This paper proposes a dynamic orthogonal matching pursuit (OMP)-based localization for automotive radar systems. At each time instant, three dictionaries are designed based on prior information on targets’ approximate locations. We use mutual coherence as the criterion for designing each dictionary. The mutual coherence minimization problem over each dictionary is developed as a selection problem in the binary domain. Afterward, the OMP algorithm is used to perform the direction of arrival (DoA) estimation over each dictionary, and the estimated DoAs are fused together through averaging to obtain the final DoA estimation. We show that the proposed method significantly outperforms the uniform grid dictionary and improves localization accuracy.

This paper proposes to integrate reconfigurable intelligent surfaces (RISs) with unmanned aerial vehicles (UAVs) as a resilience mechanism to mitigate outages in UAV networks due to UAV and link failures. The inherent addition of RIS-aided links (UE-RIS-UAV links), combined with their reconfigurability, creates alternative paths for user equipment (UEs) to transmit signals to UAVs. The paper studies the problem of maximizing connectivity of UAV networks by jointly considering UE positioning, RIS-aided link selection, and phase shift design of RISs. To tackle it, we propose an efficient two-step solution. In the first step, we propose a supergradient method that locates the UEs in positions that improve their communication links until a certain connectivity threshold is satisfied. Given the optimized UE positioning, the second step jointly optimizes the RIS-aided link selection and RIS phase shift design using semidefinite programming (SDP). Through simulations, we illustrate the superiority of the proposed solution compared to the solutions available in the literature.

In traditional fog-radio access networks (F-RANs), deploying many enhanced remote radio heads (eRRHs) faces challenges due to site limitations and fronthaul link costs. To tackle this issue, we introduced smart helpers (SHs) in our earlier study. SHs mainly listen to communications between eRRHs and users, smartly cache popular content, and serve users without having a fronthaul link to the macro base station (MBS). In this paper, we explore using an unmanned aerial vehicle (UAV) as the SH to assess the potential benefits of integrating UAVSHs into F-RANs. Because UAVs typically have limited battery life and cover smaller areas, we leverage a reconfigurable intelligent surface (RIS) to extend the service area of the UAVSH. To assess the performance of the considered UAVSH-aided F-RAN, we formulate the problem of average delivery delay minimization. To tackle the problem, we develop an algorithm to optimize user and RIS scheduling and caching decisions using multiagent reinforcement learning (MARL). The simulation results numerically prove that integrating a UAVSH into a RIS-assisted F-RAN leads to significant improvements in delivery delay, fronthaul load, and cache hit rate metrics.

Cooperative localization and tracking are expected to play a crucial role in supporting location-based services in 6G networks. This work shows that integrating reconfigurable intelligent surfaces (RISs) with sidelink communications between user equipments (UEs) can enhance tracking and localization accuracy in the absence of access points (APs). To achieve this, we consider a localization and tracking problem of RIS-assisted sidelink communications, where moving UEs are localized and tracked without relying on APs. Specifically, we first design orthogonal RIS phase shift vectors to separate RIS-aided (reflected) paths from direct sidelink communication paths at the receiving UE(s). The initial locations of the UEs are then derived from the estimated channel parameters, enabling the tracking of the UEs using an extended Kalman filter (EKF). We benchmark the performance of the localization with multiple RISs using the Cramér-Rao lower bound (CRLB), and we assess the EKF's performance using the root mean squared error (RMSE) metric. Simulation results indicate that the initial localization accuracy reaches the CRLB, and the EKF achieves an RMSE below 10 cm for 90% of the time.

