We propose a novel integrated sensing and communication (ISAC) framework that combines active reconfigurable intelligent surfaces (RIS) with a movable antenna (MA) array at the base station to jointly enhance the communication throughput and the radar sensing resolution. Unlike conventional architectures that employ passive RIS or fixed antenna arrays, the proposed framework leverages dual-domain reconfigurability, electromagnetic and geometric, by jointly optimizing transmit beamforming, RIS reflection coefficients with amplification constraints, and the spatial positions of the mobile antennas. The system is modeled under a practical RIS noise amplification model and subject to the constraints of stringent signal-to-interference-plus-noise ratio (SINR), radar beampattern, and transmission power. A unified optimization problem is formulated and decomposed into tractable subproblems using an alternating optimization approach based on semidefinite relaxation (SDR), successive convex approximation (SCA), and convex programming. Numerical results confirm that the proposed design significantly outperforms conventional passive RIS and fixed array systems in terms of both radar and communication metrics, particularly under dynamic channel conditions and constrained power budgets.

This paper proposes a quantum-enabled gradient-based coverage optimization (QEGCO) framework for dynamic antenna configuration in future wireless networks. The optimization task of adjusting antenna azimuth and tilt to maximize soft coverage is formulated using a differentiable surrogate objective, enabling the application of gradient-based methods. A parameterized quantum circuit (PQC) encodes the control variables, and the parameter-shift rule is employed to compute exact gradients efficiently, independent of spatial sampling resolution. Entanglement is introduced via CNOT gates to enhance circuit expressivity and model interactions between antennas. Simulation results demonstrate that QEGCO achieves faster convergence, higher final coverage ratios, and superior computational scalability compared to classical stochastic gradient descent (SGD) and finite gradient descent (FGD) baselines. Complexity analysis highlights that QEGCO reduces computational complexity from ${\mathcal{O}}(2\cdot a\cdot T)$ in classical methods to ${\mathcal{O}}(2\cdot a\cdot\log (T))$, where a is the number of antennas, and T is the total time required for computation of gradients and coverage metrics. These findings illustrate the potential of quantum-assisted optimization techniques for scalable and efficient wireless network self-organization. Future directions include extending QEGCO to cooperative multi-cell networks and implementing hardware-efficient quantum circuits for near-term quantum devices.

The integration of dual-functional radar-communication (DFRC) systems with active reconfigurable intelligent surfaces (RIS) and movable antennas (MA) offers a powerful mechanism to meet the demands of sixth-generation (6G) networks. This paper presents a novel active RIS-aided integrated sensing and communication (ISAC) system where a DFRC base station (BS) equipped with a movable antenna array simultaneously serves multiple users and senses radar targets. The active RIS, incorporating low-power amplifiers, compensates for signal attenuation while reconfiguring the wireless environment. The MA array provides additional spatial degrees of freedom to enhance beam control, angular resolution, and robustness to blockage. To fully exploit this configuration, we formulate a joint beamforming and position optimization problem under SINR and power constraints. A successive convex approximation (SCA)-based alternating optimization algorithm is developed to address the non-convexities. Numerical results verify significant gains in communication throughput and radar beam sharpness compared to conventional passive RIS and fixed phased arrays, highlighting its potential for dynamic 6G scenarios such as intelligent surveillance and autonomous mobility.

As the proliferation of Internet of Things (IoT) devices accelerates, next-generation wireless networks face unprecedented demands for secure, efficient, and scalable communication frameworks. This paper investigates the integration of non-orthogonal multiple access (NOMA) with reconfigurable intelligent surfaces (RIS) and full-duplex (FD) operations to address these challenges while mitigating the adverse effects of residual hardware impairments (HWI) that cause signal distortion. The proposed RIS-aided FD-NOMA system is designed to enhance resilience and secrecy in IoT communication networks, optimizing the secrecy rate while adhering to power constraints for active beamforming at the base node (BN) and unit-modulus requirements for passive beamforming via RIS. Employing an alternate optimization (AO) framework, the complex joint optimization problem is divided into tractable subproblems solved through generalized convex approximations to achieve near-optimal solutions. Numerical results validate the superiority of the proposed model, demonstrating substantial performance improvements over conventional IoT systems without RIS support or with half-duplex NOMA protocols. The findings underscore the transformative potential of RIS-aided FD-NOMA systems in securing IoT networks against hardware imperfections while meeting their stringent connectivity and security demands.

