The rapid expansion of highways in desert regions has resulted in an increase in camel-vehicle collisions, leading to substantial human, economic, and animal welfare impacts. Despite the success of virtual fencing in managing livestock such as cattle, goats, and sheep, its application for camels remains largely unexplored. This paper introduces an innovative global positioning system (GPS)-enabled virtual fencing prototype that leverages non-invasive auditory cues and real-time monitoring to control camel movements. The system integrates advanced geofencing algorithms, a random forest machine learning classifier trained on accelerometer data with an accuracy of 92% for activity recognition, and long range (LoRa) communication for reliable long-range data transmission. Field tests on camels demonstrate that auditory signals at 2000 Hz effectively deter the camels from crossing virtual boundaries after repeated interactions. The system maintains robust performance, achieving GPS positional accuracy of 8.08 meters and ensuring effective communication over distances up to 1.5 km. This study offers a significant contribution to the fields of wireless communication and Internet of Things (IoT)-based animal management systems.

Delivering an immersive experience to virtual reality (VR) users through wireless connectivity offers the freedom to engage from anywhere at any time. Nevertheless, it is challenging to ensure seamless wireless connectivity that delivers real-time and high-quality videos to the VR users. This paper proposes a field of view (FoV) aware caching for mobile edge computing (MEC)-enabled wireless VR network. In particular, the FoV of each VR user is cached/prefetched at the base stations (BSs) based on the caching strategies tailored to each BS. Specifically, decentralized and personalized federated learning (DP-FL) based caching strategies with guarantees are presented. Considering VR systems composed of multiple VR devices and BSs, a DP-FL caching algorithm is implemented at each BS to personalize content delivery for VR users. The utilized DP-FL algorithm guarantees a probably approximately correct (PAC) bound on the conditional average cache hit. Further, to reduce the cost of communicating gradients, one-bit quantization of the stochastic gradient descent (OBSGD) is proposed, and a convergence guarantee of$\mathcal {O}(1/\sqrt {T})$is obtained for the proposed algorithm, where T is the number of iterations. Additionally, to better account for the wireless channel dynamics, the FoVs are grouped into multicast or unicast groups based on the number of requesting VR users. The performance of the proposed DP-FL algorithm is validated through realistic VR head-tracking dataset, and the proposed algorithm is shown to have better performance in terms of average delay and cache hit as compared to baseline algorithms.

This paper focuses on the signal classifier component of a tele-rehabilitation framework that uses wearable surface electromyography (sEMG) devices to identify hand grasps and deliver real-time, adaptive therapy. The prediction accuracy and robustness of a stacking ensemble classifier significantly — with the combination of Support Vector Machine (SVM), Random Forest (RF), and a Logistic Regression meta—learner-improves performance, enabling continuous and personalized monitoring. Through the use of machine learning techniques, this study enables patients to conduct rehabilitation exercises remotely and to track their progress in real-time with therapists.

Extended reality (XR) applications often perform resource-intensive tasks, which are computed remotely, a process that prioritizes the latency criticality aspect. To this end, this paper shows that through leveraging the power of the central cloud (CC), the close proximity of edge computers (ECs), and the flexibility of uncrewed aerial vehicles (UAVs), a UAV-aided hybrid cloud/mobile-edge computing architecture promises to handle the intricate requirements of future XR applications. In this context, this paper distinguishes between two types of XR devices, namely, strong and weak devices. The paper then introduces a cooperative non-orthogonal multiple access (Co-NOMA) scheme, pairing strong and weak devices, so as to aid the XR devices quality-of-user experience by intelligently selecting either the direct or the relay links toward the weak XR devices. A sum logarithmic-rate maximization problem is, thus, formulated so as to jointly determine the computation and communication resources, and link-selection strategy as a means to strike a trade-off between the system throughput and fairness. Subject to realistic network constraints, e.g., power consumption and delay, the optimization problem is then solved iteratively via discrete relaxations, successive-convex approximation, and fractional programming, an approach which can be implemented in a distributed fashion across the network. Simulation results validate the proposed algorithms performance in terms of log-rate maximization, delay-sensitivity, scalability, and runtime performance. The practical distributed Co-NOMA implementation is particularly shown to offer appreciable benefits over traditional multiple access and NOMA methods, highlighting its applicability in decentralized XR systems.

Modern technology has contributed to the rise of high-dimensional data in various domains such as bio-informatics, chemometrics, and face recognition. In the recent literature, random projections and, in particular, randomly-projected ensembles based on the classical Linear Discriminant Analysis (LDA), have been proposed for classification problems involving such high-dimensional data. In this work, we study the two main classes of randomly-projected LDA ensemble classifiers, namely discriminant averaging and vote averaging. Through asymptotic analysis in a growth regime where the problem dimensions are assumed to grow at constant rates to each other for a fixed ensemble size, we determine the exact mechanism through which the ensemble size affects the classification performance. Furthermore, we investigate whether projection selection truly matters in an ensemble setting, and, ultimately, derive the optimal form of the randomly-projected LDA ensemble. Motivated by these findings, we propose a framework for efficient tuning of the optimal classifier's ensemble size and projection dimension based on an estimator of the classifier probability of misclassification which is consistent under the assumed growth regime. The proposed framework is shown to outperform the existing rule-of-thumb, as well as other methods for parameter tuning, on both real and synthetic data.

