In this paper, we focus on the issue of secure communication of an underlay spectrum sharing network. In our model, multi-antenna enabled secondary transmitter is powered by energy harvested from a primary transmitter. It controls its transmit power to meet an interference-outage constraint for the primary network under imperfect channel states of the interference links. We consider an energy-aware transmit antenna selection (ETAS) rule that selects an antenna for the secondary transmitter based on the energy status and different channel states in the presence of a passive eavesdropper. For evaluating secrecy performance, we first derive an analytical expression of interference-outage probability which helps to compute power margin factor and thereby analyze secrecy outage probability (SOP) and secrecy throughput (ST). Furthermore, to capture the reliability performance, we analyze connection outage probability (COP) and connection throughput (CT). Simulation results demonstrate that the ETAS rule achieves higher ST and CT and lower SOP and COP than several conventional antenna selection and space time transmission schemes.

Considering an reconfigurable intelligent surface (RIS)-assisted underlay spectrum sharing network, our goal is to minimize an average symbol error probability (SEP) of a secondary user while adhering to an interference-outage constraint imposed by a primary user. We first derive an optimal rule for on-off power control at a secondary source and passive beamforming at the RIS to minimize the average SEP at a secondary destination. We derive novel analytical expressions for the probability density functions of the effective interference channel power gains at the primary receiver and primary interference-outage probability. Building upon those analyses, we subsequently propose two simpler, yet near-optimal rules for on-off power control and RIS passive beamforming with lower complexity. Finally, simulation results corroborate the efficacy of the proposed framework and show the impact of different system parameters on the SEP.

The amalgamation of millimeter-wave (mmWave) communications and multiple-input multiple-output (MIMO) orthogonal time frequency space (OTFS) systems holds significant promise for next-generation wireless networks, offering high data rates and robust performance in high-mobility environments. This paper presents a novel convolutional neural network (CNN)-based channel estimation framework that unfolds the sparse Bayesian learning (SBL) algorithm into a deep neural network (DNN) for mmWave MIMO-OTFS systems. The proposed SBL-based Deep CNN (SBL-DCNN) is a modeldriven approach that combines the conventional expectation maximization (EM)-based SBL algorithm with a novel sparsity mask feature tailored exclusively for the delay Doppler domain (DD) to promote sparsity in channel predictions and accelerate the convergence of the training stage. This hybrid strategy improves MIMO-OTFS channel estimation by combining the strengths of SBL and domain-specific features, employing a cascade of diverse 2D convolution filters to effectively capture the complex underlying channel sparsity structures. The proposed network trained on data at moderate signal-to-noise power ratio (SNR) values demonstrates strong generalization and improved prediction performance across a wide range of SNR conditions in comparison to conventional sparse signal recovery-based channel estimation schemes.

This paper proposes novel Bayesian learning approaches for sparse channel estimation in a multi-user millimeter-wave massive multiple-input multiple-output underlay cognitive radio system. The intelligent reflecting surfaces (IRS)-aided secondary network adopts a two-phase transmission protocol comprising of silent and estimation phases. During the silent phase, the secondary base station(SBS) captures primary network pilot transmissions to estimate the cascaded channel between primary users and the SBS. Next, the estimation phase considers two pilot design policies with an inherent estimation accuracy and spectral efficiency trade-off, for cascaded channel estimation with respect to the secondary users, IRS, and SBS. Further, the associated hybrid and marginalized Cramér-Rao bounds are developed to benchmark the efficacy of proposed estimation schemes. Simulation results demonstrate the superior performance of the proposed approaches in comparison to existing compressed sensing methods such as orthogonal matching pursuit and subspace multi-user joint channel estimation.

Intelligent reflecting surface (IRS)-aided millimeter wave (mmWave) systems are potential contenders for large scale deployment in 5G and beyond communication. Owing to the passive nature of IRS systems, the acquisition of accurate channel state information in bandwidth constrained scenarios becomes a highly challenging task. This work leverages the inherent sparsity associated with cascaded channels of IRS mmWave systems to develop novel sparse Bayesian learning (SBL)-based channel estimation algorithms. The underlying row-wise temporal correlation of the effective angular domain sparse channel matrix is first theoretically demonstrated followed by the development of the temporal SBL channel estimation approach. Further, utilizing the scaling property of cascaded channels for multiple users, low complex variants of the proposed SBL solutions are developed. Simulation results demonstrate significant improvement of the proposed SBL schemes over existing techniques.

To address secrecy performance in a multi-relay underlay wiretap spectrum sharing network, we present a secrecy rate-optimal relay and antenna selection scheme, which serves as a fundamental benchmark. It jointly selects a transmit antenna at a secondary source, a receive antenna at its destination, and a relay between them to maximize the secrecy rate at the destination under the malicious attempt of an eavesdropper. We derive exact and asymptotic expressions for the secrecy outage probability, which accurately reveal the secrecy diversity order and the coding gain. Under a proportional interference constraint, full secrecy diversity order is achieved only when the main link is stronger than the wiretap link, otherwise the diversity gain is lost. We then consider a practical scenario where the secondary users have only the mean channel power gains of the wiretap links. Under such channel state information (CSI), we propose a statistical CSI-based relay and antenna selection scheme and numerically show that this can be used as a better performance/complexity tradeoff. Both the schemes substantially outperform the conventional relay and antenna selection scheme, which requires no CSI about the wiretap links.

