This paper investigates covert multi-hop communication in wireless networks where an adversary employs a cyclostationary (cycle) detector to reveal hidden transmissions. The covert route employs direct sequence spread spectrum (DSSS) signaling to ensure either maximum end-to-end covertness maximization or minimum latency minimization-under quality-of-service (QoS) and link budget constraints. Optimal bandwidth, transmit power, and spreading gain for each hop jointly satisfy reliability and either rate or covertness requirements. We show the equivalence between the covertness and the detection SNR gain-based widest-path formulations, and, hence, enabling efficient route computation. Numerical simulations in a realistic 3D environment illustrate that (i) end-to-end latency increases exponentially with the covertness requirement, (ii) the end-to-end latency increase is super-linear with the packet size M, and (iii) cycle and energy detectors impose different latency behavior as a function of the message length and the covertness requirement. The proposed framework provides important insights into resource allocation and routing design for covert networks against advanced detection adversaries.

State-of-the-art covert routing in heterogeneous networks (HetNets) focuses on balancing covertness and throughput, but often overlooks explicit energy optimization. While covert communication inherently limits transmit power, meeting throughput demands without coordinated design can still lead to high energy consumption. To this end, we propose DECOR, Decentralized Energy-efficient COvert Routing framework that jointly optimizes covertness, throughput, and energy efficiency. Unlike traditional methods that use a single wireless technology, DECOR leverages the diversity of available wireless communication technologies in HetNet to enable simultaneous multi-modal routing. The core idea behind DECOR is that optimal simultaneous utilization of multiple modalities improves throughput and overall energy efficiency. It minimizes the end-to-end energy consumption while satisfying stringent constraints on throughput and covertness through two core steps: (1)link-level optimizationusing sequential least squares programming (SLSQP), and (2)network-level optimizationthrough a custom cluster-based routing strategy. DECOR introduces a novel clustering-based strategy that aggregates intra-cluster link information and delegates routing decisions to cluster heads, significantly reducing control overhead and enabling scalable, energy-efficient covert communication. Extensive numerical analysis demonstrates that DECOR significantly outperforms existing approaches in terms of energy-efficiency and data overhead.

Multi-hop Heterogeneous wireless networks (HWNs) with multiple communication technologies have been extensively studied driven by the rising demand for enhanced resilience, coverage, and throughput. However, utilizing relays for wireless communication has the potential to heighten the risk of detection by an adversary. Furthermore, information about the adversary is usually unavailable in practice. Therefore, in this paper, we propose a covert routing using Q-learning for HWNs to maximize the detection error probability (DEP) with only limited knowledge about the adversary's general location, referred to as the threat region. To achieve this goal, we exploit fictitious adversaries that are randomly located inside the threat region to obtain estimated DEP. Then, we propose three different approaches to establish a route between the source and destination. Through simulations, we compare our proposed approaches to the conventional covert routing using Q-learning where the location of the adversary is assumed to be known. Our results show that our methods only experience a 10% reduction in DEP with fictitious adversaries. Furthermore, we investigate how the radius of the threat region affects the performance.

Reinforcement learning (RL) holds great promise for network control problems, yet its deployment in real-world systems remains limited due to the instability and unpredictability of RL policies during training. To address this challenge, we propose a two-phase conservative RL framework that combines domain expertise from classical network optimization with modern deep RL techniques. Our key idea is to initialize the learning process with a stable base policy, derived from expert knowledge, and then apply conservative fine-tuning under a Kullback–Leibler (KL) divergence constraint to safely explore improved behaviors. We apply this framework to the problem of covert multi-hop routing, where the objective is to optimize data throughput while minimizing detectability by adversaries. In Phase I, we construct a reliable base policy by imitating the back-pressure algorithm, which guarantees throughput-optimal behavior and stable queue dynamics. Phase II fine-tunes this policy to improve covert performance, as measured by the Detection Error Probability (DEP), while preserving training-time stability. Empirical evaluations on a grid network show that our method enables more reliable learning than pure RL. While pure RL (e.g., PPO) can sometimes achieve higher covert performance, it frequently suffers from large queues and collapsed throughput during training. In our experiments, our conservative RL framework reduces the worst-case training-time queue length by over 99% while maintaining comparable covert communication performance.

We consider a three party wireless network- Alice is a legitimate transmitter, Bob is a legitimate receiver, and Willie is a malicious adversary. Alice's goal is to communicate with Bob with a certain minimum Quality of Service (QoS) while evading detection from Willie. Alice transmits a Direct Sequence Spread Spectrum (DSSS) signal where the processing gain and transmit power are selected with the aim of evading detection by spreading the bit energy over a larger bandwidth while maintaining required QoS. Concurrently, Willie tries to detect the legitimate transmission by using either an energy or a cycle detector. We study this scenario within an adversarial optimization framework that aims at achieving a robust max-min covertness under link performance constraints. The framework is common to both detectors and hence allows for a comparative performance analysis that identifies common key performance parameters. The adversarial signal-to-noise ratio (SNR) detection gain enables direct trading of the DSSS processing gain for the (squared) channel quality ratio when aiming to improve link covertness, regardless of which detector Willie uses. The DSSS processing gain and SNR gain (and, hence, covertness) are limited by the bit rate and bit error rate link requirements, respectively. While both detectors' performance is limited by the SNR at Willie, the cycle detectors benefit significantly more from longer observation time of the legitimate transmission.