This article explores the critical aspect of electronic hardware reliability analysis in reconfigurable intelligent surface (RIS)-aided networks within the context of sixth-generation (6G) communications. Recognizing the potential vulnerabilities of metasurfaces to environmental factors, we highlight the continuous hardware impairments that can significantly impact the electromagnetic properties of RISs, reducing their lifetime. Extending the life cycle of RISs is strategically important, especially in mission-critical ultra-reliable wireless applications where system failures can result in significant costs and, in extreme cases, necessitate structural replacements. Accordingly, we investigate the nonresidual continuous hardware degradation of RISs through a stochastic process and optimize maintenance strategies using statistical information to extend the RIS system's lifespan. The optimal life expectancy of the RIS system with systematic maintenance concerning the observed impairment level is demonstrated through analytical results. The findings indicate that the information-based framework can significantly extend the expected life of a RIS system by postponing maintenance. Furthermore, a comprehensive mathematical framework for reliable communication is introduced, whereby the distribution of the received SINR is determined in the presence of hardware impairment due to imperfect maintenance. Through extensive numerical simulations, the efficacy and robustness of the proposed framework under hardware impairments are illustrated.

As we advance towards sixth-generation (6G) communications, the integration of dual-functional radar and communication (DFRC) systems with active reconfigurable intelligent surfaces (RIS) emerges as a promising solution to enhance spectrum efficiency. This work introduces an innovative DFRC system enhanced by an active RIS, capable of amplifying and phase-shifting signals to improve communication quality. We propose an optimization algorithm that maximizes combined data rates while considering constraints on radar probing power and power budgets for both DFRC and active RIS. Employing techniques such as weighted minimum mean square error (WMMSE) and fractional programming, our simulation results demonstrate that active RIS-enhanced DFRC systems significantly outperform passive RIS and non-RIS setups in terms of Weighted Sum Rate (WSR). Further analyses reveal that active RIS effectively mitigates multiplicative fading and enhances signal directivity, with performance gains influenced by the number of RIS elements, total system power, and radar detection power ratio. This work highlights the transformative potential of active RIS in optimizing next-generation wireless networks.

In this work, we explore the use of active reconfigurable intelligent surfaces (A-RIS) to improve vehicle-to-everything (V2X) communication systems to address limitations in traditional vehicular communication. In particular, we formulate an optimization problem to maximize the uplink sum rate for vehicle-to-infrastructure (V2I) links by optimizing transmit precoders, phase-shift matrices, transmit power, and spectrum sharing for vehicle-to-vehicle (V2V) links. To handle complex hybrid control scenarios, we propose a mixed-action deep reinforcement learning (DRL) algorithm and compare it with conventional benchmark methods like deep deterministic policy gradient (DDPG) with discrete actions (DA) and alternating optimization (AO). We evaluate the proposed algorithm’s effectiveness under imperfect channel state information as well. Simulation results highlight the efficacy of our approach, demonstrating significant enhancement in vehicular communication quality through A-RIS. Furthermore, we illustrate the impact of various factors such as number of A-RIS elements, vehicle speed, loss, execution time, amplification power, and CSI error on the performance of the V2X system.

In this paper, we consider the digital twin (DT) of a reconfigurable intelligent surface (RIS) aided communication system, whereby the problem of mitigating the effects of increasing hardware impairments at the RIS system to increase its lifespan is studied. A base station (BS) with dedicated control signals inspects a hardware-impaired RIS system in real time. The DT determines whether systematic maintenance should be done immediately or postponed in order to increase the expected lifespan of the RIS system. To make an optimal decision, the DT uses information from the BS about the level of impairment to effectively switch between the RIS elements. Furthermore, we focus on the case of single systematic maintenance with a deterministic failure threshold but also discuss generalizations for multiple systematic maintenances. Extensive numerical examples are provided to elucidate the optimal solutions obtained and illustrate the efficacy of the proposed method.

In this work, we investigate the performance of a federated edge learning (FEEL)-enhanced edge computing in unmanned aerial vehicles (UAV)-aided internet of things (IoT) system under the consideration of limited energy at each UAV. It consists of multiple UAVs which apply FEEL for local training and, then, transmit the required parameters to a centralized IoT-server. We use a new cost metric obtained by a linear combination of latency and energy consumption, and formulate an optimization problem to jointly optimize the central processing unit (CPU)-frequency during FEEL and allotted bandwidth under the consideration of the limited overall system bandwidth and energy available at each UAV. Due to the non-convex nature of the formulated problem, we propose a twin delayed deep deterministic policy gradient (TD3)-based algorithm that solves the problem and provides the optimum CPU frequency and allotted bandwidth to each user. We validate the accuracy and convergence of the proposed algorithm via exhaustive simulations and highlight its effectiveness by comparing its performance with that of deep deterministic policy gradient (DDPG) and deep Qnetwork (DQN)-based solutions.