The evolvement of wireless communication services concurs with significant growth in data traffic, thereby inflicting stringent requirements on terrestrial networks. This work invigorates a connectivity solution that integrates aerial and terrestrial communications with a cloud-enabled high-altitude platform station (C-HAPS) to promote an equitable connectivity landscape. The C-HAPS system is connected to terrestrial base-stations and hot-air balloons via a data-sharing fronthauling strategy. The base-stations and hot-air balloons are then grouped into disjoint clusters and coordinately serve both aerial and terrestrial users. The paper focuses on determining the user-to-transmitter scheduling policy and the associated users’ beamforming vectors in the downlink direction of the considered network by maximizing two different objectives: the sum-rate and sum-of-log of the long-term average rate, both subject to limited transmit power and finite fronthaul capacity. The paper uses well-chosen convexification and approximation steps, such as fractional programming and sparse beamforming via re-weighted$\ell _{0}$-norm approximation, to solve the two non-convex discrete and continuous optimization problems using numerical iterative optimization algorithms. The results outline the gain illustrated through equitable access service in crowded and unserved areas and showcase the numerical benefits stemming from the proposed C-HAPS coordination of hot-air balloons and terrestrial base-stations for empowering the digital inclusion framework.

This paper proposes, and evaluates the benefits of, a hybrid central cloud (CC) and mobile edge computing (MEC) platform, especially introduced to balance the network resources for joint communication and computation. The transmission is further empowered by splitting the users’ messages into private and common parts, to mitigate the interference within the CC and MEC platforms. While several power-hungry, computationally-limited unmanned aerial vehicles (UAVs) are deployed at the cell-edge to boost the CC connectivity and relieve part of its computation burden, the CC connects to the base-stations via capacity-limited fronthauls. The paper then considers the problem of maximizing the weighted sum-rate subject to fronthaul and computation capacity, achievable rates, power, delay, and data-split constraints. Thereby determining the beamforming vectors associated with the private and common messages, the computation allocations, and the data-split factors. Such intricate non-convex optimization problem is tackled using an iterative algorithm that relies on well-chosen discrete relaxation, successive convex approximation, and fractional programming, and can be compellingly implemented in a distributed fashion. The simulations illustrate the proposed algorithm’s capabilities for empowering joint communication and computation, and highlight the pronounced role of rate-splitting and common message decoding in alleviating large-scale interference in hybrid CC/MEC networks.

The tremendous growth of data traffic in mobile communication networks (MCNs) and the associated exponential increase in mobile devices’ numbers necessitate the use of multi-cloud radio access networks (MC-RANs) as a viable solution to cope with the requirements of next-generation MCNs (6G). In MC-RANs, each central processor (CP) manages the signal processing of its own set of base stations (BSs), and so the system performance becomes a function of the joint intra-cloud and inter-cloud interference mitigation techniques. To this end, this paper considers the problem of maximizing the network-wide energy efficiency (EE) subject to user-to-cloud association, fronthaul capacity, maximum transmit power, and achievable rate constraints, so as to determine the joint beamforming vector of each user and the user-to-cloud association strategy. The paper tackles the non-convex and mixed discrete-continuous nature of the problem formulation using fractional programming (FP) and inner-convex approximation (ICA) techniques, as well as l0-norm relaxation heuristics, and shows how the proposed approach can be implemented in a distributed fashion via a reasonable amount of information exchange across the CPs. The paper simulations highlight the appreciable algorithmic efficiency of the proposed approach over state-of-the-art schemes.

Despite the promising high-data rate features of visible light communications (VLC), they still suffer from unbalanced services due to blockages and channel fluctuation among users. This paper introduces and evaluates a new transmission scheme which adopts cooperative non-orthogonal multiple access (Co-NOMA) in hybrid VLC/radio-frequency (RF) systems, so as to improve both system sum-rate and fairness. Consider a network consisting of one VLC access point (AP) and multiple strong and weak users, where each weak user is paired with a strong user. Each weak user can be served either directly by the VLC AP, or via the strong user which converts light information received through the VLC link, and forwards the information to the weak user via the RF link. The paper then maximizes a network-wide weighted sum-rate, so as to jointly determine the strong-weak user-pairs, the serving link of each weak user (i.e., either direct VLC or hybrid VLC/RF), and the power of each user message, subject to user connectivity and transmit power constraints. The paper tackles such a mixed-integer non-convex optimization problem using an iterative approach. Simulations show that the proposed scheme significantly improves the VLC network performance (i.e., sum-rate and fairness) as compared to the conventional NOMA scheme.