A fundamental challenge in covert routing is that meeting both covertness and throughput requirements often leads to increased transmit power, which can significantly elevate the overall energy consumption of the network. Therefore, it is important to achieve higher throughput and better energy efficiency while maintaining the required covertness. To this end, we propose a novel simultaneous multi-modal Decentralized Energy- Efficient covert Routing approach - DEER for a multi-hop heterogeneous network (HetNet). Unlike the prevailing single-modal approaches, DEER leverages the diversity of the available wireless communication technologies for simultaneous multi-modal routing. DEER aims to minimize the end-to-end total transmit power of the whole route in a decentralized fashion while maintaining the constraints on required throughput and covertness. DEER stems into two main steps: node-level optimization followed by network-level optimization using the proposed custom-tailored Dijkstra's based link state routing protocol to meet the constraints while minimizing the end-to-end total transmit power. We demonstrate by numerical analysis that DEER improves the energy efficiency by$23.5 x$and$2.9 x$times in comparison to the baseline single-modal and naive simultaneous multimodal approaches respectively.

In this paper, we study covert communication in wireless networks consisting of a transmitter, a receiver, an adversary, and multiple randomly distributed interferers. We model the spatial distribution of the interferers as a ß -Ginibre point process which can consider a repulsion among the interferers' locations and simplifies to the Poisson point process for a particular case. Both the average detection error probability (DEP) at the adversary and the communication outage probability (COP) at the receiver are investigated. We derive approximations of the average DEP and COP, and validate that the approximations are tight when compared to the simulated results. In addition, we examine the impact of the repulsion among the interferers on both the average DEP and COP. Finally, we show that the achievable average DEP, which is the maximized DEP with a requirement on the COP, is enhanced when there exists repulsion among the positions of the interferers.

In this paper, we develop an approach to exploit multiple disparate wireless communication technologies simultaneously to enhance covertness of a communication link. Specifically, given two available communication modalities between a pair of friendly nodes (Alice and Bob), the goal is to evade detection by an adversary (Willie) who is equipped with a radiometer covering the frequency bands of both modalities. We propose a joint detection threshold optimization technique from Willie's point of view. We also develop a joint transmit power optimization strategy for Alice to maximize covertness while meeting the throughput requirement at Bob. Through numerical simulations we show that the proposed scheme matches the performance of exhaustive search method while reducing the computational time by 98% and also improves the covertness by 56% compared to a naïve benchmark scheme.

In this paper, we explore covert routing communication in a heterogeneous network where a source sends a confidential message to a destination node with the help of relaying nodes where each node adaptively selects one modality among multiple communication modalities based on the wireless environment. We study three optimization problems: 1) the maximization of the end-to-end detection error probability at an adversary with a requirement on the throughput; 2) the end-to-end throughput maximization under a covertness constraint; and 3) the end-to-end latency minimization with a covertness condition. For the three optimization problems, we develop novel algorithms that identify the routes from a source to a destination and allocate resources, which are communication modality, transmit power, and bandwidth, at all nodes along the route. First, for single-hop communications, we derive a closed-form joint optimal power and bandwidth solution for a given modality, and then provide a modality selection method. For multi-hop communications, we propose the optimal routing strategies for the three problems by modeling the network as graphs and defining edge weights based on the objectives of the problems. From numerical simulations, it is validated that the performance of the network can be enhanced with the proposed optimal joint route and resource allocation techniques by judiciously selecting one of the multiple modalities for each hop in the route based on the wireless environment.

In this paper, we explore covert communication in a heterogeneous network where a transmitter sends a confidential message to a receiver by utilizing two different radio frequency (RF) bands, low-very high frequency (low-VHF) and microwave frequency modalities. Since two RF modalities exhibit different characteristics in terms of channels and available bandwidths, it is important to efficiently leverage the modalities based on the wireless environment. Therefore, we develop a new algorithm that optimizes the transmit powers and bandwidths for the modalities and selects one modality with the goal of maximizing the detection error probability at a warden while guaranteeing a quality-of-service requirement of the receiver. We first derive a closed-form joint optimal power and bandwidth solution for a given modality, and then provide a modality selection method. From numerical simulations, it is validated that the proposed scheme achieves the optimal performance and the covertness can be enhanced by judiciously choosing one of the two modalities based on the channel condition.

In this paper, we investigate the problem of distributed coherent beamforming in wireless networks where multiple distributed transmitters adjust the phases of their signals to form a directional and targeted communication link to a client receiver. The quality-of-service (QoS) and security are key components of robust and covert wireless networks. Although the security can be improved by exploiting information about potential adversaries, such information may not be available in practical networks since the adversaries are often passive. Therefore, we introduce transmission strategies which not only send a confidential message by forming a beam towards the client but also broadcast interference with the aim of obfuscating adversaries without having any information about them. Two different client feedback scenarios are considered, two-bit and rich feedback. The proposed algorithms can be performed in a fully distributed manner without any knowledge about potential adversaries. Numerical simulations validate the effectiveness of the proposed schemes.

In this paper, we investigate a wireless network where multiple distributed transmitters adjust the phases of their signals so that they can be constructively added at an intended receiver (client). Unlike conventional beamforming with co-located and phase-synchronized antennas, geographically separated transmitters may have phase offsets induced by individual local carrier oscillators, that pose a challenge for coherent distributed beamforming. This is especially true for transmitters that are far apart, when distributed clock synchronization protocols may be more difficult to implement. There may also be a desired spatial repulsion among the positions of the transmitters in order to mitigate mutual coupling effects and extend the coverage region. In this regard, we analyze the performance of distributed beamforming with phase offsets by modeling the spatial distribution of the transmitters as a $\beta$-Ginibre point process that models the repulsive behavior. We consider two transmission strategies: (i) Transmitter selection in which the client chooses the transmitter providing the highest received power at the client, and (ii) Coherent beamforming in which multiple transmitters simultaneously send their signals to the client. From numerical simulations, we examine the impact of the phase offsets on the performance and confirm the accuracy of our analysis. It is shown that even with significant phase offset errors, employing coherent beamforming can be an effective strategy.