Cooperative relaying and multiple-input multiple-output (MIMO) transmission technologies exploit spatial diversity to improve the performance of the secondary users in an underlay cognitive radio network. We consider a MIMO cognitive relay network in which a secondary source and multiple relays have imperfect channel state information (CSI) of the interference links to the primary receiver. They sufficiently back-off their transmit powers on the basis of such CSI in order to adhere to an interference outage constraint. We propose an optimal relay and antenna selection scheme, which jointly selects a relay between the source and destination, a transmit antenna at the source, and a receive antenna at the destination to maximize the end-to-end signal-to-interference-plus-noise ratio (SINR) at the destination. To demonstrate the advantages of our proposed framework, we derive closed-form expression for the outage probability of the secondary network under non-identically distributed Rayleigh fading channels. We also derive an insightful expression for the asymptotic outage probability for high SINR and show that the diversity gain is lost when the interference power constraint is fixed. We then consider a practical scenario where the secondary users have only the mean channel power gains of the interference links. Under such CSI, we also derive an expression for the outage probability, and show that this can be used as a better performance/complexity tradeoff for high SINR.

The availability of channel state information (CSI) about the interference links from the secondary transmitters to the primary receivers is widely assumed in the literature on underlay cognitive radio (CR) in order to control the interference caused to the primary network. However, when multiple primary receivers are present, acquiring such CSI about all the interference links in a timely and scalable manner is practically challenging. We study an underlay cooperative relay system, in which the channel gains of only a subset of the interference links are available at the source and relays. Based on such incomplete CSI, the source and relays back-off their transmit powers in order to satisfy an interference outage constraint. We derive a tight upper bound on the outage probability of the secondary system for the rate-optimal relay selection rule. Our numerical results show the effect of incomplete CSI on the secondary system performance and how its impact can be ameliorated.

Cooperative relaying combined with selection exploits spatial diversity to improve the performance of interference-constrained secondary users in an underlay cognitive radio (CR) network. While a relay improves the signal-to-interference-plus-noise ratio (SINR) of the secondary network, it requires two hops and also generates interference to the primary network. We present a novel, optimal relay selection rule that maximizes the fading-averaged transmission rate of an average interference-constrained underlay secondary network. It differs from the several ad hoc incremental relaying schemes proposed in the literature, while requiring a feedback overhead that is comparable to them. We then analyze the average rate of the optimal rule. We also present insightful high and low SINR asymptotic analyses, which bring out the extent to which the use of the relays improves the average rate as a function of the system parameters. Our numerical results show that the proposed rule outperforms several known relay selection schemes for CR, and also characterize the regimes in which some of these schemes are near-optimal.

Cooperative relaying combined with selection exploits spatial diversity to improve the performance of interference-constrained secondary users in an underlay cognitive radio network. While a relay improves the signal-to-interference- plus-noise ratio, it requires two hops and also generates interference to the primary. Therefore, in underlay cognitive radio, new criteria are needed to determine which relay to select. We present an optimal relay selection rule that maximizes the fading-averaged transmission rate of an average interference-constrained underlay secondary network. It differs from the many rules proposed in the literature. We then analyze its fading-averaged channel capacity. Numerical results show that the proposed rule outperforms direct transmission and several other rules, such as incremental relaying, proposed in the literature.

Cooperative relaying combined with selection exploits spatial diversity to significantly improve the performance of interference-constrained secondary users in an underlay cognitive radio network. We present a novel and optimal relay selection (RS) rule that minimizes the symbol error probability (SEP) of an average interference-constrained underlay secondary system that uses amplify-and-forward relays. A key point that the rule highlights - for the first time - is that, for the average interference constraint, the signal-to-interference-plus-noise-ratio (SINR) of the direct source-to-destination (SD) link affects the choice of the optimal relay. Furthermore, as the SINR increases, the odds that no relay transmits increase. We also propose a simpler, more practical, and near-optimal variant of the optimal rule that requires just one bit of feedback about the state of the SD link to the relays. Compared to the SD-unaware ad hoc RS rules proposed in the literature, the proposed rules markedly reduce the SEP by up to two orders of magnitude.

Cooperative relaying combined with selection has been extensively studied in the literature to improve the performance of interference-constrained secondary users in underlay cognitive radio (CR). We present a novel symbol error probability (SEP)-optimal amplify-and-forward relay selection rule for an average interference-constrained underlay CR system. A fundamental principle, which is unique to average interference-constrained underlay CR, that the proposed rule brings out is that the choice of the optimal relay is affected not just by the source-to-relay, relay-to-destination, and relay-to-primary receiver links, which are local to the relay, but also by the direct source-to-destination (SD) link, even though it is not local to any relay. We also propose a simpler, practically amenable variant of the optimal rule called the 1-bit rule, which requires just one bit of feedback about the SD link gain to the relays, and incurs a marginal performance loss relative to the optimal rule. We analyze its SEP and develop an insightful asymptotic SEP analysis. The proposed rules markedly outperform several ad hoc SD link-unaware rules proposed in the literature. They also generalize the interference-unconstrained and SD link-unaware optimal rules considered in the literature.

Cooperative relaying combined with selection exploits spatial diversity to significantly improve the performance of interference-constrained secondary users in an underlay cognitive radio (CR) network. However, unlike conventional relaying, the state of the links between the relay and the primary receiver affects the choice of the relay. Further, while the optimal amplify-and-forward (AF) relay selection rule for underlay CR is well understood for the peak interference-constraint, this is not so for the less conservative average interference constraint. For the latter, we present three novel AF relay selection (RS) rules, namely, symbol error probability (SEP)-optimal, inverse-of-affine (IOA), and linear rules. We analyze the SEPs of the IOA and linear rules and also develop a novel, accurate approximation technique for analyzing the performance of AF relays. Extensive numerical results show that all the three rules outperform several RS rules proposed in the literature and generalize the conventional AF RS rule.