The escalating number of wireless users requiring different services, such as enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable low-latency communications (URLLC), has led to exploring non-orthogonal multiplexing methods like heterogeneous non-orthogonal multiple access (H-NOMA). This method allows users demanding divergent services to share the same resources. However, implementing the H-NOMA scheme faces major resource management challenges due to unpredictable interference caused by the random access mechanism of mMTC users. To address this issue, this paper proposes a joint optimization and cooperative multi-agent (MA) deep reinforcement learning-based resource allocation mechanism, aimed at maximizing the energy efficiency (EE) of H-NOMA-based networks. Specifically, this work initially establishes an optimization framework capable of determining the optimal power allocation for any specific sub-channel assignment (SA) setting for all users. Based on that, a cooperative MA double deep Q network (CMADDQN) scheme is carefully designed at the base station to conduct SA among users. In addition, a distributed full learning-based approach using MADDQN for both SA and power allocation is also designed for comparison purposes. Simulation results show that the proposed joint optimization and machine learning method outperforms the solely-learning-based approach and other benchmark schemes in terms of convergence rate and EE performance.

This work considers the orthogonal frequency division multiple access (OFDMA) technology that enables multiple unmanned aerial vehicles (multi-UAV) communication systems to provide on-demand services. The main aim of this work is to derive the optimal allocation of radio resources, 3D placement of UAVs, and user association matrices. To achieve the desired objectives, we decoupled the original joint optimization problem into two sub-problems: i) 3D placement and user association and ii) sum-rate maximization for optimal radio resource allocation, which are solved iteratively. The proposed iterative algorithm is shown via numerical results to achieve fast convergence speed after less than 10 iterations. The benefits of the proposed design are demonstrated via superior sum-rate performance compared to existing reference designs. Moreover, the results declared that the optimal power and sub-carrier allocation helped mitigate the co-cell interference that directly impacts the system’s performance.

Ambient backscatter communication (AmBC) technology can potentially offer spectral- and energy-efficient solutions for future wireless systems. This paper proposes a novel design to facilitate the spectrum sharing between a secondary system and a primary system based on the AmBC technique in intelligent reflective surface (IRS)-assisted unmanned aerial vehicle (UAV) networks. In particular, an IRS-aided UAV cooperatively relays the transmission from a terrestrial primary source node to a user equipment on the ground. On the other hand, leveraging on the AmBC technology, a terrestrial secondary node transmits its information to a terrestrial secondary receiver by modulating and backscattering the ambient relayed radio frequency (RF) signals from the UAV-IRS. The performance of such a system setup is analyzed by deriving the expressions of outage probability and ergodic spectral efficiency. Finally, we present the numerical results to provide useful insights into the system design and also validate the derived theoretical results using Monte Carlo simulations.

In this paper, we investigate the multiplexing of grant-based (GB) and grant-free (GF) device transmissions in an uplink heterogeneous network (HetNet), namely GB-GF HetNet, where the devices transmit their information using low-rate short data packets. Specifically, GB devices are granted unique time-slots for their transmissions. In contrast, GF devices can randomly select time-slots to transmit their messages utilizing the GF non-orthogonal multiple access (NOMA), which has emerged as a promising enabler for massive access and reducing access latency. However, random access (RA) in the GF NOMA can cause collisions and severe interference, leading to system performance degradation. To overcome this issue, we propose a multiple access (MA) protocol based on reinforcement learning for effective RA slots allocation. The proposed learning method aims to guarantee that the GF devices do not cause any collisions to the GB devices and the number of GF devices choosing the same time-slot does not exceed a predetermined threshold to reduce the interference. In addition, based on the results of the RA slots allocation using the proposed method, we derive the approximate closed-form expressions of the average decoding error probability (ADEP) for all devices to characterize the system performance. Our results presented in terms of access efficiency (AE), collision probability (CP), and overall ADEP (OADEP), show that our proposed method can ensure a smooth operation of the GB and GF devices within the same network while significantly minimizing the collision and interference among the device transmissions in the GB-GF HetNet.

The evolution of 5G wireless networks poses significant research challenges such as securing the user data, maintaining certain latency and reliability requirements etc. However, it can be challenging to simultaneously meet these performance requisites, which may lead to resort to a trade-off among different metrics. This paper investigates the secrecy-reliability performance trade-off (SRPT) for non-orthogonal multiple access (NOMA)-based millimeter wave (mmWave) networks. Herein, we consider two end-users, namely primary and secondary, which are served by an mmWave base station using downlink NOMA. Besides, a passive eavesdropper lying in the vicinity of these end-users attempts to intercept their legitimate message signals. For this set-up, we derive the closed-form expressions of the outage probability (OP) of a targeted end-user and intercept probability (IP) of the eavesdropper to analyze the SRPT of the system. We further propose a low-complexity average channel state information (CSI)-based power allocation strategy to improve the reliability of a targeted user while maintaining its information secrecy. Moreover, we obtain the condition under which NOMA guarantees superior secrecy performance than that of orthogonal multiple access (OMA) scheme. We corroborate our theoretical analysis via simulation results presented in terms of IP and OP.