Link instability induced by users' mobility is one of the challenges of optical wireless communications (OWC) inherited from the propagation nature of light. Hence, good understanding of the optical channel characteristics in dynamic environments plays a vital role in developing robust resource management strategies in OWC networks. Unfortunately, it is quite difficult to collect accurate indoor optical channel data in dynamic environments. In addition, indoor trajectory dataset is not publicly available. To overcome such limitations, this paper proposes a mobile terminal-centric analytical framework that captures the propagation channel characteristics in a mobile OWC network whose downlink is based on visible light and uplink is based on infrared light. We abstract the nature of human behavior by integrating both macro and micro mobility patterns. These patterns are then used to realize the spatio-temporal characteristics of optical wireless channels under long-term environment-confined mobility. The statistics derived from the developed framework indicate that the mobile line-of-sight (LOS) channel gain follows space-time-dependent multiple-peak Nakagami distributions, whereas the non-line-of-sight (NLOS) channel gain adheres to various space-time-dependent single peak distributions under different indoor layouts. The overall distribution of NLOS bandwidth follows space-time-dependent multiple-peak log-logistic distributions in downlinks and space-time-dependent generalized log-logistic distributions in uplinks. Our investigation demonstrates that the indoor layout and the user's environment-confined mobility pattern significantly impact the LOS dynamics but present limited impact on NLOS components. Motivated by the need for better channel models for mobile OWC, the proposed framework fills up an important gap in literature and help the research community to understand better the indoor optical wireless channel characteristics.

This paper focuses on the development of energy efficient visible light communication (VLC) networks. More specifically, since the quality-of-service and energy cost are key parameters in designing energy efficient networks, this paper optimizes the VLC base station (BS) density to minimize the area power consumption (APC) under an outage probability constraint. Using stochastic geometry, approximations of the outage probability of VLC networks are first introduced. The derived approximations are applicable to an arbitrary field-of-view at photodiodes and present low computational complexities. Leveraging the derived analytical results, a low complexity algorithm to find the VLC BS density that minimizes the APC of VLC networks is then proposed. The numerical simulations corroborate the tightness of the approximations on the outage probability and confirm that the proposed algorithm exhibits almost identical performance as the algorithm that exhaustively searches the optimal BS density.

In this paper, we focus on the problem of distributed coherent beamforming in wireless networks where multiple distributed transmitters adjust the phases of their signals to form a directional and targeted communication link to a client receiver. The quality-of-service (QoS) and security are key aspects of robust and covert wireless networks. Although the security can be enhanced by exploiting information about the locations of adversaries, such information may not be available in practical networks since the adversaries are often passive. Therefore, we propose transmission strategies which divide transmitters into two groups where one group forms a beam towards the client and the other group broadcasts interference in order to obfuscate adversaries. As the interference may degrade the QoS of the client, the latter group steers a null to the client to alleviate the interference at the client. The proposed scheme can be performed in a fully distributed manner with only two bits of feedback information from the client and without any knowledge about the locations of potential adversaries.

This paper focuses on the development of energy efficient hybrid networks consisting of radio frequency (RF) base stations (BSs) and visible light communication (VLC) BSs. More specifically, since the quality-of-service and energy cost are key parameters in designing energy efficient networks, this paper optimizes the RF BS and VLC BS intensities to minimize the area power consumption (APC) under an outage probability constraint. Using stochastic geometry, approximations of the outage probability of VLC networks, which are applicable to an arbitrary field-of-view at photodiodes and present low computational complexities, are first introduced. Leveraging the derived analytical results, a low complexity algorithm to find the VLC BS intensity that minimizes the APC of VLC networks is then proposed. Furthermore, algorithms to identify the intensities of RF BSs and VLC BSs for energy efficient hybrid RF/VLC networks via one-dimensional search methods are also developed. The numerical simulations corroborate the tightness of the approximations on the outage probability and confirm that the proposed algorithms exhibit almost identical performances as the algorithms that exhaustively search the optimal BS intensities. Finally, it is shown that the hybrid RF/VLC networks achieve a lower outage probability with a reduced APC compared to the RF-only networks and VLC-only networks.

In this paper, we consider wireless caching helper networks (WCHNs) consisting of cache-enabled device-to-device (D2D) transmitters and caching helpers (CHs), which deliver data by exploiting cached contents. We consider two types of modes at a typical user, namely D2D and CH modes. In the D2D and CH modes, after requesting a content, the user receives the content from a D2D transmitter and a CH caching the content, respectively. In practical scenarios, to mitigate interference, the CHs may not be placed close to each other, and thus there exists a form of repulsion among the CHs' locations. In this context, we model the spatial distribution of the CHs as a β-Ginibre point processe, which reflects the repulsive behavior and contains the Poisson point process as a special case. Then, we provide analytical expressions for the coverage probabilities in the WCHNs.