Due to the dynamic nature of a vehicular fog computing environment, efficient real-time resource allocation in an Internet of Vehicles (IoV) network without affecting the quality of service of any of the onboard vehicles can be challenging. This article proposes a priority-sensitive task offloading and resource allocation scheme in an IoV network, where vehicles periodically exchange beacon messages to inquire about available services and other important information necessary for making the offloading decisions. In the proposed methodology, the vehicles are stimulated to share their idle computation resources with the task vehicles, whereby a deep reinforcement learning algorithm based on soft actor–critic is designed to classify the tasks based on priority and computation size of each task for optimally allocating the power. Furthermore, we also design deep deterministic policy gradient (DDPG) and twin delayed DDPG (TD3) algorithms for the considered framework. In particular, the algorithms work toward achieving the optimal policy for task offloading by maximizing the mean utility of the considered network. Extensive numerical results under different network conditions, along with comparison among the three algorithms, are presented to validate the feasibility of distributed reinforcement learning for task offloading in future IoV networks.

Ultra-reliable low-latency communication (URLLC) is one of the most important components in the fifth generation (5G) cellular networks for realizing mission-critical applications. In this paper, we jointly optimize the transceiver design and decoding error probability (DEP) of a full-duplex (FD) URLLC system, where the base station (BS) operates in FD mode, while the uplink (UL) and downlink (DL) users work in half-duplex (HD) mode. Accordingly, an optimization problem is formulated to maximize the achievable total (UL plus DL) rate for an FD URLLC system under finite blocklength, subject to the end-to-end (E2E) reliability constraint from the UL user to each DL user and the total transmission power constraint at the UL user and at the BS. We analyze the problem structure and convexify the problem by approximating the channel dispersion in scenarios of high and mid-to-high signal-to-interference plus noise ratio (SINR) regimes, respectively. Next, efficient iterative algorithms are proposed to find the near-optimal power allocation for the UL user and transceiver weights for the BS. Furthermore, closed-form expressions of the transceiver weights are derived, and the convergence of the proposed algorithms is proved. Simulation examples demonstrate the impact of the code blocklength, number of DL users, transmitter/receiver distortion and DEP threshold on the system performance.

An in-band full-duplex (IBFD) multiple-input multiple-output (MIMO) radio’s self-interference (SI) and co-channel interference (CCI) cancellation strengths usually determine its performance gains over conventional half-duplex ones. Accordingly, this paper explores an alternative to traditional optimization driven design (ODD) techniques available in the literature for beamformer design in IBFD radios. In particular, to mitigate the residual SI and CCI, we propose a run-time data-driven prediction approach to predict the beamforming matrices at the uplink users and the base station. First, we formulate an ODD-based beamforming design problem, which we structurally optimize through sum-rate maximization, and cast it as a second-order cone programming problem. Then, we repeatedly solve this problem to generate a dataset forming a multiple multivariate regression problem. We use the dataset to train a multi-layer perceptron (MLP) employing a supervised learning scheme to solve the associated regression problem. Experimental results demonstrate that the MLP based beamformer design achieves a near-optimal performance at a remarkably high speed for reasonable residual SI and CCI cancellation without the need for explicit channel estimation.

Intelligent reflecting surface (IRS) is being considered as a prospective candidate for next generation wireless communication due to its ability to significantly improve coverage and spectral efficiency by controlling the propagation environment. One of the ways IRS increases spectral efficiency is by adjusting phase shifts to perform passive beamforming. In this letter, we integrate the concept of IRS aided communication to the domain of multi-direction beamforming, whereby multiple receive antennas are selected to convey more information bits than existing spatial modulation (SM) techniques at any specific time. To complement this system, we also propose a successive signal detection (SSD) technique at the receiver. Numerical results show that the proposed design is able to improve the average successful bits transmitted (ASBT) by the system, which outperforms other state-of-the-art methods proposed in literature.

To alleviate the backhaul congestion in future hybrid heterogenous networks, this paper investigates the potential benefits of implementing storages in small base stations (SBSs) operating at frequency range 2 (FR2) bands that co-exist with a tier of massive multiple input multiple output (MIMO) macro BSs (MBSs) operating at frequency range 1 (FR1) bands. We develop a unified analytical framework and derive theoretical bounds for such a cache-enabled FR1-FR2 hybrid network under limited backhaul scenario to analyze the exact and approximate latency, average success probability of file delivery, and average data rate considering two open-access user association policies: i) location-based and ii) content-based association. Numerical results demonstrate that wireless edge caching (WEC) can improve the performance of hybrid wireless networks, albeit certain trade-offs, e.g., increasing cache-enabled SBSs cannot always improve the network performance and there exists an optimal SBS density that provides the best latency and throughput performance. Furthermore, we compare the performance of the network with respect to other key network design parameters such as cache size, content popularity, backhaul capacity, and blockages for both the user associations. Our results show that latency under content-based user association is less than that of location-based user association, and although the difference in the average rates under the two user associations is not obvious, content-based association can extricate more backhaul capacity and thus reduce installation cost significantly.