In massive machine-type communications (mMTC) networks, the ever-growing number of MTC devices and the limited radio resources have caused a severe problem of random access channel (RACH) congestion. To mitigate this issue, several potential multiple access (MA) mechanisms including sparse code MA (SCMA) have been proposed. Besides, the short-packet transmission feature of MTC devices requires the design of new transmission and congestion avoidance techniques as the existing techniques based on the assumption of infinite data-packet length may not be suitable for mMTC networks. Therefore, it is important to find novel solutions to address RACH congestion in mMTC networks while considering SCMA and short-packet communications (SPC). In this paper, we propose an SCMA-based random access (RA) method, in which Q-learning is utilized to dynamically allocate the SCMA codebooks and time-slot groups to MTC devices with the aim of minimizing the RACH congestion in SPC-based mMTC networks. To clarify the benefits of our proposed method, we compare its performance with those of the conventional RA methods with/without Q-learning in terms of RA efficiency and evaluate its convergence. Our simulation results show that the proposed method outperforms the existing methods in overloaded systems, i.e., the number of devices is higher than the number of available RA slots. Moreover, we illustrate the sum rate comparison between SPC and long-packet communications (LPC) when applying the proposed method to achieve more insights on SPC.

The quest for finding a small-sized energy supply to run the small-scale wireless gadgets, with almost an infinite lifetime, has intrigued humankind since past several decades. In this context, the concept of Quantum batteries has come into limelight more recently to serve the purpose. However, the main issue revolving around the closed-system design of Quantum batteries is to ensure a loss-less environment, which is extremely difficult to realize in practice. In this paper, we present the modeling and optimization aspects of a Radio-Frequency (RF) Energy Harvesting (EH) assisted Quantum battery, wherein several EH modules (in the form of micro- or nano- sized integrated circuits (ICs)) help each of the involved Quantum sources achieve the so-called quasi-stable state. Specifically, a micro-controller manages the overall harvested energy from the RF-EH ICs and a photon emitting device, such that the emitted photons are absorbed by the electrons in the Quantum sources. In order to precisely model and optimize the considered framework, we formulate a transmit power minimization problem for an RF-based wireless system to optimize the number of RF-EH ICs under the given EH constraints at the Quantum battery-enabled wireless device. We obtain an analytical solution to the above-mentioned problem using a rational approach, while additionally seeking another solution obtained via a non-linear program solver. The effectiveness of the proposed technique is reported in the form of numerical results by taking a range of system parameters into account.

This paper addresses the radio access network (RAN) resource slicing problem in the context of the joint allocation of transmit powers and time-frequency resource blocks (RBs) in the 5G system consisting of ultra-reliable and low-latency communication (URLLC) and enhanced mobile broadband (eMBB) users. Specifically, we formulate a modulation and coding scheme (MCS) based optimization problem to maximize the sum goodput of eMBB users while satisfying URLLC and eMBB users' QoS requirements. The proposed scheme considers the impact of imperfect channel state information (CSI) and the active user's queue status for the dynamic assignment of radio resources to the heterogeneous users according to its demand. The resulting mixed-integer non-convex problem is first transformed into a tractable form by exploiting the probabilistic to non-probabilistic conversion, Big-M theory, and difference-of-convex (DC) programming. Later, the transformed problem is solved using the successive convex approximation (SCA) based iterative algorithm. Our simulation results illustrate the superiority of the proposed algorithm compared to the baseline methods in terms of eMBB rate, latency in delivering the URLLC packets, and total power consumption.

The ever-increasing number of machine-type communications (MTC) devices and the limited available radio resources are leading to a crucial issue of radio access network (RAN) congestion in upcoming 5G and beyond wireless networks. Thus, it is crucial to investigate novel techniques to minimize RAN congestion in massive MTC (mMTC) networks while taking the underlying short-packet communications (SPC) into account. In this paper, we propose an adaptive Q-learning (AQL) algorithm based on block error rate (BLER), an important metric in SPC, for a non-orthogonal multiple access (NOMA) based mMTC system. The proposed method aims to efficiently accommodate MTC devices to the available random access (RA) slots in order to significantly reduce the possible collisions, and subsequently to enhance the system throughput. Furthermore, in order to obtain more practical insights on the system design, the scenario of imperfect successive interference cancellation (ISIC) is considered as compared to the widely-used perfect SIC assumption. The performance of the proposed AQL method is compared with the recent Q-learning solutions in the literature in terms of system throughput over a range of parameters such as the number of devices, blocklength, and residual interference caused by ISIC, along with its convergence evaluation. Our simulation results illustrate the superiority of the proposed method over the existing techniques, in the scenarios where the number of devices is higher than the number of available RA time-slots.