To best provision the wireless data deluge, service providers are increasingly considering the use of Unmanned aerial vehicles (UAVs) for enhancing wireless connectivity. UAVs are especially important in case of disasters and accidents which may cripple terrestrial networks. In order to maintain the communication of UAVs with the core network, it becomes particularly important to connect UAVs to terrestrial base stations (BSs) via wireless backhaul links. In this work, we use stochastic geometry to study the impact of millimeter-wave (mmWave) backhauling of UAVs in a hybrid aerial-terrestrial cellular network, where the UAVs are added to assist terrestrial BSs in delivering service to users (UEs). In the proposed model, the UE can associate with either a terrestrial BS or a UAV connected to a BS to get backhaul support. The performance of the model is evaluated in terms of coverage probability and validated against intensive simulations. The obtained results unveil that the quality of the UAVs backhaul link has a significant role in improving the UEs experience. The results further illustrate the impact of the different UAVs heights regimes on the coverage probability.

Since its first use by Euler on the problem of the seven bridges of Königsberg, graph theory has shown excellent abilities in solving and unveiling the properties of multiple discrete optimization problems. The study of the structure of some integer programs reveals equivalence with graph theory problems making a large body of the literature readily available for solving and characterizing the complexity of these problems. This tutorial presents a framework for utilizing a particular graph theory problem, known as the clique problem, for solving communications and signal processing problems. In particular, this article aims to illustrate the structural properties of integer programs that can be formulated as clique problems through multiple examples in communications and signal processing. To that end, the first part of the tutorial provides various optimal and heuristic solutions for the maximum clique, maximum weight clique, and ${k}$ -clique problems. The tutorial, further, illustrates the use of the clique formulation through numerous contemporary examples in communications and signal processing, mainly in maximum access for nonorthogonal multiple access networks, throughput maximization using index and instantly decodable network coding, collision-free radio-frequency identification networks, and resource allocation in cloud-radio access networks. Finally, the tutorial sheds light on the recent advances of such applications, and provides technical insights on ways of dealing with mixed discrete-continuous optimization problems.

This month’s issue offers insight into efficient compression and execution of DNNs, the challenge of connecting rural areas, and the clique problem in wireless communication. which

This paper analyzes the integration of Non-Orthogonal Multiple Access (NOMA) in a Fog Radio Access Network (FRAN) architecture with limited fronthaul capacity. More precisely, it proposes methods for optimizing the resource allocation for the downlink of a NOMA-based FRAN with multiple resource blocks (RB). The resource allocation problem is formulated as a mixed-integer optimization problem, which determines the user-to-RB assignment, the power allocated to each RB, and the power split levels of the NOMA users served by each RB. The optimization problem maximizes a network-wide rate-based utility function subject to fronthaul-capacity constraints. The paper proposes a feasible decoupled solution for such a non-convex optimization problem using a three-step hybrid centralized/distributed approach, which in part relies on the edge-devices computation capabilities. The paper proposes and compares two distinct methods for solving the assignment problem, namely a Hungarian-based method, and a Multiple Choice Knapsack-based method. The power allocation to RBs and the NOMA power split optimization are solved using the alternating direction method of multipliers (ADMM). Simulations results illustrate the advantages of the proposed methods compared to different baseline schemes, including the conventional Orthogonal Multiple Access (OMA), for different utility functions and different network environments.

The superiority of optical communications in underwater mediums, in terms of higher data rate and reliability, makes underwater optical wireless communications (UOWC) more favorable to provide ultra-reliable low-latency underwater communications, as compared to other wireless technologies, e.g., acoustic and radio frequency (RF) communications. UOWC limited transmission range, however, remains a major hurdle against assessing its true deployment benefits, which motivates for the necessity of developing practical routing protocols for multi-hop underwater optical wireless sensor networks (UOWSNs). This paper sheds light on the existing state-of-art UOWC routing protocols, the majority of which requires centralized implementation with large end-to-end delay. The article further proposes routing algorithms which can be implemented in a distributed fashion across the multi-hop links, with a reasonable amount of information exchange. The merits of the proposed algorithms are particularly highlighted through illustrative simulations, which show how the proposed strategies outperform the classical protocols, both in terms of reliability and end-to-end latency. Finally, the paper shows how the proposed distributive routing protocols achieve ultra-reliable low-latency underwater communications.

Visible light communications (VLC) have emerged as a strong candidate for meeting the escalating demand for high data rates. In this paper, we consider a VLC network, where multiple access points (APs) serve both energy-harvesting users (EHUs), i.e., users who harvest energy from light emitted by diodes and information users (IUs), i.e., users who gather data information. In order to jointly balance the achievable sum rate at the IUs and the energy harvested by the EHUs, the paper considers maximizing a network-wide utility, which consists of a weighted sum of the IUs sum rate and the EHUs harvested energy, subject to individual IU rate constraint, individual EHU harvested-energy constraint, and AP power constraints, so as to jointly determine the direct current (DC) bias value at each AP, and the power of the alternating-current (AC) signals of the users. A difficult non-convex optimization problem is solved using an iterative approach which relies on inner convex approximations, and compensates for the used approximations using proper outer-loop updates. The paper further considers solving the special cases of the problem, i.e., maximizing the sum rate, and maximizing the total harvested-energy, both subject to the same constraints. Numerical results highlight the significant performance improvement of the proposed algorithms, and illustrate the impacts of the network parameters on the performance trade-off between the sum rate and harvested-energy.