In traditional fog-radio access networks (F-RANs), enhanced remote radio heads (eRRHs) are connected to a macro base station (MBS) through fronthaul links. Deploying a massive number of eRRHs is not always feasible due to site constraints and the cost of fronthaul links. This paper introduces an innovative concept of using smart helpers (SHs) in F-RANs. These SHs do not require fronthaul links and listen to the within-coverage eRRHs’ communications. Then, they smartly select and cache popular content. This capability enables SHs to serve users with frequent on-demand service requests potentially. As such, network operators have the flexibility to easily deploy SHs in various scenarios, such as dense urban areas and temporary public events, to expand their F-RANs and improve the quality of service (QoS). To study the performance of the proposed SH-aided F-RAN, we formulate an optimization problem of minimizing the average transmission delay that jointly optimizes cache resources and user scheduling. To tackle the formulated problem, we develop an innovative multi-stage algorithm that uses a reinforcement learning (RL) framework. Various performance measures, e.g., the average transmission delay, fronthaul load, and cache hit rate of the proposed SH-aided F-RAN are evaluated numerically and compared with those of traditional F-RANs.

The integration of reconfigurable intelligent surfaces (RISs) and unmanned aerial vehicle (UAV) communications has emerged as a promising solution for improving link quality and massive connectivity for beyond 5G wireless networks. This paper presents an innovative approach to maximizing connectivity of uplink multi-RIS-assisted UAV networks enabled by RIS placement and virtual partitioning, wherein RISs are deployed to assist in the communications between user-equipment (UEs) and UAVs. In the considered model, the UEs intend to transmit data to the UAVs, and RISs can assist in improving network connectivity by connecting the UEs to the blocked UAVs. First, exact and approximated closed-form (CF) expressions for signal-to-noise ratio (SNR) are derived based on aligned and non-aligned portions of the RISs. Then, we formulate the problem of maximizing the network connectivity that jointly considers 1) UE-RIS-UAV link selection and 2) RIS placement and virtual partitioning. This problem is a computationally expensive combinatorial optimization. Using the block coordinate descent (BCD) approach, we propose novel UE-RIS-UAV selection and RIS placement and partitioning methods. Specifically, we develop clustering and perturbation methods for UE-RIS-UAV selection, and derive a closed-form solution for the partitioning of the RISs. Moreover, for optimizing the RISs placement, Adam optimizer is used. Simulation results demonstrate that the proposed approaches yield a gain in the range of 12% to 45% compared to benchmark schemes. The finding emphasizes the potential of integrating RIS with UAV communications as a robust and reliable connectivity solution for future wireless communication systems.

In this paper, we study the problem of placing a reconfigurable intelligent surface (RIS) and tuning its reflected links to improve the resiliency and connectivity of uncrewed aerial vehicle (UAV) networks. We formulate an optimization problem of maximizing network connectivity that jointly optimizes RIS position, its phase shift, and UE-RIS-UAV link scheduling. Such problem is computationally expensive combinatorial optimization. To tackle this problem, we first design the phase control strategy at the RIS for the UE-RIS-UAV link scheduling. With such design, we propose an optimal linear search method, which has high computational complexity for large networks. Then, leveraging the convex relaxation method and the designed phase strategy, we propose another solution using semi-definite programming (SDP) optimization, which solves the problem in polynomial time. Simulation results show that our proposed solutions outperform other benchmark schemes.

Reconfigurable intelligent surface (RIS) is pivotal for beyond 5G networks in regards to the surge demand for reliable communication in unmanned aerial vehicle (UAV) networks. This paper presents an innovative approach to maximize connectivity of UAV networks using RIS deployment and virtual partitioning, wherein an RIS is deployed to assist in the communications between an user-equipment (UE) and blocked UAVs. Closed-form (CF) expressions for signal-to-noise ratio (SNR) of the two-UAV setup are derived and validated. Then, an optimization problem is formulated to maximize network connectivity by optimizing the 3D deployment of the RIS and its partitioning subject to predefined quality-of-service (QoS) constraints. To tackle this problem, we propose a method of virtually partitioning the RIS given a fixed 3D location, such that the partition phase shifts are configured to create cascaded channels between the UE and the blocked two UAVs. Then, simulated-annealing (SA) method is used to find the 3D location of the RIS. Simulation results demonstrate that the proposed joint RIS deployment and partitioning framework can significantly improve network connectivity compared to benchmarks, including RIS-free and RIS with a single narrow-beam link.