Federated learning (FL) allows multiple edge computing nodes to jointly build a shared learning model without having to transfer their raw data to a centralized server, thus reducing communication overhead. However, FL still faces a number of challenges such as nonindependent and identically distributed data and heterogeneity of user equipments (UEs). Enabling a large number of UEs to join the training process in every round raises a potential issue of the heavy global communication burden. To address these issues, we generalize the current state-of-the-art federated averaging (FedAvg) by adding a weight-based proximal term to the local loss function. The proposed FL algorithm runs stochastic gradient descent in parallel on a sampled subset of the total UEs with replacement during each global round. We provide a convergence upper bound characterizing the tradeoff between convergence rate and global rounds, showing that a small number of active UEs per round still guarantees convergence. Next, we employ the proposed FL algorithm in wireless Internet-of-Things (IoT) networks to minimize either total energy consumption or completion time of FL, where a simple yet efficient path-following algorithm is developed for its solutions. Finally, numerical results on unbalanced data sets are provided to demonstrate the performance improvement and robustness on the convergence rate of the proposed FL algorithm over FedAvg. They also reveal that the proposed algorithm requires much less training time and energy consumption than the FL algorithm with full user participation. These observations advocate the proposed FL algorithm for a paradigm shift in bandwidth-constrained learning wireless IoT networks.

This paper investigates the combined benefits of full-duplex (FD) and cell-free massive multiple-input multiple-output (CF-mMIMO), where a large number of distributed access points (APs) having FD capability simultaneously serve numerous uplink and downlink user equipments (UEs) on the same time-frequency resources. To enable the incorporation of FD technology in CF-mMIMO systems, we propose a novel heap-based pilot assignment algorithm, which not only can mitigate the effects of pilot contamination but also reduce the involved computational complexity. Then, we formulate a robust design problem for spectral efficiency (SE) maximization in which the power control and AP-UE association are jointly optimized, resulting in a difficult mixed-integer nonconvex programming. To solve this problem, we derive a more tractable problem before developing a very simple iterative algorithm based on inner approximation method with polynomial computational complexity. Numerical results show that our proposed methods with realistic parameters significantly outperform the existing approaches in terms of the quality of channel estimate and SE.

In-band full-duplex (FD) operation is practically more suited for short-range communications such as WiFi and small-cell networks, due to its current practical limitations on the self-interference cancellation. In addition, cell-free massive multiple-input multiple-output (CF-mMIMO) is a new and scalable version of MIMO networks, which is designed to bring service antennas closer to end user equipments (UEs). To achieve higher spectral and energy efficiencies (SE-EE) of a wireless network, it is of practical interest to incorporate FD capability into CF-mMIMO systems to utilize their combined benefits. We formulate a novel and comprehensive optimization problem for the maximization of SE and EE in which power control, access point-UE (AP-UE) association and AP selection are jointly optimized under a realistic power consumption model, resulting in a difficult class of mixed-integer nonconvex programming. To tackle the binary nature of the formulated problem, we propose an efficient approach by exploiting a strong coupling between binary and continuous variables, leading to a more tractable problem. In this regard, two low-complexity transmission designs based on zero-forcing (ZF) are proposed. Combining tools from inner approximation framework and Dinkelbach method, we develop simple iterative algorithms with polynomial computational complexity in each iteration and strong theoretical performance guaranteed. Furthermore, towards a robust design for FD CF-mMIMO, a novel heap-based pilot assignment algorithm is proposed to mitigate effects of pilot contamination. Numerical results show that our proposed designs with realistic parameters significantly outperform the well-known approaches (i.e., small-cell and collocated mMIMO) in terms of the SE and EE. Notably, the proposed ZF designs require much less execution time than the simple maximum ratio transmission/combining.

Despite the importance of Rayleigh-product multiple-input multiple-output channels and their experimental validations, there is no work investigating their performance in the presence of residual additive transceiver hardware impairments, which arise in practical scenarios. Hence, this paper focuses on the impact of these residual imperfections on the ergodic channel capacity for optimal receivers, and on the ergodic sum rates for linear minimum mean-squared-error (MMSE) receivers. Moreover, the low- and high-signal-to-noise ratio cornerstones are characterized for both types of receivers. Simple closed-form expressions are obtained that allow the extraction of interesting conclusions. For example, the minimum transmit energy per information bit for optimal and MMSE receivers is not subject to any additive impairments. In addition to the exact analysis, we also study the Rayleigh-product channels in the large system regime, and we elaborate on the behavior of the ergodic channel capacity with optimal receivers by varying the severity of the transceiver additive impairments.

Future Internet of Things (IoT) networks are expected to support a massive number of heterogeneous devices/sensors in diverse applications ranging from eHealthcare to industrial control systems. In highly-dense deployment scenarios such as industrial IoT systems, providing reliable communication links with low-latency becomes challenging due to the involved system delay including data acquisition and processing latencies at the edge-side of IoT networks. In this regard, this paper proposes a priority-based channel access and data aggregation scheme at the Cluster Head (CH) to reduce channel access and queuing delays in a clustered industrial IoT network. First, a prioritized channel access mechanism is developed by assigning different Medium Access Control (MAC) layer attributes to the packets coming from two types of IoT nodes, namely, high-priority and low-priority nodes, based on the application-specific information provided from the cloud-center. Subsequently, a preemptive M/G/1 queuing model is employed by using separate low-priority and high- priority queues before sending aggregated data to the Cloud. Our results show that the proposed priority-based method significantly improves the system latency and reliability as compared to the non-prioritized scheme.