In this paper, we consider fog radio access networks (F-RANs) consisting of cache-enabled device-to-device (D2D) transmitters and fog access points (F-APs), which deliver data by exploiting cached contents or leveraging cloud processing. We consider three types of modes at a typical user, namely, D2D, F-AP, and cooperative modes. In the D2D and the F-AP modes, when the user requests content, the user receives the content from a D2D transmitter and an F-AP caching the content, respectively. In the cooperative mode, F-APs located near the user send data aided by a centralized cloud processing unit. We also examine a mode selection algorithm in which the user adaptively selects one of the three modes. In practical scenarios, to mitigate interference, the transmitters may not be placed close to each other, and thus, there may exist a form of repulsion among the transmitters' locations. In this context, we model the spatial distributions of the D2D transmitters and the F-APs as $\beta $ -Ginibre point processes, which reflect the repulsive behavior and contain the Poisson point process as a special case. Then, we provide analytical expressions for the coverage probabilities in the F-RANs. Our results are corroborated by Monte Carlo simulations.

In this paper, we investigate wireless networks consisting of a legitimate transmitter (Alice), a legitimate receiver (Bob), eavesdroppers (Eves), and friendly jammers. Two network scenarios are considered depending on whether Alice and the jammers have the ability to detect the existence of Eves in their vicinity. If they do not have the ability, as a means to enhance the secrecy, Alice transmits artificial noise and each jammer selectively radiates a jamming signal based on the channel gain between the jammer and Bob. On the other hand, when they have the ability, Alice sends a confidential message to Bob if no Eve is detected within its guard zone, and the jammers transmit jamming signals when there exists at least one Eve in their vicinity. We model the spatial distributions of Eves and jammers as $\beta $ -Ginibre point processes, which can characterize repulsion among the nodes and include the Poisson point process (PPP) as a special case. Then, we analyze both the probability that Bob successfully decodes the confidential message and the probability that the message is secure against eavesdropping. Also, we show that our analysis is a generalization of previous works on the networks with PPPs by recovering them from our analytical results.

Due to its mathematical tractability, the homogeneous Poisson point process (PPP) has been employed to model wireless networks and analyze their performance. The PPP has the fundamental property that in a network with n nodes, the n nodes are distributed independently from each other. As such the PPP is not a suitable model for many networks where there exists a repulsion among the nodes. In order to address this limitation, in this paper we model the spatial distribution of transmitters in wireless networks as a Poisson hard-core process (PHCP) in which no two nodes can be closer to each other than a given repulsion radius from one another. We first provide an exact expression of the coverage probability of the networks and then introduce the method to efficiently evaluate the derived expression. Additionally, we derive approximations of the coverage probability which have low computational complexities. The accuracy and efficiency of our analytical results are validated by our simulations.

In this paper, we analyze the performance of wireless sensor networks using stochastic geometry. In practical networks, since sensor nodes in the networks are not independently placed, there exists a correlation among the locations of the nodes. In order to capture the effect of the correlation, we model the spatial distribution of nodes as α-Ginibre point processes (GPPs) which reflect the repulsion. It is assumed that each sensor node is associated with the closest gateway and employs a channel inversion power control which adjusts transmit power based on the contact distance. We first identify the characteristics of the contact distance and transmit power, and then investigate the outage performance of the networks using the derived characteristics. Since the α-GPP contains the Poisson point process (PPP) as a particular case, our analysis can be interpreted as a generalization of previous works on the networks modeled by PPPs. The accuracy of our analysis is validated through simulation results.

Wireless radio frequency energy harvesting has been adopted in wireless networks as a method to supply energy to wireless nodes. In this paper, we analyze a wireless energy harvesting network based on a Boolean-Poisson model. This model assumes that energy sources are distributed according to a Poisson point process and have disc-shaped coverage regions with random radii. We introduce a distribution for the coverage radii, which takes aggregated harvested power into account. The union of the coverage regions of the energy sources forms the energy harvesting zone. We derive the transmission success probability of single-hop networks characterized by the probability that two sensor nodes are located in the energy harvesting zone. Then, we analyze the performance of multi-hop networks in the cases, where the locations of the sensor nodes are either fixed or randomly distributed. Moreover, we consider a star-shaped topology, which reflects the scenario wherein some sensor nodes simultaneously transmit data to a data collector. In this setting, we derive an approximation of the average throughput at the data collector. Numerical results validate the accuracy of our analysis in the single-hop and multi-hop networks and confirm the tightness of our approximation in the case of the star-shaped topology.

The Poisson point process (PPP) has been widely employed to model wireless networks and analyze their performance. The PPP has the property that nodes are conditionally independent from each other. As such, it may not be a suitable model for many networks, where there exists repulsion among the nodes. In order to address this limitation, we adopt a Poisson hardcore process (PHCP), in which no two nodes can be closer than a repulsion radius from one another. We consider two-tier heterogeneous networks, where the spatial distributions of transmitters in the first-tier and the second-tier networks follow a PHCP and a PPP, respectively. To alleviate inter-tier interference, we consider a guard zone for the first-tier network and presume that the second-tier transmitters located in the zone are deactivated. Under this setup, the activated second-tier transmitters form a Poisson hard-core hole process. We first derive exact computable expressions of the coverage probability and introduce a method to efficiently evaluate the expressions. Then, we provide approximations of the coverage probability, which have lower computational complexities. In addition, as a special case, we investigate the coverage probability of single-tier networks by modeling the locations of transmitters as a PHCP.

In this paper, we provide minimum mean-squared error-based source-relay-destination transceiver designs for multiple-input multiple-output amplify-and-forward relaying systems, where direct link between the source and the destination is non-negligible. In an earlier work, a local optimal technique was introduced which employs a projected gradient method and an interior point method. Since these methods may have quite high computational complexity, we investigate a new local optimal solution for the source-relay-destination transceiver which has low complexity. To this end, we first introduce the optimal closed-form solution for the relay transceiver for given source and destination filters. Then, for given relay and destination transceivers, the optimal source precoder design is derived, which requires only 1-D bisection search. Based on these solutions, we propose a joint optimization algorithm which iteratively finds a local optimal solution. Also, we introduce a simple non-iterative algorithm which computes filters in closed-forms with low complexity. Furthermore, since perfect channel knowledge may not be feasible in practical systems, a joint transceiver technique which is robust to channel uncertainties is provided. It is confirmed by simulation results that the proposed schemes outperform conventional techniques with significantly reduced complexity.