Simultaneous co-channel transmission and reception, denoted as in-band full-duplex (FD) communications, has been promoted as a solution to improve the spectral efficiency in wireless networks. For cellular networks, in addition to the existing aggregate interference in half-duplex transmission, the residual self-interference and cross-mode interference [i.e., between uplink (UL) and downlink (DL)] impose major obstacles for FD communications' deployment. Although the FD communication's promising impact on the overall network data rate has been established in the literature, the rate gains are achieved in the DL transmissions at the expense of marginal gain, or even degradation, for the UL transmissions. This paper, therefore, focuses on the analysis of UL ergodic rate in FD cellular networks where a minimum distance between BSs using the same time-frequency resource block is imposed. Hence, the mutually interfering BSs' locations are modeled by Matérn hard core point process. The distribution of the aggregate interference and the channel-to-interference-plus-noise ratio at the UL of a typical user are characterized using a stochastic geometry analysis. Several UL power control techniques are presented and their resulting ergodic rates are derived and compared. The simulation results suggest that the UL performance highly depends on the network parameters and the UL power control techniques.

In order to meet the new escalating demand for high data rate services and applications, visible light communication (VLC) has emerged as a promising solution for the fifth-generation (5G) wireless networks and beyond. Consider a VLC network, where multiple access points (APs) serve both energy-harvesting users (EHUs), i.e., users which harvest energy from light intensity, and information-users (IUs), i.e., users which gather data information. The performance of the system becomes a function of the direct current (DC) bias values allocated to each AP. After adopting a zero-forcing (ZF) precoding approach to cancel the inter-cell interference, the paper formulates the problem of maximizing the network harvested energy subject to individual harvested energy and data rate constraints at the EHUs and IUs, respectively, so as to determine the DC bias of every AP. The paper then proposes solving such a difficult non-convex optimization problem using an iterative approach. The proposed algorithm uses well-chosen approximations of the objective and constraints functions, and compensates for the approximations using proper outer-loop updates. The paper further proposes a sub optimal heuristic which provides a feasible, yet simple, solution to the problem. Numerical results illustrate the convergence of our proposed algorithms, and highlight the significant performance improvement of the proposed algorithm as compared to the proposed baseline approach.

Non-Orthogonal Multiple Access (NOMA) is a promising technology for 5G that enables each resource unit to simultaneously serve multiple users. This work evaluates the potential benefit of joint scheduling and power adaptation in NOMA- based downlink in Fog-Radio Access Networks (FRAN). We consider the downlink of a FRAN, where the Fog Access Points (FAPs) are connected to central cloud baseband units (BBUs) through capacity-constrained fronthaul links. The FAPs adopt a two-user NOMA scheme, within each resource block (RB), to serve a common set of users. The paper formulates an optimization problem which maximizes a network-wide {rate-based utility} function subject to fronthaul- capacity constraints, so as to determine both the user- to-FAP assignment and the power levels of the users served by each FAP. The main contribution of the paper is solving this mixed-integer non-convex optimization problem using a two- step centralized-distributed approach, which is aligned with FRAN operation that {aims to} %relies on partially shifting the network control to the FAPs so as to overcome delays due to fronthaul rate constraints. The assignment step is first solved at the centralized BBU pool by reformulating the problem such that the Hungarian algorithm is applicable. The power {adaptation} is then solved at every FAP using a barrier method. Simulation results show that the proposed NOMA-based algorithm outperforms conventional Orthogonal Multiple Access (OMA) algorithms, even with stringent fronthaul limitations. The proposed algorithm further shows an appreciable {performance trade-off} between the rate and fairness metrics.

Cloud radio access network (CRAN) emerges as a promising architecture for large-scale interference management. This paper addresses the benefit of one particular type of coordinated resource allocation in CRANs through the combined effect of joint scheduling and beamforming. Consider the downlink of a CRAN where the cloud is connected to several remote radio heads (RRHs), each equipped with multiple antennas. The transmit frame of every RRH is formed by several radio resource blocks (RRBs), each capable of serving multiple single-antenna users via spatial multiplexing using beamforming. The paper focuses on the problem of maximizing the network-wide weighted sum-rate by jointly determining the set of scheduled users at each RRB, and their corresponding beamforming vectors. The main contribution of the paper is to solve such a mixed discrete-continuous optimization problem using a graph-theoretical based approach. The paper introduces the joint scheduling and beamforming graph, wherein each independent set accounts for a feasible schedule and feasible beamforming vectors. Afterward, the joint scheduling and beamforming problem is shown to be equivalent to a maximum independent set problem in the proposed graph. Simulation results suggest that the proposed joint solution provides appreciable performance improvements as compared to the classical iterative approach.