Cognitive Radio (CR) communication has been considered as one of the promising technologies to enable dynamic spectrum sharing in the next generation of wireless networks. Among several possible enabling techniques, Spectrum Sensing (SS) is one of the key aspects for enabling opportunistic spectrum access in CR Networks (CRN). From practical perspectives, it is important to design low-complexity wideband CR receiver having low resolution Analog to Digital Converter (ADC) working at a reasonable sampling rate. In this context, this paper proposes a novel spatio-temporal wideband SS technique by employing multiple antennas and one-bit quantization at the CR node, which subsequently enables the use of a reasonable sampling rate. In our analysis, we show that for the same sensing performance requirements, the proposed wideband receiver can have lower power consumption than the conventional CR receiver equipped with a single-antenna and a high-resolution ADC. Furthermore, the proposed technique exploits the spatial dimension by estimating the direction of arrival of Primary User (PU) signals, which is not possible by the conventional SS methods and can be of a significant benefit in a CRN. Moreover, we evaluate the performance of the proposed technique and analyze the effects of one-bit quantization with the help of numerical results.

In this paper, we study a problem of relay selection in a two-hop relaying network where the destination is equipped with Simultaneous Wireless Information and Power Transmission (SWIPT) capabilities. In contrast to conventional cooperative networks, the destination node is considered to be capable of simultaneously decoding information and harvesting energy from both the source and the relay transmissions. In this context, we formulate two optimization problems for both time switching (TS) and power splitting (PS) based SWIPT schemes. The first problem is the maximization of the overall user data rate while ensuring a minimum harvested power. The second problem focuses on the maximization of the overall harvested power at the user under the constraint on the minimum achievable rate. Assuming an amplify-and-forward (AF) relay protocol, closed-form solutions are obtained for the selection of an optimal relay, relay amplification coefficient and the optimal time or power splitting factor. The performance of the proposed relay selection strategies with the aforementioned objectives is evaluated and compared with the case of random relay selection. Furthermore, the Rate-Energy (R-E) tradeoff performance of the scenario with both the direct and indirect relay-assisted links is compared to the case where only a relay-assisted link is available. Our simulation results demonstrate the significant benefits of combining direct and indirect links in SWIPT-enabled cooperative networks in terms of the R-E tradeoff.

Dynamic Spectrum Access (DSA) is considered as a promising solution to address the problem of spectrum scarcity in future wireless networks. However, the main challenges associated with this approach are to acquire accurate spectrum usage information in a timely manner and to deal with the dynamicity of channel occupancy. Although Cooperative Sensing (CS) can provide significant advantages over individual device-level sensing in terms of sensing efficiency and the achievable throughput, the acquired channel occupancy information may become outdated in dynamic channel conditions due to the involved latency. In this regard, we propose to utilize a collaborative cloud-edge processing framework to minimize the CS delay in DSA networks. In this framework, the cloud-center can estimate channel occupancy parameters such as duty cycle based on the available historical sensing data by using a suitable spectrum prediction technique, and subsequently this prior knowledge can be utilized to adapt the sensing mechanism employed at the edge-side of a DSA network. Motivated by this, we formulate and solve the problem of minimizing CS delay in cloud-assisted DSA networks. A two-stage bisection search method is employed to solve this CS delay minimization problem. Our results show that the proposed cloud-assisted CS scheme can significantly reduce the CS delay in DSA networks.

A superimposed (arithmetically added) Pilot (SiP) sequence based channel estimation method for beamforming assisted multi-antenna High Altitude Platform (HAP) land mobile radio communication systems is proposed, which exploits the prior available information of users' spatial location, density of users, and beam-width of HAP directional antenna. A thorough characterization of HAP sparse multipath radio propagation channels' is presented in first part of the paper, where mathematical relationship of HAP antenna beam-width with channel's delay span and optimal length of SiP base sequence are presented. Further, a location information aided and low- power SiP sequence based Stage-wise Orthogonal Match Pursuit (StOMP) algorithm is proposed for estimation of channels from single-antenna user terminals to beamforming assisted large scale multiple-antenna HAP. A thorough analysis on the basis of Normalized Channel Mean Square Error (NCMSE) and Bit Error Rate (BER) performance of proposed method is presented; where the effect of channels' sparsity level, Pilot-to-Information power Ratio (PIR), beam-width of HAP's directional antenna, amount of HAP antenna elements, density of interfering users, and spatial location of active user terminal are thoroughly studied. A comparison of the proposed method with a notable reference technique available in the literature is also presented.

One of the fundamental issues in Wireless Sensor Networks (WSN) is to count and localize multiple targets accurately. In this context, there has been an increasing interest in the literature in using Compressive Sensing (CS) based techniques by exploiting the sparse nature of spatially distributed targets within the monitored area. However, most existing works aim to count and localize the sparse targets utilizing a Single Measurement Vector (SMV) model. In this paper, we consider the problem of counting and localizing multiple targets exploiting the joint sparsity feature of a Multiple Measurement Vector (MMV) model. Furthermore, the conventional MMV formulation in which the same measurement matrix is used for all sensors is not valid any more in practical time-varying wireless environments. To overcome this issue, we reformulate the MMV problem into a conventional SMV in which MMVs are vectorized. Subsequently, we propose a novel reconstruction algorithm which does not need the prior knowledge of the sparsity level unlike the most existing CS-based approaches. Finally, we evaluate the performance of the proposed algorithm and demonstrate the superiority of the proposed MMV approach over its SMV counterpart in terms of target counting and localization accuracies.