In this paper, we analyze the performance of wireless sensor networks using stochastic geometry. In practical networks, since nodes in the networks are not independently placed, there exists a correlation among the locations of the nodes. In order to capture the effect of the correlation, we model the spatial distribution of the nodes as α-Ginibre point processes (GPPs), which reflect the repulsion. It is assumed that each sensor node is associated with the closest gateway and employs a fractional channel inversion power control, which adjusts transmit power based on the contact distance. We first identify the characteristics of the contact distance and transmit power, and then investigate the outage performance of the networks using the derived characteristics. We also examine energy harvesting networks where each sensor harvests energy from radio frequency signals radiated by energy sources and transmits data to its serving gateway when the harvested energy is enough to conduct the fractional channel inversion power control. Since the α-GPP contains the Poisson point process (PPP) as a particular case, our analysis can be interpreted as a generalization of previous works on the networks modeled by PPPs. The accuracy of our analysis is validated through simulation results.

Wireless radio frequency (RF) energy harvesting has been adopted in wireless networks as a method to supply energy to wireless nodes, e.g., sensors. In this paper, we present a new analysis of the wireless energy harvesting network based on a Boolean-Poisson model. This model considers that the energy sources have a fixed coverage range. The energy sources are distributed according to a Poisson point process (PPP) while their radii of coverage are random and are assumed to follow a given probability distribution. We derive the performance measures consisting of the energy harvesting probability and the transmission success probability both in the cases of two nodes and multiple nodes. Our analysis is validated by simulation.

In this paper, we propose a new user selection algorithm for full-duplex (FD) multiuser multiple-input single-output (MU-MISO) systems where a FD base station (BS) communicates with multiple half-duplex (HD) users in both downlink and uplink channels simultaneously. Due to self-interference at the BS and co-channel interference among users, a joint downlink and uplink user selection to maximize system performance incurs high search complexity. To reduce the complexity, we introduce a two step user selection algorithm which successively chooses downlink users followed by uplink users based on the decomposed sum rate of the FD systems. From the numerical results, we confirm that the proposed user selection algorithm for the FD MU systems exhibits a small performance loss compared to the optimal user selection algorithm with much reduced complexity.

In simultaneous wireless information and power transfer (SWIPT) systems, dedicated energy signals only convey wireless energy, but not information. For this reason, the energy-carring signals in the SWIPT can be pre- determined in advance and is shared among communication nodes. By exploiting this nature, this paper designs the optimal transmit beamforming vectors for the multiple-input single-output SWIPT interference channel with signal cooperation (IFC-SC), where the energy- carrying signal waveforms are known to transmitters and receivers. Specifically, we aim to identify the optimal tradeoff between the information rate and the harvested energy. To this end, an information rate maximization problem is formulated under minimum required harvested energy constraint, which is non-convex in general. To solve the problem, a new parameterization technique is introduced, and we can decouple the original problem into two subproblems, which yields closed-form beamforming solutions by addressing the line search method for the parameter. Simulation results confirms that the proposed optimal IFC-SC beamforming vectors outperform conventional SWIPT IFC systems.

Ambient radio frequency (RF) energy harvesting methods have drawn significant interests due to their ability to provide energy to wireless devices from ambient RF sources. This paper considers ambient RF energy harvesting wireless sensor networks where a sensor node transmits data to a data sink using the energy harvested from the signals transmitted by the ambient RF sources. We analyze the performance of the network, i.e., the mean of the harvested energy, the power outage probability, and the transmission outage probability. In many practical networks, the locations of the ambient RF sources are spatially correlated and the ambient sources exhibit repulsive behaviors. Therefore, we model the spatial distribution of the ambient sources as an α-Ginibre point process (α-GPP), which reflects the repulsion among the RF sources and includes the Poisson point process as a special case. We also assume that the fading channel is Nakagami-m distributed, which also includes Rayleigh fading as a particular case. In this paper, by exploiting the Laplace transform of the α-GPP, we introduce semi-closed-form expressions for the considered performance metrics and provide an upper bound of the power outage probability. The derived expressions are expressed in terms of the Fredholm determinant, which can be computed numerically. In order to reduce the complexity in computing the Fredholm determinant, we provide a simple closed-form expression for the Fredholm determinant, which allows us to evaluate the Fredholm determinant much more efficiently. The accuracy of our analytical results is validated through simulation results.

In this paper, we propose a new user selection algorithm for full-duplex (FD) multiuser multiple-input single-output (MU-MISO) systems, where an FD base station (BS) communicates with multiple half-duplex users in both downlink and uplink channels simultaneously. Due to self-interference at the BS and co-channel interference among users, a joint downlink and uplink user selection to maximize system performance incurs high search complexity. To reduce the complexity, we introduce a two-step user selection algorithm, which successively chooses downlink users followed by uplink users based on the decomposed sum rate of the FD systems. In addition, we analyze the average sum rate performance of our proposed user selection algorithm for FD MU-MISO systems and derive a tight approximation of the performance. From the numerical results, we confirm that our analysis matches well with simulation results, and the proposed user selection algorithm for the FD MU-MISO systems exhibits a small performance loss compared with the optimal user selection algorithm with much reduced complexity.