The feasibility of resource allocation schemes is a major challenge in the practical implementation of wireless systems. Decentralized resource allocation is one such feasibility requirement, as it yields optimized schemes when no centralized processing is possible. This paper evaluates one particular type of decentralized interference management schemes in a full-duplex (FD) cellular network. Consider an FD cellular network that allows flexible partial overlap between the uplink (UL) and downlink (DL) frequency channel. The channel overlap generates self-interference, cross-mode interference and intra-mode interference. The performance of the system becomes, therefore, a function of the powers allocated at each base station (BS)-user pair, and the fraction of spectrum overlap between the DL and UL of each communicating pair. The paper considers the problem of maximizing a network-wide utility function subject to power constraints, so as to appropriately fine-tune the spectrum overlap and the transmit powers in a distributed manner across the network. The paper proposes solving the problem using the externalities approach, which can be implemented in a distributed fashion with a reasonable amount of information exchange between the network entities. The paper further examines three types of utility functions: the sum of log-rate, the network spectral efficiency, and the energy efficiency, so as to clearly describe the trade-off between the achieved rate, the consumed power, and UL/DL fairness in the FD setup. Simulation results highlight the convergence of the proposed distributed algorithm, and illustrate its performance under different utilities as compared to centralized solutions for various networks scenarios.

Recent studies on cloud-radio access networks assume either signal-level or scheduling-level coordination. This paper considers a hybrid coordinated scheme as a means to benefit from both policies. Consider the downlink of a multi-cloud radio access network, where each cloud is connected to several base-stations (BSs) via high capacity links and, therefore, allows for joint signal processing within the cloud transmission. Across the multiple clouds, however, only scheduling-level coordination is permitted, as low levels of backhaul communication are feasible. The frame structure of every BS is composed of various time/frequency blocks, called power-zones (PZs), which are maintained at a fixed power level. This paper addresses the problem of maximizing a network-wide utility by associating users to clouds and scheduling them to the PZs, under the practical constraints that each user is scheduled to a single cloud at most, but possibly to many BSs within the cloud, and can be served by one or more distinct PZs within the BSs' frame. This paper solves the problem using graph theory techniques by constructing the conflict graph. The considered scheduling problem is, then, shown to be equivalent to a maximum-weight independent set problem in the constructed graph, which can be solved using efficient techniques. This paper then proposes solving the problem using both optimal and heuristic algorithms that can be implemented in a distributed fashion across the network. The proposed distributed algorithms rely on the well-chosen structure of the constructed conflict graph utilized to solve the maximum-weight independent set problem. Simulation results suggest that the proposed optimal and heuristic hybrid scheduling strategies provide appreciable gain as compared with the scheduling-level coordinated networks, with a negligible degradation to signal-level coordination.

Finite energy reserves and the irreplaceable nature of nodes in battery-driven wireless sensor networks (WSNs) motivate energy-aware network operation. This paper considers energy-efficiency in a WSN by investigating the problem of minimizing the power consumption consisting of both radiated and circuit power of sensor nodes, so as to determine an optimal set of active sensors and corresponding transmit powers. To solve such a mixed discrete and continuous problem, the paper proposes various sensor selection and power allocation algorithms of low complexity. Simulation results show an appreciable improvement in their performance over a system in which no selection strategy is applied, with a slight gap from derived lower bounds. The results further yield insights into the relationship between the number of activated sensors and its effect on total power in different regimes of operation, based on which recommendations are made for which strategies to use in the different regimes.

Conventional cloud radio access networks assume single cloud processing and treat inter-cloud interference as background noise. This paper considers the downlink of a multi-cloud radio access network (CRAN) where each cloud is connected to several base-stations (BS) through limited-capacity wireline backhaul links. The set of BSs connected to each cloud, called cluster, serves a set of pre-known mobile users (MUs). The performance of the system becomes therefore a function of both inter-cloud and intra-cloud interference, as well as the compression schemes of the limited capacity backhaul links. The paper assumes independent compression scheme and imperfect channel state information (CSI) where the CSI errors belong to an ellipsoidal bounded region. The problem of interest becomes the one of minimizing the network total transmit power subject to BS power and quality of service constraints, as well as backhaul capacity and CSI error constraints. The paper suggests solving the problem using the alternating direction method of multipliers (ADMM). One of the highlight of the paper is that the proposed ADMM-based algorithm can be implemented in a distributed fashion across the multi-cloud network by allowing a limited amount of information exchange between the coupled clouds. Simulation results show that the proposed distributed algorithm provides a similar performance to the centralized algorithm in a reasonable number of iterations.

The radio-frequency (RF) technology is a scalable solution for the backhaul planning. However, its performance is limited in terms of data rate and latency. Free Space Optical (FSO) backhaul, on the other hand, offers a higher data rate but is sensitive to weather conditions. To combine the advantages of RF and FSO backhauls, this paper proposes a cost-efficient backhaul network using the hybrid RF/FSO technology. To ensure a resilient backhaul, the paper imposes a given degree of redundancy by connecting each node through K link-disjoint paths so as to cope with potential link failures. Hence, the network planning problem considered in this paper is the one of minimizing the total deployment cost by choosing the appropriate link type, i.e., either hybrid RF/FSO or optical fiber (OF), between each couple of base-stations while guaranteeing K link-disjoint connections, a data rate target, and a reliability threshold. The paper solves the problem using graph theory techniques. It reformulates the problem as a maximum weight clique problem in the planning graph, under a specified realistic assumption about the cost of OF and hybrid RF/FSO links. Simulation results show the cost of the different planning and suggest that the proposed heuristic solution has a close-to-optimal performance for a significant gain in computation complexity.