In this paper, a novel simplified statistical approach to evaluate the error performance bound of Ordered Statistics Decoding (OSD) of Linear Block Codes (LBC) is investigated. First, we propose a novel statistic which depicts the number of errors contained in the ordered received noisy codeword. Then, simplified expressions for the probability mass function and cumulative distribution function are derived exploiting the implicit statistical independence property of the samples of the received noisy codeword before reordering. Second, we incorporate the properties of this new statistic to derive the simplified error performance bound of the OSD algorithm for all order-I reprocessing. Finally, with the proposed approach, we obtain computationally simpler error performance bounds of the OSD than those proposed in literature for all length LBCs.

In this paper, we study the performance of hybrid cognitive radio systems that combine the benefits of interweave and underlay systems by employing a spectrum sensing and a power control mechanism at the Secondary Transmitter (ST). Existing baseline models considered for performance analysis assume perfect knowledge of the involved channels at the ST, however, such situations hardly exist in practical deployments. Motivated by this fact, we propose a novel approach that incorporates channel estimation at the ST, and consequently characterizes the performance of Hybrid Systems (HSs) under realistic scenarios. To capture the impact of imperfect channel knowledge, we propose outage constraints on the detection probability at the ST and on the interference power received at the primary receiver. Our analysis reveals that the baseline model overestimates the performance of the HS in terms of achievable secondary user throughput. Finally, based on the proposed estimation-sensing-throughput tradeoff, we determine suitable estimation and sensing durations that effectively capture the effect of imperfect channel knowledge and subsequently enhance the achievable secondary user throughput.

Knowledge of interacting channels is essential for characterizing the performance of a cognitive radio system in terms of interference power received by a primary receiver and throughput at a secondary receiver. Baseline models considered for the performance characterization assume perfect knowledge of the interacting channels. Recently, an analytical framework has been proposed that incorporates channel estimation and subsequently characterizes the performance of cognitive Interweave Systems (ISs). However, the analysis was pertained to the deterministic behaviour of the interacting channels. In this paper, we extend the characterization of the aforementioned framework to investigate the influence of channel fading on the performance of the IS. Our analysis indicate that an inappropriate choice of estimation time can severely degrade the performance of the IS in terms of achievable secondary throughput.

Cognitive Radio communication is foreseen as one of the possible candidates that can resolve spectrum scarcity currently faced by the upcoming wireless technologies. This scarcity can be solved by enabling secondary access to the licensed spectrum. The interference at the primary receiver can be avoided by employing a detector (spectrum sensing) at the Secondary Transmitter (ST). Energy detection is widely used due to its low complexity and applicability to a large range of primary user signals. Recently, antenna diversity techniques such as square-law selector and square-law combiner have been used to enhance the detection performance at the ST. In this context, the detector's performance pertaining to the antenna diversity techniques has been characterized analytically. However, issues such as RF impairments and deploying a fading model render hardware implementation of such techniques challenging. Motivated by this fact, this paper presents the deployment of a hardware, and subsequently utilize the theoretical expressions to validate the performance of a multi-antenna system at the ST that exploits antenna diversity techniques in a realistic environment. Finally, we emphasize the challenges faced during the hardware implementation and present our approach to address these challenges.

Among several potential applications of Full- Duplex (FD) technology, FD Cognitive Radio (CR) communication is one important area where FD can provide several advantages and possibilities such as concurrent sensing and transmission, improved sensing efficiency and the secondary throughput. However, the main challenge is to mitigate the harmful effects of the residual Self-Interference (SI) which depends on the SI mitigation capability of the employed technique. One way to mitigate this effect is to control the transmit power of the CR node, however, this power control over the entire frame duration results in a power- throughput tradeoff. In this context, we propose a novel Two-Phase Concurrent Sensing and Transmission (2P-CST) framework in which a CR performs concurrent sensing and transmission for a certain fraction of the frame duration by employing a power control mechanism and for the remaining fraction of the frame duration, the CR only transmits with the full power. The proposed framework allows the flexibility to optimize the sensing time and the transmit power in order to maximize the achievable throughput of the FD-CR system. Our results demonstrate that the proposed 2P-CST FD transmission strategy provides better performance in terms of the achievable throughput than the conventional Periodic Sensing and Transmission (PST) and CST techniques.