In this paper, we investigate wireless powered communication network for OFDM systems, where a hybrid access point (H-AP) broadcasts energy signals to users in the downlink, and the users transmit information signals to the H-AP in the uplink based on orthogonal frequency division multiple access. We consider a full-duplex H-AP which simultaneously transmits energy signals and receives information signals. In this scenario, we address a joint subcarrier scheduling and power allocation problem to maximize the sum-rate under two cases: perfect self-interference cancellation (SIC) where the H-AP fully eliminates its self-interference (SI) and imperfect SIC where residual SI exists. In general, the problems for both cases are nonconvex due to the subcarrier scheduling, and thus it requires an exhaustive search method, which is prohibitively complicated to obtain an optimal solution. In order to reduce the complexity, for the perfect SIC scenario, we jointly optimize subcarrier scheduling and power allocation by applying the Lagrange duality method. Next, for the imperfect SIC case, the problem becomes more complicated due to the SI at the H-AP. To solve this problem, we propose an iterative algorithm based on the projected gradient method. Simulation results show that the proposed algorithm for the case of perfect SIC exhibits almost the same sum-rate performance compared to the optimal algorithm, and the proposed iterative algorithm for the imperfect SIC case offers a significant performance gain over conventional schemes.

In this paper, we investigate wireless powered communication network for OFDM systems, where a hybrid access point (H-AP) broadcasts energy signals to users in the downlink, and the users transmit information signals to the H-AP in the uplink based on an orthogonal frequency division multiple access scheme. We consider a full-duplex H-AP which simultaneously transmits energy signals and receives information signals, and a perfect self-interference cancellation where the H-AP fully eliminates its self interference. In this scenario, we address a joint subcarrier scheduling and power allocation problem to maximize the sum-rate. In general, the problem is on-convex due to subcarrier scheduling, and thus it requires an exhaustive search method, which is prohibitively complicated to obtain the globally optimal solution. In order to reduce the complexity, we jointly optimize subcarrier scheduling and power allocation by applying the Lagrange duality method. Simulation results show that the proposed algorithm exhibits only negligible sum-rate performance loss compared to the optimal exhaustive search algorithm and a significant performance gain over conventional scheme.

This paper investigates simultaneous wireless information and power transfer in multiple-input single-output interference channels, and designs transmit beamforming vectors which achieves the optimal tradeoff between the information rate and the harvested energy. To this end, the problem for maximizing the information rate is formulated with minimum required harvested energy constraint. In order to solve this nonconvex problem, we introduce parameterization techniques for characterizing the achievable rate- energy (R-E) region. As a result, the original problem is separated into two subproblems, for which closed- form solutions are obtained by addressing the line search method. Finally, we provide numerical examples for the achievable R-E region through simulations.

In this paper, we consider K-user multiple-input single-output interference channels with a cognitive relay. Assuming that data of all transmitters and channel state information are known at the cognitive relay, we design a linear precoder for the cognitive relay with the aim of maximizing the sum-rate without changing the transmitter operations at all transmitters. We first define the receiver set as a set which contains a part of receivers, and then present a performance metric called "partial signal-to- interference-plus-noise ratio" (PSINR) based on the receiver set. Then, we can obtain a precoder at the cognitive relay by solving the PSINR maximization problem. The optimal receiver set which yields the maximum sum-rate can be identified by checking all possible receiver sets. Since this exhaustive search has prohibitive complexity, we introduce a greedy set search method and finally propose a precoder design scheme by combining the PSINR maximization problem and the greedy set search method. Numerical simulation results confirm that the proposed scheme shows performance close to the projected gradient method with reduced complexity.

In this paper, we study a transmit beamforming technique for multiple input single output downlink multi-user systems with directional antennas where a transmit antenna gain is determined in three dimensional coordinates. For a multiuser active antenna system, beamforming designs to maximize the weighted sum rate are proposed by optimizing the transmit antenna gain and power allocation. Since finding joint optimal parameters requires prohibitively high computational complexity, we separate the optimization problem into two sub-problems of the vertical beamforming and the power allocation. Then a simple vertical beamforming algorithm based on a high signalto- noise ratio assumption is presented. Also, for a multi-user passive antenna system, we provide a beamforming scheme which employs a multi-sector concept. Simulation results show that the proposed beamforming schemes outperform the conventional beamforming schemes.

In this paper, we provide new precoding schemes which jointly optimize the source and relay precoders in multiple-input multiple-output amplify-and-forward relaying systems with minimum mean-squared error decision feedback equalizer (DFE) at the destination node. Instead of conventional schemes which resort to an iterative method, we propose simple precoding schemes based on a closed-form solution. To this end, we first extend the decomposable property of the error covariance matrix for a linear receiver to relaying systems with DFE receivers. Then, we suggest two closed-form solutions which successively identify the source and the relay precoders. To improve the performance, a mode selection which adaptively chooses one of the two closed-form precoding schemes according to the channel conditions is introduced. Then, we propose a simple eigenvalue based mode selection algorithm, and analyze its selection probability behavior for Rayleigh fading channels. Simulation results demonstrate that the performance of the proposed method is almost identical to the iterative solution with much reduced complexity.

In this paper, we propose new relay transceiver designs based on the minimum mean square error (MMSE) criterion for amplify-and-forward multiple-input-multiple-output (MIMO) relaying systems with direct link. Since each antenna element is equipped with its own power amplifier, a norm power constraint, which restricts the transmit power with the expected norm of the transmit signal vector, is not suitable for practical systems. Therefore, we consider a shaping constraint (SC), which imposes a limit on the shape of the transmit covariance matrix. The SC includes several power constraints such as the peak power constraint and the per-antenna power constraint as special cases. To this end, we first derive the optimal structure of the MMSE relay transceiver under the SC. Then, by introducing an upper bound of the mean square error, we provide closed-form relay transceiver solutions. Due to limited bandwidth of the feedback channel, perfect channel knowledge at the transmitter may not be feasible. Thus, we also propose a quantized relay transceiver design based on Grassmannian codebooks for a limited-feedback scenario. From simulation results, it is confirmed that the proposed relay transceiver techniques demonstrate a significant performance improvement compared with conventional schemes.