In this paper, we jointly optimize the transceiver design and decoding error probability of a full-duplex (FD) ultrareliable low-latency communication (URLLC) system, where the base-station (BS) operates in an FD mode while the uplink (UL) and downlink (DL) users work in a half-duplex (HD) mode. Accordingly, an optimization problem is formulated for an FD URLLC system under the finite blocklength (FBL) to maximize the achievable total (UL plus DL) rate subject to the reliability (i.e., the decoding error probability) of each link and total transmission power constraints at the UL user and the BS. We convexify the formulated non-convex problem by analyzing the problem structure. Next, an efficient iterative algorithm is proposed to find the near-optimal power allocation for the UL user and the transceiver weights for the BS. Simulation examples show the impact of the blocklength and decoding error probability on the system performance.

Spectrum sharing and full duplexing are two promising technologies for alleviating the severe spectrum crunch that has threatened to blight the progress of future wireless communication systems. In this paper, we consider a two tier coexistence framework involving a collocated multiple-input-multiple-output (MIMO) radar system (RS) and a full-duplex MIMO cellular system (CS). Considering imperfect channel state information and hardware impairments at the CS, we focus on a spectrum sharing environment to improve the quality of service (QoS) for cellular users by designing i) precoders at CS via the minimization of sum mean-squared-errors, subject to the constraints of transmit powers of the CS and probability of detection (PoD) of the RS, and ii) precoders at the RS to mitigate the interference from RS towards CS. While the monotonically increasing relationship between PoD and its non-centrality parameter is exploited to resolve the PoD in terms of interference threshold towards the RS, a generalized likelihood ratio test for target detection is used to derive detector statistics of the precoded radar waveforms. Numerical results demonstrate the feasibility of the proposed spectrum sharing algorithms, albeit with certain tradeoffs in RS transmit power, PoD and QoS of cellular users.

In this paper we investigate the co-existence between a multi-cell multi-user (MU) full-duplex (FD) multiple-input multiple-output (MIMO) cellular network and a MIMO radar. While a joint beam-forming design technique at the cellular base-stations and users is proposed to maximize the detection probability of the MIMO radar subject to constraints of data rate per user in each cell and transmit power, null-space based waveform projection is used to mitigate the interference from the radar towards the cellular network. In particular, the proposed technique optimizes the performance of detection probability by maximizing its lower bound, which is obtained by exploiting the monotonically increasing relationship of detection probability and its non-centrality parameter. Numerical results show the feasibility of spectrum sharing between both systems.

In this paper, we investigate the feasibility of wireless edge caching in a hybrid millimeter-wave (mmWave)- micro-wave (μWave) network. Considering the average success probability (ASP) of file delivery as the performance metric, we derive expressions for the association probability of the typical user to the mmWave and μWave networks using stochastic geometric tools. Accordingly, we provide an upper bound on the ASP of file delivery and formulate the content caching placement scheme as an optimization problem with respect to caching probabilities, which jointly optimizes the ASP of file delivery considering both content placement and delivery phases. To simplify the non-convex problem and obtain design insights, we split it into two scenarios: 1) noise-limited and 2) interference-limited, and then propose optimal caching placement algorithms for both. We numerically evaluate the performance of the proposed schemes under several essential factors, such as content popularity, cache size, target data rate, blockages in the mmWave network, BS density, and path loss and also compare it with other common proactive caching schemes, namely uniform caching, caching M most popular files, and random caching. Numerical results demonstrate the superiority of the proposed caching scheme over others, albeit certain trade-offs.

In this paper, we investigate a full duplex (FD) multi-user non-orthogonal multiple access (NoMA) communication system based on the optimization of received signal-to-interference-plus-noise ratio (SINR) per unit power. Since the communication system operates in the FD mode, co-channel interference (CCI) and self-interference (SI) dominate the system's performance. Accordingly, to combat the CCI, we adopt a game-theoretic approach and propose users' clustering algorithms and to suppress the SI, we formulate an optimization problem to maximize the power-normalized SINR (PN-SINR). While the user clustering optimization problem is constrained by: 1) the successive interference cancellation (SIC) constraint and 2) two binary constraints for the allocations of uplink (UL) and downlink (DL) users, the PN-SINR problem is constrained by: 1) total transmit power budget at the base station and UL users; 2) the fundamental condition for the implementation of successive interference cancellation in the NoMA; and 3) the minimum fairness condition for the UL users. The original PN-SINR problem is non-convex and hence is converted into an equivalent subtractive-form problem, after which we propose an iterative algorithm to find the optimal power allocation policy. Properties of all the proposed algorithms are thoroughly investigated and the numerical results are provided. Based on the channel conditions and suppression level of SI and CCI, the superiority of the proposed FD-NoMA system over half-duplex NoMA and FD orthogonal multiple access systems is verified.