Secondary access to the licensed spectrum is viable only if the interference is avoided at the primary system. In this regard, different paradigms have been conceptualized in the existing literature. Among these, interweave systems (ISs) that employ spectrum sensing have been widely investigated. Baseline models investigated in the literature characterize the performance of the IS in terms of a sensing-throughput tradeoff, however, this characterization assumes perfect knowledge of the involved channels at the secondary transmitter, which is unavailable in practice. Motivated by this fact, we establish a novel approach that incorporates channel estimation in the system model, and consequently investigate the impact of imperfect channel knowledge on the performance of the IS. More particularly, the variation induced in the detection probability affects the detector’s performance at the secondary transmitter, which may result in severe interference at the primary receivers. In this view, we propose employing average and outage constraints on the detection probability, in order to capture the performance of the IS. Our analysis reveals that with an appropriate choice of the estimation time determined by the proposed approach, the performance degradation of the IS can be effectively controlled, and subsequently the achievable secondary throughput can be significantly enhanced.

Understanding the performance of cognitive radio systems is of great interest. To perform dynamic spectrum access, different paradigms are conceptualized in the literature. Of these, Underlay System (US) has caught much attention in the recent past. According to US, a power control mechanism is employed at the Secondary Transmitter (ST) to constrain the interference at the Primary Receiver (PR) below a certain threshold. However, it requires the knowledge of channel towards PR at the ST. This knowledge can be obtained by estimating the received power, assuming a beacon or a pilot channel transmission by the PR. This estimation is never perfect, hence the induced error may distort the true performance of the US. Motivated by this fact, we propose a novel model that captures the effect of channel estimation errors on the performance of the system. More specifically, we characterize the performance of the US in terms of the estimation-throughput tradeoff. Furthermore, we determine the maximum achievable throughput for the secondary link. Based on numerical analysis, it is shown that the conventional model overestimates the performance of the US.

Herein, we study the spectral coexistence of Geostationary (GEO) Fixed Satellite Services (FSS) downlink and Broadcasting Satellite Services (BSS) feeder links in the Ka-band (17.3 – 18.1 GHz) which is primarily allocated for BSS feeder links. Firstly, a novel cognitive spectrum exploitation framework is proposed in order to utilize the available band efficiently. Subsequently, based on the interference analysis carried out between these systems, two cognitive approaches, namely Carrier Allocation (CA) and Beamforming (BF), are investigated under the considered framework assuming the availability of an accurate Radio Environment Map (REM). The employed techniques allow the flexibility of using additional shared carriers for the FSS downlink system along with the already available exclusive carriers (19.7 – 20.2 GHz), thus increasing the overall system throughput. It is shown that a significant improvement in the per beam throughput as well as in the beam availability can be achieved by applying CA and BF approaches in the considered scenario.

Exploring innovative cellular architectures to achieve enhanced system capacity and good coverage has become a critical issue towards realizing the fifth generation (5G) of wireless communications. In this context, this paper proposes a novel concept of an intelligent Amplify and Forward (AF) 5G repeater for enabling the densification of future cellular networks. The proposed repeater features a Spectrum Sensing (SS) intelligence capability and utilizes such intelligence in a complementary fashion in comparison to its existing counterpart (e.g., Cognitive Radio) by detecting the active channels within the assigned spectrum. This intelligence allows the proposed repeater to carry out selective amplification of the active channels in contrast to the full amplification in conventional AF repeaters. Furthermore, the performance of a Frequency Division Multiple Access (FDMA) based two hop cellular network utilizing the proposed repeater is evaluated in terms of the system throughput. Simulation results demonstrate up to 13 % increase when compared with the conventional repeaters. Moreover, the effect of SS errors on the system capacity is analyzed.

Spectrum Sensing (SS) is one of the fundamental mechanisms required by a Cognitive Radio (CR). Among several SS techniques, cyclostationary feature detection is considered as an important technique due to its robustness against noise variance uncertainty and its capability to distinguish among different systems on the basis of their cyclostationary features. However, one of the main limitations of this detector in practical scenarios is its performance degradation in the presence of cyclic frequency mismatch, which mainly arises due to the lack of knowledge about the transmitter clock/oscillator errors at the detector. In this context, this paper proposes a novel solution to address the cyclic frequency mismatch problem utilizing the Slepian basis expansion instead of the widely used Fourier basis expansion. It is shown that the proposed approach captures the deviation in the cyclic frequency caused by the aforementioned imperfections and hence provides a significant improvement in the sensing performance in the presence of cyclic frequency mismatch.

A fundamental problem facing the next generation of Satellite Communications (SatComs) is the spectrum congestion and how the scarce spectral resources are allocated to meet the demand for higher rate and reliable broadband communications. In this context, this paper addresses the cognitive satellite uplink where satellite terminals reuse frequency bands of Fixed-Service (FS) terrestrial microwave links which are the incumbent users in the Ka band. In this scenario, the transmit power of the satellite terminals has to be controlled such that the total aggregated interference at the FS system is kept below some acceptable threshold. In this paper, we review simple and efficient power allocation techniques available in the literature and, with slight adaptations, we evaluate them to the proposed satellite uplink and terrestrial FS co-existence scenario. The presented numerical results highlight the tradeoff between the level of channel state information and the rates that can be achieved at the satellite network.

