antennas $M$ that are available at the receiver side. We show the superiority of the Maximum Ratio Combining (MRC) scheme over Selection Combining (SC) in terms of energy efficiency, and prove that the gap between the optimum allocated resources converges to $(M!)^(1/(2M))$ as the reliability constraint becomes more stringent. Meanwhile, in most cases MRC was also shown to be more energy efficient than Switch and Stay Combining (SSC) scheme, although this does not hold only when operating with extremely large $M$, extremely high/small average signal/interference power and/or highly power consuming receiving circuitry. Numerical results show the feasibility of the ultra-reliable operation when $M$ increases, while greater the fixed power consumption and/or drain efficiency of the transmit amplifier is, the greater the optimum transmit power and rate.