In this paper, spectrum sharing between a multiple-input-multiple-output (MIMO) radar and a full duplex (FD) multi-user MIMO (MU-MIMO) cellular system is studied. While a joint transceiver design technique at a hardware impaired FD cellular system considering imperfect channel state information is developed to maximize the detection probability of the MIMO radar, null-space based waveform projection is proposed to mitigate the interference from the radar towards the cellular system. The proposed technique exploits the monotonically increasing relationship of detection probability and its non-centrality parameter and accordingly optimizes the performance of detection probability of the radar, while also providing the cellular users with specific quality-of-service (QoS). Numerical results demonstrate the feasibleness of spectrum sharing between both systems, albeit certain trade-offs in radar transmit power, detection probability and QoS of users in the cellular system.

We propose a novel transceiver design technique to facilitate flexible spectrum sharing between a multiple-input multiple-output (MIMO) radar and a full-duplex (FD) MIMO cellular system. The optimization problem for maximizing the rate of the cellular system is formulated, subject to the constraints of individual power at the uplink users, total power at the base station, and interference power towards the MIMO radar from the cellular system so that the detection probability of the radar is not hindered. We show that the above problem can be cast as a second-order cone programming problem and the joint design of transceiver matrices can be obtained through an iterative algorithm. Numerical results show that using the spectrum shared by the radar, the FD cellular system can achieve sum rate of up to 25-30 bits/sec/Hz for a reasonable self-interference cancellation of around -70 dB. However, to facilitate this, while also maintaining a detection probability of around 0.9, the radar needs to spend an extra power of around 2-3 dB.

In this paper, we investigate the energy harvesting (EH) technique and accordingly design transceivers for a K link multiple-input multiple-output interference channel. Each link consists of two full-duplex (FD) Internet of Things (IoT) nodes exchanging information simultaneously in a bi-directional communication channel. All the nodes suffer from interference, in particular strong self-interference and inter-node interference, due to operating in FD mode and simultaneous transmission at each link, respectively. Further, we divide the received signal at each node into two parts. While one part of the signal is used for information decoding, the other part is used for EH. We jointly design the transmit and receive beamforming vectors and receiver power splitting ratios by minimizing the total transmission power of the system, subject to both signal-to-interference-plus-noise ratio and EH threshold constraints. Furthermore, the case of multiple-input single-output interference channel is also included for the sake of comparison. We also revisit the above problems for the case when the available channel state information (CSI) at the transmitters is imperfect, where the errors of the CSI are assumed to be norm bounded. Simulation results show that the EH technique can harvest enough energy to support power consumption limited IoT devices by aiding in recharging their respective batteries.

In this paper, the feasibility of spectrum sharing between a multiple-input multiple-output (MIMO) radar system (RS) and a MIMO cellular system (CS), comprising of a full-duplex (FD) base station (BS) serving multiple downlink and uplink users at the same time and frequency is investigated. While a joint transceiver design technique at the CS's BS and users is proposed to maximize the probability of detection (PoD) of the MIMO RS, subject to constraints of quality of service (QoS) of users and transmit power at the CS, null-space based waveform projection is used to mitigate the interference from RS toward CS. In particular, the proposed technique optimizes the performance of PoD of RS by maximising its lower bound, which is obtained by exploiting the monotonically increasing relationship of PoD and its non-centrality parameter. The numerical results show the utility of the proposed spectrum sharing framework, but with certain tradeoffs in performance corresponding to RS's transmit power, RS's PoD, CS's residual self-interference power at the FDBS and QoS of users.

In this paper, we analyze the performance of a millimetre wave (mmWave) network where the base stations (BSs) equipped with very large uniform circular arrays (UCA) serve spatially distributed users. Due to its capability of scanning through both the azimuth as well as elevation dimensions, a 3D UCA is considered at the BSs to characterize the signal-to-interference-plus-noise ratio (SINR) of the network. Furthermore, stochastic geometric tools are employed while modelling the interference associated with a particular user. Thereby, expressions for coverage probability of the network is derived and validated in the numerical section. In particular, with the help of simulation results, we show the effect of the number of BS antennas, radius of the circular coverage area and different network parameters such as BS density, blockage density, path loss exponent, and SINR threshold on the coverage probability of the mmWave network.

The rapid growth in energy consumption due to strong demands of wireless multimedia services, has become a major concern from an environmental perspective. In this paper, we investigate a novel energy-efficient resource allocation scheme for relay-assisted multiuser networks to maximize the energy efficiency (EE) of the network by jointly optimizing the subcarrier pairing permutation formed in one-to-many/many-to-one manner, subcarrier allocation, as well as the power allocation all together. By analyzing the properties of the complex mixed-integer nonlinear programming problem, which is generally very difficult to solve in its original form, we transform the problem into an equivalent convex problem by relaxing the integer variables using the concept of subcarrier time sharing, and by applying a successive convex approximation approach. Based on the dual decomposition method, we derive an optimal solution to the joint optimization problem. Numerical results are provided to validate the theoretical findings and to demonstrate the effectiveness of the proposed algorithms.