This paper investigates the optimal transmit beamforming designs for simultaneous wireless information and power transfer (SWIPT) in multiple-input single-output interference channels (IFC). Based on cooperation level among transmitters and receivsers, we classify the SWIPT IFC systems into two categories. First, we consider the IFC with partial cooperation, where only channel state information (CSI) is available at transmitters and receivers, but not the signal waveform. Second, we examine the IFC with signal cooperation, where both the CSI and the signal waveforms are known to transmitters and receivers. Then, for the both scenarios, we identify the Pareto boundary of the achievable rate-energy (R-E) region which characterizes the optimal tradeoff between the information rate and the harvested energy. To this end, the problems for maximizing the information rate are formulated with minimum required harvested energy constraint. To solve these non-convex problems, we introduce parameterization techniques for characterizing the R-E region. As a result, the original problem is separated into two subproblems, for which closed-form solutions are obtained by addressing the line search method. Finally, we provide numerical examples for the Pareto boundary of the R-E region through simulations.

In this paper, we consider K-user multiple-input single-output interference channels with a cognitive relay. Assuming that data of all transmitters and channel state information are known at the cognitive relay, we design a linear precoder for the cognitive relay with the aim of maximizing the sum-rate without changing the transmitter operations at all transmitters. We first define the receiver set as a set which contains a part of the receivers, and then introduce a performance metric called “partial signal-to-interference-plus-noise ratio” (PSINR) based on the receiver set. Then, we can obtain a precoder at the cognitive relay by solving the PSINR maximization problem. The optimal receiver set which yields the maximum sum-rate can be identified by checking all possible receiver sets. Since this exhaustive search has prohibitive complexity, we develop a low complexity set search method by utilizing the properties of the optimal receiver set. Combining the PSINR maximization problem and the low complexity search method, we finally propose a precoder design scheme for the sum-rate maximization. Numerical simulation results confirm that the proposed scheme shows performance close to the projected gradient method with much reduced complexity.

In this paper, we propose a new relay transceiver design based on the minimum mean-squared error criterion for non-regenerative multiple-input multiple-output relaying systems with direct link. Since norm power constraint at the relay node does not restrict the output power at each antenna element in designing the relay transceiver, we consider maximum eigenvalue constraint (MVC) which imposes a limit on the peak power of the output. To this end, we first derive the optimal structure of the relay transceiver under the MVC. Then, by introducing an upper bound of the mean-squared error which makes the problem tractable, we provide a closed-form relay transceiver design with the MVC. From simulation results, it is confirmed that the proposed relay transceiver technique demonstrates a significant performance improvement compared to conventional schemes.

In this paper, we propose a new beamforming technique that maximizes the end-to-end signal-to-noise ratio (SNR) for amplify-and-forward multiple-input-multiple-output cooperative relaying systems with direct link between thesource and the destination. Instead of conventional schemes resorting to an iterative method, such as a gradient ascentalgorithm, our scheme provides a simple closed-form solution for source-relay joint beamformer designs. To this end, wefirst derive a new expression of the end-to-end SNR for the cooperative relaying systems and its lower bound, which isgiven as the harmonic mean of two individual SNRs. Then, a new beamforming scheme, which adaptively optimizes one of the two SNRs depending on the channel condition, is proposed. In addition, we perform a diversity order analysis of the proposed scheme and show that our scheme achieves a full diversity order of relaying systems with direct link. It is confirmed by simulation results that the proposed technique obtains almost identical performance to the gradient ascent algorithm with much reduced complexity, and our analytical work accurately predicts the numerical results.

In this paper, we study a vertical beamforming technique for multiple-input multiple-output downlink multi-user systems. In general, the transmit antenna gain is controlled by adjusting the boresight of antennas in directional antennas, and thus the cell average rate varies according to the angle of the boresight. First, we compute the tilting angles for directional antenna systems which maximize the cell average rate. To this end, the probability density function of a three-dimensional user distribution is derived. Based on the result, we analyze the average rate gain of active antenna systems over passive antenna systems for a single user case. Furthermore, for a multi-user active antenna system, beamforming designs to maximize the weighted sum rate are proposed by optimizing the transmit antenna gain and power allocation. Since finding joint optimal parameters requires prohibitively high computational complexity, we separate the optimization problem into two sub-problems of the vertical beamforming and the power allocation. Then a simple vertical beamforming algorithm based on a high signal-to-noise ratio assumption is presented. Also, for a multi-user passive antenna system, we provide a beamforming scheme based on a multi-sector concept. Simulation results show that the proposed beamforming schemes outperform the conventional beamforming schemes.

In this paper, we propose a new distributed approach for designing the beamforming vectors based on virtual signal-to-interference-plus-noise ratio (VSINR) for weighted sum-rate (WSR) maximization in multiple-input single-output interference channels. Recently, it was shown that by adaptively adjusting parameters which control the leakage interference according to channel realizations and the signal-to-noise ratio (SNR) level, the WSR performance can be improved compared to conventional methods with fixed parameters. However, due to an iterative procedure for each channel realization, this approach requires high computational complexity. To overcome this problem, by utilizing asymptotic results from random matrix theory, we propose a new low-complexity beamforming scheme with constant parameters which depend only on the channel statistics and SNR. Numerical results confirm that the proposed scheme provides the near-optimal WSR performance with much reduced system complexity.