Satellite communication (SatCom) is facing a spectrum scarcity problem due to the limited available exclusive spectrum and the high demand of the broadband satellite services. In this context, there has been an increasing interest in the satellite community to exploit the non- exclusive Ka-band spectrum in order to enhance the spectral efficiency of future broadband satellite systems. Herein, we propose a novel concept of enabling the spectral coexistence of satellite and terrestrial networks using three dimensional (3D) beamforming, which exploits the elevation dimension in addition to the commonly used azimuth dimension. The proposed beamforming solution is employed in a Multiple-Input Low Noise Block Downconverter (MLNB) based Feed Array Reflector (FAR) in contrast to the widely used Uniform Linear Array (ULA) structure. Within the employed antenna structure, the performance of the proposed beamforming solution is evaluated considering different feed arrangements. Finally, a database-assisted approach and two blind approaches are suggested for the effective implementation of the proposed solutions.

Herein, we consider the problem of detecting primary users' signals in the presence of noise correlation, which may arise due to imperfections in fltering and oversampling operations in a Cognitive Radio (CR) receiver. In this context, we study a Maximum Eigenvalue (ME) detection technique using recent results from Random Matrix Theory (RMT) for characterizing the distribution of the maximum eigenvalue of a class of sample covariance matrices. Subsequently, we derive a theoretical expression for a sensing threshold as a function of the probability of false alarm and evaluate the sensing performance in terms of probability of correct decision. It is shown that the proposed approach signifcantly improves the sensing performance of the ME detector in correlated noise scenarios.

Compressive Sensing (CS) has been widely investigated in the Cognitive Radio (CR) literature in order to reduce the hardware cost of sensing wideband signals assuming prior knowledge of the sparsity pattern. However, the sparsity order of the channel occupancy is time-varying and the sampling rate of the CS receiver needs to be adjusted based on its value in order to fully exploit the potential of CS-based techniques. In this context, investigating blind Sparsity Order Estimation (SOE) techniques is an open research issue. To address this, we study an eigenvalue-based compressive SOE technique using asymptotic Random Matrix Theory. We carry out detailed theoretical analysis for the signal plus noise case to derive the asymptotic eigenvalue probability distribution function (aepdf) of the measured signal's covariance matrix for sparse signals. Subsequently, based on the derived aepdf expression, we present a technique to estimate the sparsity order of the wideband spectrum with compressive measurements using the maximum eigenvalue of the measured signal's covariance matrix. The performance of the proposed technique is evaluated in terms of normalized SOE Error (SOEE). It is shown that the sparsity order of the wideband spectrum can be reliably estimated using the proposed technique.

Estimating the Signal to Noise Ratio (SNR) of the Primary Users' (PUs) signals over a wideband spectrum accurately is crucial in order to fully exploit an under-utilized primary spectrum using underlay Cognitive Radio (CR) techniques. In this context, we study an SNR estimation problem for a wideband CR under practical correlated scenarios in compressive settings. We carry out detailed theoretical analysis for the considered scenarios and then derive the expressions for the asymptotic eigenvalue probability distribution function (aepdf) of the measured signal's covariance matrix using asymptotic Random Matrix Theory. Subsequently, based on the derived aepdfs, we present a technique to estimate the PU SNR over a wideband spectrum with compressive measurements. The performance of the proposed technique is evaluated in terms of normalized Mean Square Error (MSE) and it is shown that the SNR of the PU signals over the wideband spectrum can be reliably estimated using the proposed technique.

In addition to spectrum sensing capability required by a Cognitive Radio (CR), Signal to Noise Ratio (SNR) estimation of the primary signals is crucial in order to adapt its coverage area dynamically using underlay techniques. Furthermore, in practical scenarios, the fading channel may be correlated due to various causes such as insufficient scattering in the propagation path and antenna mutual coupling. In this context, we consider the SNR estimation problem for a CR in the presence of channel correlation. We study an eigenvalue-based SNR estimation technique for large-scale CR networks using asymptotic Random Matrix Theory (RMT). We carry out detailed theoretical analysis of the signal plus noise hypothesis to derive the asymptotic eigenvalue probability distribution function (a.e.p.d.f.) of the received signal's covariance matrix in the presence of the correlated channel. Then an SNR estimation technique based on the derived a.e.p.d.f. is proposed for PU SNR in the presence of channel correlation and its performance is evaluated in terms of normalized Mean Square Error (MSE). It is shown that the PU SNR can be accurately estimated in the presence of channel correlation using the proposed technique even in low SNR region.

Herein, we consider asymptotic performance analysis of eigenvalue-based blind Spectrum Sensing (SS) techniques for large-scale Cognitive Radio (CR) networks using Random Matrix Theory (RMT). Different methods such as Scaled Largest Value (SLE), Standard Condition Number (SCN), John's detection and Spherical Test (ST) based detection are considered. The asymptotic sensing bounds for John's detection and ST based detection techniques are derived under a noise only hypothesis for sensing the presence of Primary Users (PUs). These asymptotic bounds are then used as thresholds for the SS decision and their performance is compared with other techniques in terms of probability of correct detection under both hypotheses. It is noted that the SLE detector is the best for a range of scenarios, followed by JD, SCN, ST. Furthermore, it is shown that noise correlation significantly degrades the performance of ST and JD detectors in practical scenarios.