We consider the beamforming design to maximize the energy-efficiency (EE) of a K link multiple-input multiple-output (MIMO) interference channel, where each link consists of two full-duplex (FD) nodes. We first transform the original fractional form optimization problem into an equivalent subtractive-form optimization problem by exploiting the properties of fractional programming, and then perform a two-layer optimization scheme. In the outer layer, the EE parameter is searched using a simple one-dimensional search, and in the inner layer, the relationship between Weighted-Sum-Rate (WSR) and Weighted Minimum-Mean-Squared-Error (WMMSE) problems is exploited to solve the subtractive form optimization problem. Simulation results show that the EE of FD systems is lower than that of half-duplex (HD) systems, as FD nodes need to overcome self-interference (SI) which leads to high power consumption.

In this paper, we analyze the secrecy characteristics of random wireless networks using stochastic geometric tools. The locations of the source and eavesdropper nodes are modeled as independent 2-D Poisson point processes. We investigate the secrecy outage probability of such networks from the perspective of the k th best source, which has still not been well characterized. In particular, we derive the received path gain distributions of the typical destination and the eavesdropper from the k th best source. Furthermore, we introduce a novel concept of security-region based on the k th best source index. This is pragmatic in creating a protected communication zone for the typical destination and also to bind the number of sources that can coordinate in a coordinated multi-point transmission (CoMP) network. We further derive the secrecy outage probability for these CoMP sources based on the security-region. We also provide a closed-form expression for the maximum number of eavesdroppers for a given secrecy outage constraint, which can effect the secure communication. Tractable numerical, and simulation results are presented under various assumptions of densities, path loss exponents, and antenna figures.

The secrecy outage of millimeter wave (mmWave) networks under the impact of blockages is derived. Specifically, using a network model that accounts for uncertainties both in node locations and blockages, we characterize the connection outage probability and the secrecy outage probability of mmWave networks with multiple eavesdroppers under basic factors such as density of eavesdropping nodes, antenna gain and blockage density. As a desirable side effect, certain factors such as blockages and reduced antenna gain can decrease the secrecy outage probability. This however is in contrast to general mmWave systems where it has been shown that reduced blockages and high antenna gains provide higher capacities.

We examine the deployment of a large multi-user multiple-input multiple-output (MIMO) system and the resulting Energy Efficiency (EE) considering a 2D rectangular array with increasing antenna elements within a fixed physical space. The resulting increasing mutual coupling and correlation among the base station (BS) antennas are incorporated by deriving a practical mutual coupling matrix which considers coupling among all antenna elements. We also provide a realistic analysis of the energy consumption using a new model, taking into account the circuit power consumption as a function of the number of BS antennas and then present a performance analysis of the system with respect to EE. Our analysis shows that while spectral efficiency increases with increasing number of BS antennas in a massive MIMO system, EE does not increase boundlessly when the increasing number of antennas are to be accommodated within a fixed physical space and the total power consumed is considered to be a function of the BS antennas. Accordingly, analytic expressions for the optimum number of antennas to attain maximum EE are obtained.

The potential benefits of deploying relays in outdoor millimeter-wave (mmWave) networks are investigated. We derive the coverage probability from sources to a typical destination for such systems aided by relays. The sources and the relays are modeled as independent homogeneous Poisson point processes. We present a relay modeling technique for mmWave networks considering blockages and compute the density of active relays that aid the transmission. Closed form expressions for end-to-end signal to noise ratio for two relay selection techniques, namely best path selection and best relay selection are derived. Finally, we analyze the coverage probability and transmission capacity of the network and validate them with simulation results. Our results show that the coverage probability and transmission capacity of mmWave systems, which are often affected by extensive blockages can be increased considerably with the aid of relays.

We study a full duplex (FD) multiple-input multiple-output (MIMO) cognitive cellular network, in which a secondary base-station (BS) operating in FD mode serves multiple uplink (UL) and downlink (DL) secondary users (SUs) operating in half-duplex (HD) mode simultaneously. The spectrum is shared between secondary and primary networks, and thus uplink SUs and secondary BS generate interference on primary users (PUs). We assume that the channel state information (CSI) available at the transmitters is imperfect, and the errors of the CSI are assumed to be norm bounded. Under the impact of channel uncertainty, we address the robust minimization of the sum of mean-squared-errors (MSE) of all estimated symbols subject to power constraints at the uplink SUs and secondary BS, and interference constraints projected to each PU. We show that this problem can be cast as a Semidefinite programming (SDP), and joint design of transceiver matrices can be obtained through an iterative algorithm. Numerical results are presented to show the effectiveness and robustness of the proposed robust algorithm.