Simultaneous reuse of spectral resources by uplink and downlink, denoted as in-band full duplex (FD) communication, is promoted to double the spectral efficiency when compared to its half-duplex (HD) counterpart. Interference management, however, remains challenging in FD cellular networks, especially when high disparity between uplink and downlink transmission powers exists. The uplink performance can be particularly deteriorated when operating on channels that are simultaneously occupied with downlink transmission. This paper considers a cellular wireless system with partial spectrum overlap between the downlink and uplink. The performance of the system becomes, therefore, a function of the overlap fraction, as well as the power levels of both the uplink and downlink transmissions. The paper considers the problem of maximizing an overall network utility to find the uplink/downlink transmission powers and the spectrum overlap fraction between the uplink and downlink spectrum in each cell, and proposes solving the problem using interior point method. Simulations results confirm the vulnerability of the uplink performance to the FD operation, and show the superiority of the proposed scheme over the FD and HD schemes. The results further show that explicit uplink and downlink performance should be considered for efficient design of cellular networks with overlapping uplink/downlink resources.

The advent of smartphones and tablets over the past several years has resulted in a drastic increase of global carbon footprint, due to the explosive growth of data traffic. Improving energy efficiency (EE) becomes, therefore, a crucial design metric in next generation wireless systems (5G). Cloud radio access network (C-RAN), a promising 5G network architecture, provides an efficient framework for improving the EE performance, by means of coordinating the transmission across the network. This paper considers a C-RAN system formed by several clusters of remote radio heads (RRHs), each serving a predetermined set of mobile users (MUs), and assumes imperfect channel state information (CSI). The network performance becomes therefore a function of the intra-cluster and inter-cluster interference, as well as the channel estimation error. The paper optimizes the transmit power of each RRH in order to maximize the network global EE subject to MU service rate requirements and RRHs maximum power constraints. The paper proposes solving the optimization problem using a heuristic algorithm based on techniques from optimization theory via a two-stage iterative solution. Simulation results show that the proposed power allocation algorithm provides an appreciable performance improvement as compared to the conventional systems with maximum power transmission strategy. They further highlight the convergence of the proposed algorithm for different networks scenarios.

The deluge of date rate in today's networks imposes a cost burden on the backhaul network design. Developing cost-efficient backhaul solutions becomes an exciting, yet challenging, problem. Traditional technologies for backhaul networks, including either radio-frequency (RF) backhauls or optical fibers (OF). While RF is a cost-effective solution as compared with OF, it supports the lower data rate requirements. Another promising backhaul solution is the free-space optics (FSO) as it offers both a high data rate and a relatively low cost. The FSO, however, is sensitive to nature conditions, e.g., rain, fog, and line-of-sight. This paper combines both the RF and FSO advantages and proposes a hybrid RF/FSO backhaul solution. It considers the problem of minimizing the cost of the backhaul network by choosing either OF or hybrid RF/FSO backhaul links between the base stations, so as to satisfy data rate, connectivity, and reliability constraints. It shows that under a specified realistic assumption about the cost of OF and hybrid RF/FSO links, the problem is equivalent to a maximum weight clique problem, which can be solved with moderate complexity. Simulation results show that the proposed solution shows a close-to-optimal performance, especially for reasonable prices of the hybrid RF/FSO links. They further reveal that the hybrid RF/FSO is a cost-efficient solution and a good candidate for upgrading the existing backhaul networks.

This paper addresses the joint coordinated scheduling and power control problem in cloud-enabled networks. Consider the downlink of a cloud-radio access network (CRAN), where the cloud is only responsible for the scheduling policy, power control, and synchronization of the transmit frames across the single-antenna base-stations (BS). The transmit frame consists of several time/frequency blocks, called power-zones (PZs). The paper considers the problem of scheduling users to PZs and determining their power levels (PLs), by maximizing the weighted sum-rate under the practical constraints that each user cannot be served by more than one base-station, but can be served by one or more power-zones within each base-station frame. The paper solves the problem using a graph theoretical approach by introducing the joint scheduling and power control graph formed by several clusters, where each is formed by a set of vertices, representing the possible association of users, BSs, and PLs for one specific PZ. The problem is, then, formulated as a maximum-weight clique problem, in which the weight of each vertex is the sum of the benefits of the individual associations belonging to that vertex. Simulation results suggest that the proposed cross-layer scheme provides appreciable performance improvement as compared to schemes from recent literature.

Recent studies on cloud-radio access networks (CRANs) assume the availability of a single processor (cloud) capable of managing the entire network performance; inter-cloud interference is treated as background noise. This paper considers the more practical scenario of the downlink of a CRAN formed by multiple clouds, where each cloud is connected to a cluster of multiple-antenna base stations (BSs) via high-capacity wireline backhaul links. The network is composed of several disjoint BSs' clusters, each serving a pre-known set of single-antenna users. To account for both inter- cloud and intra-cloud interference, the paper considers the problem of minimizing the total network power consumption subject to quality of service constraints, by jointly determining the set of active BSs connected to each cloud and the beamforming vectors of every user across the network. The paper solves the problem using Lagrangian duality theory through a dual decomposition approach, which decouples the problem into multiple and independent subproblems, the solution of which depends on the dual optimization problem. The solution then proceeds in updating the dual variables and the active set of BSs at each cloud iteratively. The proposed approach leads to a distributed implementation across the multiple clouds through a reasonable exchange of information between adjacent clouds. The paper further proposes a centralized solution to the problem. Simulation results suggest that the proposed algorithms significantly outperform the conventional per-cloud update solution, especially at high signal-to-interference-plus- noise ratio (SINR) target.