In this paper, we study a transmit beamforming technique for multiple input single output downlink single-user systems with three dimensional antennas where a transmit antenna gain is determined in three dimensional coordinates. In general, the transmit antenna gain is controlled by adjusting the boresight of antennas in directional antennas. To derive the optimal tilting angles for the directional antenna systems, we provide the probability density functions (PDF) of the three dimensional user distribution. Furthermore, based on the PDF, the analysis for the average rates of passive and active antenna systems is presented. Simulation results verify the accuracy of the performance analysis.

In this paper, we propose a new beamforming technique for signal-to-noise ratio maximization in nonregenerative multiple-input multiple-output cooperative relaying systems where a non-negligible direct link between the source and the destination exists. Instead of conventional optimal schemes resorting to an iterative method such as a gradient ascent algorithm, we provide a simple closed form solution for source-relay joint beamformer designs. To this end, we apply the error decomposition property which allows us to separate the problem into two phases. Then, we propose a new beamforming scheme which adaptively aligns the transmit signal to each phase depending on the channel condition. From simulation results, we confirm that the proposed technique obtains the near-optimal performance.

In this paper, we analyze the average sum rate of downlink multi-antenna systems with zero-forcing beamforming (ZFBF). In practical implementations, each antenna is equipped with its own power amplifier and is limited individually by linearity of the amplifier. Thus, this paper adopts a more realistic per-antenna power constraint instead of conventional sum-power constraint on transmit antennas. To this end, we first show that a distribution of the received signal-to-noise ratio (SNR) of the ZFBF scheme with per-antenna power constraint and equal-rate metric can be approximated as a minimum of chi-square random variables. Based on this result, we present an accurate formula of the average sum rate in a closed form. Furthermore, employing extreme value theory, an expression of the asymptotic average sum rate with large numbers of transmit antennas and users is derived from the limiting distribution of the received SNR. Simulation results verify the validity of our analysis even with not so large numbers of transmit antennas and users.

In this paper, we consider weighted sum-rate (WSR) maximization problems in multiple-input single-output (MISO) interference channels (IFC) and interfering broadcast channels (IFBC). Most of existing techniques have tried to improve the WSR performance by utilizing instantaneous channel state information. However, since these methods in general should be carried out for each channel realization, they require high computational complexity, which may not be suitable for practical systems. To overcome this issue, we propose a new low complexity beamforming scheme for IFC based on virtual signal-to-interference-plus-noise ratio with constant parameters which depend only on the long-term channel statistics. In our approach, to obtain the constant parameters, the asymptotic values of the leakage coefficients which control the interference signal power are derived by employing asymptotic results from random matrix theory. Moreover, based on the results in MISO IFC, we extend the algorithm to the MISO IFBC case by applying a power allocation algorithm. Numerical results confirm that the proposed schemes provide the near-optimal WSR performance with much reduced system complexity.

In this paper, we investigate the outage and ergodic capacity of downlink distributed antenna systems (DAS) where each distributed antenna unit (DAU) has multiple antennas with per-DAU power constraint. We first derive the optimal beamforming vector in a closed form by applying a matrix minor condition to relax the positive semi-definite constraint. We observe that our derived solution has a form of maximum ratio transmission per each DAU with full power. Based on the derived optimal beamforming, the outage and ergodic capacity under Rayleigh fading channels are analyzed. To this end, we show that a distribution of the received signal-to-noise ratio is characterized as a Gamma distribution by approximating a sum of non-identical independent Nakagami-m random variables as a single Nakagami-m random variable based on the moment matching method. Then, we present an accurate formula of the outage and ergodic capacity in a closed form which matches well with the simulation results. Furthermore, we derive an upper bound of an achievable average rate of DAS with limited feedback. We then propose a new feedback bit allocation algorithm to maximize the derived metric. Simulation results confirm the accuracy of the derived outage and ergodic capacity expressions and the efficiency of the proposed bit allocation method.

In this paper, we propose new beamforming techniques based on virtual signal-to-interference-plus-noise ratio (VSINR) for weighted sum-rate (WSR) maximization in coordinated multi-cell transmission. In earlier works based on the VSINR maximization, the parameters which control the interference power and the noise variance were set to fixed values regardless of channel realizations and the signal-to-noise ratio level. In order to obtain an improved WSR performance, we propose a method which adaptively adjusts the parameters after establishing a connection between the WSR and VSINR. Our proposed method can be applied to the cases of various coordination levels among base stations. To address practical implementation issues, a decentralized implementation of the beamforming techniques is also proposed based on local channel state information. Numerical results confirm that the proposed centralized schemes provide near-optimal WSR performance and the proposed decentralized methods show a negligible performance loss compared to the centralized algorithms with reduced system complexity.

In this letter, we study multiple-input single-output joint processing (JP) systems with limited feedback where base stations exchange both channel state information and their data via ideal backhaul links. In order to optimize the sum-rate performance of the JP system, we propose a new feedback bit allocation scheme which maximizes quantization accuracy in the presence of pathloss. The quantization accuracy is formulated by the expectation of the inner product between the actual channel vector and the quantized channel vector. First, we derive the quantization accuracy as a closed form, which compensates the phase difference of two channels. Then, the maximum quantization accuracy is achieved by searching possible bit combinations. Simulation results show that the sum rate of our proposed feedback bit allocation strategy is more than twice compared to the conventional equal bit allocation method in the three cell case.