The secrecy characteristics of random wireless networks considering a generalized fading model, where the communication nodes are equipped with multiple antennas is analyzed. Using stochastic geometric tools, we investigate the secrecy outage probability of such networks from the perspective of the kth best source, which has still not been well characterized in the literature. In particular, we derive the received path gain distributions of the typical destination and the eavesdropper from the k-th best source. Using these results, we derive the secrecy outage probability for two scenarios with respect to the received path gain from any best source and the k-th best source. Furthermore, we introduce a novel concept of security-region based on the k-th best source index. This is pragmatic in creating a protected communication zone for the typical destination. Tractable numerical and simulation results are presented under various assumptions of fading, path loss exponents and antenna figures.

We consider a K link multiple-input multiple-output interference channel where each link consists of two full-duplex (FD) nodes. Two transmit beamforming design problems are solved: 1) sum-power minimization problem subject to rate constraints and 2) energy-efficiency maximization problem subject to individual power constraints. To tackle the sum-power minimization problem, we first generalize the well-known relationship between weighted-sum-rate (WSR) and weighted minimum-mean-squared-error (WMMSE) problems, originally used to solve the sum-rate maximization problems, and then propose a low complexity centralized algorithm, which converges to a stationary point. To decrease the exchange of a huge amount of data and excessive signaling traffic among the nodes, a distributed algorithm is also proposed. For the energy-efficiency maximization problem, the original fractional form optimization problem is first transformed into an equivalent subtractive-form optimization problem by exploiting the properties of fractional programming, and then performs a dual-layer optimization scheme. In the outer layer, the energy-efficiency parameter is searched using a simple 1-D search, and in the inner layer, the relationship between WSR and WMMSE is exploited to solve the subtractive form optimization problem. Since the proposed algorithms require perfect channel-state-information, which is difficult to acquire in practice, we also propose a robust design, by taking the imperfect channel knowledge into consideration. It is shown in the simulations that the sum-power achieved in FD mode depends heavily on the transmitter/receiver distortion. Also the energy-efficiency of FD systems is lower than that of half-duplex systems, as FD nodes need to overcome self-interference and increased inter-user interference, which leads to high power consumption.

The secrecy outage of millimeter wave (mmWave) overlaid micro-wave (μWave) networks under the impact of blockages is analyzed, and closed form as well as integral expressions are provided. Specifically, using a network model that accounts for uncertainties both in node locations and blockages, we characterize the conditional connection outage probability and the secrecy outage probability of hybrid networks with multiple eavesdroppers under basic factors, such as density of eavesdropping nodes, antenna gain, and blockage density. The upper and lower bounds of the conditional secrecy outage probability for both the line-of-sight and non-line-of-sight links are derived. As a desirable side effect, certain factors, such as blockages and reduced antenna gain, can decrease the secrecy outage probability in mmWave networks. This can be considered as a tradeoff between outage capacity and secrecy outage capacity with respect to blockages. Hence, blockages that have been proved to be detrimental to achieving higher data rates in mmWave systems, can be helpful for systems with secrecy constraints. Finally, we have shown the coexistence of mmWave and μWave networks from a secrecy perspective.

Massive multiple-input-multiple-output (MIMO) systems deploying a large number of antennas at the base station (BS) have been shown to produce high spectral and energy efficiency (EE) under the assumptions of increasing BS physical space and critical antenna spacing. We examine the deployment of massive MIMO systems and resulting EE with a more realistic scenario considering a 2-D rectangular array with increasing antenna elements within a fixed physical space. Mutual coupling and correlation among the BS antennas are incorporated by deriving a practical mutual coupling matrix which considers coupling among all antenna elements within a BS. We also provide a realistic analysis of the energy consumption using a model, which takes into account the circuit power consumptions as a function of the number of BS antennas and then present a performance analysis of two practical low complexity detectors/receivers keeping EE into consideration. The simulation results obtained show that EE does not monotonically increase with the number of BS antennas. On the contrary, it is a decreasing concave or quasi-concave function of the number of BS antennas depending on the detection technique used at the receiver. We also show that with decreasing spacing between the antennas, mutual coupling increases, contributing toward reduction in EE. Our analysis thus shows that EE does not increase infinitely in a massive MIMO system when the increasing number of antennas are to be accommodated within a fixed physical space and the total power consumed is considered to be a function of the antennas. Accordingly, closed-form expressions for the optimum number of antennas to attain maximum EE for zero forcing (ZF) are obtained.