In the context of resource allocation in cloud- radio access networks, recent studies assume either signal-level or scheduling-level coordination. This paper, instead, considers a hybrid level of coordination for the scheduling problem in the downlink of a multi-cloud radio- access network, so as to benefit from both scheduling policies. Consider a multi-cloud radio access network, where each cloud is connected to several base-stations (BSs) via high capacity links, and therefore allows joint signal processing between them. Across the multiple clouds, however, only scheduling-level coordination is permitted, as it requires a lower level of backhaul communication. The frame structure of every BS is composed of various time/frequency blocks, called power- zones (PZs), and kept at fixed power level. The paper addresses the problem of maximizing a network-wide utility by associating users to clouds and scheduling them to the PZs, under the practical constraints that each user is scheduled, at most, to a single cloud, but possibly to many BSs within the cloud, and can be served by one or more distinct PZs within the BSs' frame. The paper solves the problem using graph theory techniques by constructing the conflict graph. The scheduling problem is, then, shown to be equivalent to a maximum- weight independent set problem in the constructed graph, in which each vertex symbolizes an association of cloud, user, BS and PZ, with a weight representing the utility of that association. Simulation results suggest that the proposed hybrid scheduling strategy provides appreciable gain as compared to the scheduling-level coordinated networks, with a negligible degradation to signal-level coordination.

This paper addresses the coordinated scheduling problem in cloud-enabled networks. Consider the downlink of a cloud-radio access network (CRAN), where the cloud is only responsible for the scheduling policy and the synchronization of the transmit frames across the connected base-stations (BS). The transmitted frame of every BS consists of several time/frequency blocks, called power-zones (PZ), maintained at fixed transmit power. The paper considers the problem of scheduling users to PZs and BSs in a coordinated fashion across the network, by maximizing a network-wide utility under the practical constraint that each user cannot be served by more than one base-station, but can be served by one or more power-zones within each base-station frame. The paper solves the problem using a graph theoretical approach by introducing the scheduling graph in which each vertex represents an association of users, PZs and BSs. The problem is formulated as a maximum weight clique, in which the weight of each vertex is the benefit of the association represented by that vertex. The paper further presents heuristic algorithms with low computational complexity. Simulation results show the performance of the proposed algorithms and suggest that the heuristics perform near optimal in low shadowing environments.

The cloud-radio access network (CRAN) is expected to be the core network architecture for next generation mobile radio systems. In this paper, we consider the downlink of a CRAN formed of one central processor (the cloud) and several base station (BS), where each BS is connected to the cloud via either a wireless or capacity-limited wireline backhaul link. The paper addresses the joint design of the hybrid backhaul links (i.e., designing the wireline and wireless backhaul connections from the cloud to the BSs) and the access links (i.e., determining the sparse beamforming solution from the BSs to the users). The paper formulates the hybrid backhaul and access link design problem by minimizing the total network power consumption. The paper solves the problem using a two-stage heuristic algorithm. At one stage, the sparse beamforming solution is found using a weighted mixed 11/12 norm minimization approach; the correlation matrix of the quantization noise of the wireline backhaul links is computed using the classical rate-distortion theory. At the second stage, the transmit powers of the wireless backhaul links are found by solving a power minimization problem subject to quality-of-service constraints, based on the principle of conservation of rate by utilizing the rates found in the first stage. Simulation results suggest that the performance of the proposed algorithm approaches the global optimum solution, especially at high signal-to-interference-plus-noise ratio (SINR).

We propose a distributed user selection strategy in a network MIMO setting with M base stations serving K users. Each base station is equipped with L antennas, where LM ≪ K. The conventional selection strategy is based on a well known technique called semi-orthogonal user selection when the zero-forcing beamforming (ZFBF) is adopted. Such technique, however, requires perfect channel state information at the transmitter (CSIT), which might not be available or need large feedback overhead. This paper proposes an alternative distributed user selection technique where each user sets a timer that is inversely proportional to his channel quality indicator (CQI), as a means to reduce the feedback overhead. The proposed strategy allows only the user with the highest CQI to respond with a feedback. Such technique, however, remains collision free only if the transmission time is shorter than the difference between the strongest user timer and the second strongest user timer. To overcome the situation of longer transmission times, the paper proposes another feedback strategy that is based on the theory of compressive sensing, where collision is allowed and all users encode their feedback information and send it back to the base-stations simultaneously. The paper shows that the problem can be formulated as a block sparse recovery problem which is agnostic on the transmission time, which makes it a good alternative to the timer approach when collision is dominant.