This paper considers a Fractional Sampling (FS) technique to enhance the Spectrum Sensing (SS) efficiency of a Cognitive Radio (CR) using a decision statistic based on asymptotic Random Matrix Theory (RMT). Firstly, the effect of noise correlation on eigenvalue based SS is studied analytically and by numerical evaluation. Secondly, new bounds for the Standard Condition Number (SCN) are proposed to enhance the SS efficiency in correlated noise scenarios. It is shown that proposed FS method can enhance SS efficiency up to certain FS rates at the expense of receiver complexity and no performance advantage is obtained if the FS rate is increased beyond this limit. As a result, a method for determining the operating point for the FS rate in terms of sensing performance and complexity is suggested.

Interference Alignment (IA) has been considered a promising technique for spectral coexistence of different wireless systems in an underlay cognitive mode. Furthermore, Frequency Packing (FP) can be considered as an important technique for enhancing the spectrum efficiency in spectrum-limited satellite applications. In this paper, we consider a spectral coexistence scenario of a multibeam satellite and a monobeam satellite with the monobeam satellite as primary and the multibeam satellite as secondary. In this context, this paper focuses on examining the effect of FP on the performance of multi-carrier based IA technique. For this purpose, different IA techniques such as coordinated IA, uncoordinated IA and static IA have been considered. The effect of FP on the performance of different IA techniques in the considered scenario is evaluated in terms of system sum rate and primary rate protection ratio. It is shown that the system sum rate increases with the FP factor for all the techniques and the primary rate is perfectly protected with the coordinated IA technique even with dense FP.

The usable satellite spectrum is becoming scarce due to continuously increasing demand for broadcast, multimedia and interactive services. In this context, cognitive satellite communications has received important attention lately in the research community. Exploring efficient spectrum sharing techniques for enhancing spectral efficiency in satellite communication has become an important research challenge. In this paper, we study the main aspects of satellite cognitive communications and present possible practical scenarios for hybrid/dual cognitive satellite systems. Furthermore, suitable cognitive techniques for the considered scenarios are identified. More specifically, Spectrum Sensing (SS), interference modeling, and beamforming techniques are discussed for hybrid cognitive scenario and SS, interference alignment, and cognitive beamhopping techniques are discussed for dual satellite systems. This paper concludes by providing interesting open research issues in this domain.

Increasing demand of high‐speed data rate is leading to a challenging task to provide services to the users within exponentially growing market for wireless multimedia services. Subsequently, the available radio resources are becoming scarce because of different factors such as spectrum segmentation and dedicated frequency allocation to existing wireless standards. Exploring new techniques for enhancing the spectral efficiency in wireless communication has been an important research challenge. In this study, the enhancement of spectral efficiency of wireless communication systems is considered. A framework is proposed to implement the concept of compressive sampling (CS) for compressing the natural random signals. The performance of proposed framework is evaluated in the context of multiple input multiple output orthogonal frequency division multiplexing system. Simulation‐based results show that 25% of resources can be saved by marginal trade‐off with the quality of service (QoS) requirement applying CS to the natural random signals. Furthermore, it can be claimed that this QoS trade‐off can be optimised with dynamic selection of random measurement matrices.

In addition to Spectrum Sensing (SS) capability required by a Cognitive Radio (CR), Signal to Noise Ratio (SNR) estimation of the primary signals at the CR receiver is crucial in order to adapt its coverage area dynamically using underlay techniques. In practical scenarios, channel and noise may be correlated due to various reasons and SNR estimation techniques with the assumption of white noise and uncorrelated channel may not be suitable for estimating the primary SNR. In this paper, firstly, we study the performance of different eigenvalue-based SS techniques in the presence of channel or/and noise correlation. Secondly, we carry out detailed theoretical analysis of the signal plus noise hypothesis to derive the asymptotic eigenvalue probability distribution function (a.e.p.d.f.) of the received signal's covariance matrix under the following two cases: (i) correlated channel and white noise, and (ii) correlated channel and correlated noise, which is the main contribution of this paper. Finally, an SNR estimation technique based on the derived a.e.p.d.f is proposed in the presence of channel/noise correlation and its performance is evaluated in terms of normalized Mean Square Error (MSE). It is shown that the PU SNR can be reliably estimated when the CR sensing module is aware of the channel/noise correlation.

Next generation networks are moving towards the convergence of mobile, fixed and broadcasting services in one standard platform, which requires the co-existence of satellite and terrestrial networks in the same spectrum. This framework has motivated the concept of cognitive Satellite Communication (SatComs). In this aspect, the problem of exploiting Spectrum Sensing (SS) techniques for a dual polarized fading channel is considered. In this paper, the performance of Energy Detection (ED) technique is evaluated in the context of a co-existence scenario of a satellite and a terrestrial link. Diversity combining techniques such as Equal Gain Combining (EGC) and Selection Combining (SC) are considered to enhance the SS efficiency. Furthermore, analytical expressions for probability of detection (Pd) and probability of false alarm (Pf) are presented for these techniques in the considered fading channel and the sensing performance is studied through analytical and simulation results. Moreover, the effect of Cross Polar Discrimination (XPD) on the sensing performance is presented and it is shown that SS efficiency improves for low XPD.