Document retrieval is one of the most fundamental problem in information retrieval. The objective is to retrieve all documents from a document collection that are relevant to an input pattern. Several variations of this problem such as ranked document retrieval, document listing with two patterns and forbidden patterns have been studied. We introduce the problem of document retrieval with forbidden extensions. Let D={T_1,T_2,...,T_D} be a collection of D string documents of n characters in total, and P^+ and P^- be two query patterns, where P^+ is a proper prefix of P^-. We call P^- as the forbidden extension of the included pattern P^+. A forbidden extension query < P^+,P^- > asks to report all occ documents in D that contains P^+ as a substring, but does not contain P^- as one. A top-k forbidden extension query < P^+,P^-,k > asks to report those k documents among the occ documents that are most relevant to P^+. We present a linear index (in words) with an O(|P^-| + occ) query time for the document listing problem. For the top-k version of the problem, we achieve the following results, when the relevance of a document is based on PageRank: - an O(n) space (in words) index with O(|P^-|log sigma+ k) query time, where sigma is the size of the alphabet from which characters in D are chosen. For constant alphabets, this yields an optimal query time of O(|P^-|+ k). - for any constant epsilon > 0, a |CSA| + |CSA^*| + Dlog frac{n}{D} + O(n) bits index with O(search(P)+ k cdot tsa cdot log ^{2+epsilon} n) query time, where search(P) is the time to find the suffix range of a pattern P, tsa is the time to find suffix (or inverse suffix) array value, and |CSA^*| denotes the maximum of the space needed to store the compressed suffix array CSA of the concatenated text of all documents, or the total space needed to store the individual CSA of each document.

Orthogonal range reporting is one of the classic and most fundamental data structure problems. (2,1,1) query is a 3 dimensional query with two-sided constraint on the first dimension and one sided constraint on each of the 2nd and 3rd dimension. Given a set of N points in three dimension, a particular formulation of such a (2,1,1) query (known as four-sided range reporting in three-dimension) asks to report all those K points within a query region [a, b]X(-infinity, c]X[d, infinity). These queries have overall 4 constraints. In Word-RAM model, the best known structure capable of answering such queries with optimal query time takes O(N log^{epsilon} N) space, where epsilon>0 is any positive constant. It has been shown that any external memory structure in optimal I/Os must use Omega(N log N/ log log_B N) space (in words), where B is the block size [Arge et al., PODS 1999]. In this paper, we study a special type of (2,1,1) queries, where the query parameters a and c are the same i.e., a=c. Even though the query is still four-sided, the number of independent constraints is only three. In other words, one constraint is shared. We call this as a Shared-Constraint Range Reporting (SCRR) problem. We study this problem in both internal as well as external memory models. In RAM model where coordinates can only be compared, we achieve linear-space and O(log N+K) query time solution, matching the best-known three dimensional dominance query bound. Whereas in external memory, we present a linear space structure with O(log_B N + log log N + K/B) query I/Os. We also present an I/O-optimal (i.e., O(log_B N+K/B) I/Os) data structure which occupies O(N log log N)-word space. We achieve these results by employing a novel divide and conquer approach. SCRR finds application in database queries containing sharing among the constraints. We also show that SCRR queries naturally arise in many well known problems such as top-k color reporting, range skyline reporting and ranked document retrieval.

In this paper, we use stochastic geometry to characterize spatially distributed multi-antenna users within a cell that consists of a single multiple-input multiple-output (MIMO) base station (BS) equipped with a large antenna array. We also use large dimensional random matrix theory (RMT) to achieve deterministic approximations of the sum rate of this system. In particular, we consider the users inside the cell to follow a Poisson point process (PPP). The sum rate of this system is analyzed with respect to (i) the different number of antennas at the BS as well as (ii) the intensity of the users within the coverage area of the cell. We obtained closed-form approximations for the deterministic rate at low signal-to-noise ratio (SNR) and high SNR regimes, which have very low computational complexity. We also derive the deterministic rate corresponding to a general user who is chosen from a set of users ordered in accordance with PPP.

Massive Multiple-Input-Multiple-Output (MIMO) systems deploying a large number of antennas at the base station (BS) have been shown to produce high spectral and energy efficiencies (EE) under the assumptions of increasing BS physical space and critical antenna spacing. We propose a more realistic system model considering a fixed physical space: incorporating coupling and correlation among the BS antennas while providing a realistic analysis of the power consumption using a new power consumption model, taking into account the circuit power consumptions as a function of the number of BS antennas. We also touch on the performance analysis of two practical low complexity detectors keeping EE into consideration. The simulation results obtained show that EE does not monotonically increase with the number of BS antennas. On the contrary, it is a decreasing concave or quasi concave function of the number of BS antennas depending on the detection technique used at the receiver. Also shown is that with decreasing spacing between the antennas, mutual coupling increases contributing towards reduction in EE. Our analysis thus shows that EE does not increase in a massive MIMO system when a large number of antennas are to be accommodated within a fixed physical space and the total power consumed is considered to be a function of the antennas.