Complexity and hardware constraints are two essential considerations in applying interference mitigation techniques to practical wireless systems. This paper considers a practical wireless backhaul network composed of several access nodes (AN), each serving several remote terminals (RT), and where the transmit frame structure at each AN is comprised of multiple zones, with different RTs scheduled on different zones. The objective of this paper is to design power control strategies to mitigation inter-AN interference in the downlink. Unlike prior studies, this paper adopts a practical constraint whereby every AN maintains the same power level across the different zones within one transmitted frame. The main advantage of imposing this new constraint, called the one-power-zone (OPZ) constraint in this paper, is that for a class of scheduling policies under which the number of zones assigned to each RT is fixed, the power optimization and the scheduling subproblems are decoupled under the OPZ constraint. This allows the design of efficient power control methods independent of scheduling. Further, it also simplifies the design of radio-frequency (RF) front-end. The main contribution of this paper is a set of efficient algorithms to solve this constrained power control problem based on an iterative function evaluation technique. The proposed algorithms have low computational complexity, and can be implemented in a distributed fashion. Some of these algorithms can be further implemented asynchronously at each AN.

Employing centralized resource management schemes is generally infeasible in large-scale networks. The deployment of heterogeneous Femtocell Access Points (FAPs) over the cellular licensed spectrum is therefore challenging. In particular, the resulting inter-node interference inhibits the network performance. In this paper, we design a hierarchical, distributed, interference management scheme that exploits the benefits of clustering. First, in order to reduce the cross-tier interference, each FAP independently identifies vacant subbands for potential transmission. Then, by exchanging some simple messages with its immediate neighbors in an iterative fashion, coalition clusters are formed. Given the small population of each group, centralized resource management is subsequently performed to avoid intra-cluster interference. Different clusters, however, may still share a fraction of common idle channels, which degrades system performance. Therefore, this paper further considers inter-cluster interference management to determine the set of privileged FAPs that can share a subband via solving a binary power control optimization problem. While the optimal solution requires prohibitive complexity, this paper provides tight bounds on the sum rate of the binary power control problem. The simulation results show that, in a high interference regime, inter-cluster coordination provides a significant performance improvement compared to the case of no coordination.

Conventional wireless cellular systems treat out-of-cell interference as noise. This paper proposes methods and examines the benefit of designing decodable interference signals, whereby a transmitter may split its message into a common and a private part, and the common message may be decoded and subtracted by users in adjacent cells. This paper considers a downlink scenario, where the base-stations are equipped with multiple antennas, the mobile users are equipped with a single antenna, and multiple users are active simultaneously via spatial multiplexing. The network optimization problem consists of jointly determining the appropriate users in adjacent cells for rate splitting, the optimal transmit beamformers for common and private messages, and the optimal common-private rates to maximize the minimum achievable rate across the users. This paper shows that for fixed user selection and fixed common-private rate splitting, the optimization of transmit beamformers can be solved using a semidefinite programming (SDP) relaxation approach. Further, it is shown that for the case where the network consists of two message-splitting pairs, SDP relaxation is tight, i.e., beamforming is optimal. Finally, this paper proposes a heuristic user-selection and rate splitting strategy to characterize the performance improvement for cell-edge users due to common-message decoding.

Conventional multicell wireless systems operate with out-of-cell interference treated as noise - interference detection is infeasible as intercell interference is typically weak. This paper considers the benefit of designing decodable interference signals by allowing common-private message splitting at the transmitter and common message decoding by users in adjacent cells. In particular, we consider a downlink scenario, where base-stations are equipped with multiple transmit antennas, the remote users are equipped with a single antenna, and multiple remote users are active simultaneously via spatial division multiplexing. We solve a network optimization problem of jointly determining the appropriate users in adjacent cells for rate splitting, the optimal beamforming vectors for both common and private messages, and the optimal common-private rates to minimize the total transmit power across the base-stations subject to service rate requirements for remote users. We observe that for fixed user selection and fixed common-private rate splitting, the optimization of beamforming vectors can be performed using a semidefinite programming approach. Further, this paper proposes a heuristic user-selection and rate splitting strategy to maximize the benefit of common message decoding. Simulation results show that common message decoding can significantly improve both the total transmit power and the feasibility region for cell-edge users.

In a conventional wireless cellular system, signal processing is performed on a per-cell basis; out-of-cell interference is treated as background noise. This paper considers the benefit of coordinating base-stations across multiple cells in a multi-antenna beamforming system, where multiple base-stations may jointly optimize their respective beamformers to improve the overall system performance. Consider a multicell downlink scenario where base-stations are equipped with multiple transmit antennas employing either linear beamforming or nonlinear dirty-paper coding, and where remote users are equipped with a single antenna each, but where multiple remote users may be active simultaneously in each cell. This paper focuses on the design criteria of minimizing either the total weighted transmitted power or the maximum per-antenna power across the base-stations subject to signal-to-interference-and-noise-ratio (SINR) constraints at the remote users. The main contribution of the paper is an efficient algorithm for finding the joint globally optimal beamformers across all base-stations. The proposed algorithm is based on a generalization of uplink-downlink duality to the multicell setting using the Lagrangian duality theory. An important feature is that it naturally leads to a distributed implementation in time-division duplex (TDD) systems. Simulation results suggest that coordinating the beamforming vectors alone already provide appreciable performance improvements as compared to the conventional per-cell optimized network.