Reconfigurable intelligent surfaces (RISs) have drawn considerable attention due to their ability to introduce controllable phase-shifts onto impinging electromagnetic waves and impose link redundancy. Meanwhile, unmanned aerial vehicles (UAVs) are expected to make future 6G networks more connected, but they are prone to several failures, which cause network disintegration. To harness the benefits of both, we study their integration to improve connectivity of multi-RIS-assisted UAV networks. We first propose to define the criticality of nodes, which reflects the importance of some nodes over other nodes. We then employ the algebraic connectivity metric, which is adjusted by the reflected links of the RISs and their criticality weights, to formulate the problem of maximizing the network connectivity. Such problem is a computationally expensive combinatorial optimization. Using a relaxation method where the discrete scheduling constraint of the problem is relaxed to be continuous, we propose two efficient solutions, namely semi-definite programming (SDP) optimization and Laplacian matrix perturbation, which both solve the problem in polynomial time. We rigorously derive the lower and upper bounds of the algebraic connectivity obtained from the perturbation solution. Simulation results compare the performance of the proposed solutions with different schemes, including without RISs, unoptimized link scheduling and phase shifts, greedy search, and optimal. The results show that the proposed schemes achieve considerably improved performance with low computational complexity compared to other schemes.

It is anticipated that integrating unmanned aerial vehicles (UAVs) with reconfigurable intelligent surfaces (RISs), resulting in RIS-assisted UAV networks, will offer improved network connectivity against node failures for the beyond 5G networks. In this context, we utilize a RIS to provide path diversity and alternative connectivity options for information flow from user equipment (UE) to UAVs by adding more links to the network, thereby maximizing its connectivity. This paper employs the algebraic connectivity metric, which is adjusted by the reflected links of the RIS, to formulate the problem of maximizing the network connectivity in two cases. First, we consider formulating the problem for one UE, which is solved optimally using a linear search. Then, we consider the problem of a more general case of multiple UEs, which has high computational complexity. To tackle this problem, we formulate the problem of maximizing the network connectivity as a semi-definite programming (SDP) optimization problem that can be solved efficiently in polynomial time. In both cases, our proposed solutions find the best combination between UE(s) and UAVs through the RIS. As a result, it tunes the phase shifts of the RIS to direct the signals of the UEs to the appropriate UAVs, thus maximizing the network connectivity. Simulation results are conducted to assess the performance of the proposed solutions compared to the existing solutions.

This paper proposes a solution for energy-efficient communication in reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicle (UAV) networks. The limited battery life of UAVs is a major concern for their sustainable operation, and RIS has emerged as a promising solution to reducing the energy consumption of communication systems. The paper formulates the problem of maximizing the energy efficiency of the network as a mixed integer nonlinear program, in which UAV placement, UAV beamforming, On-Off strategy of RIS elements, and phase shift of RIS elements are optimized. The proposed solution utilizes the block coordinate descent approach and a combination of continuous and binary genetic algorithms. Moreover, for optimizing the UAV placement, Adam optimizer is used. The simulation results show that the proposed solution outperforms the existing literature. Specifically, we compared the proposed method with the successive convex approximation (SCA) approach for optimizing the phase shift of RIS elements.

Federated learning (FL) is a promising distributed machine learning technique for building inference models over wireless networks due to its ability to maintain user privacy and reduce communication overhead. In this paper, we consider minimizing the energy consumption of a device-to-device (D2D) network while maintaining the convergence rate of FL subject to its time constraint. In the considered D2D network, each device has limited transmission range and is connected partially to other devices in the network. A group of devices can form a cluster and one of these devices is judiciously selected as a local aggregator (LA) to aggregate the local models of other devices in the cluster. Leveraging the nature of D2D communications, we exploit the devices that are located at the conflict zones of LAs. As such, the LAs can disseminate their local aggregated models among them. Towards this goal, a joint optimization problem, considering scheduling the devices to the LAs and computation frequency allocation of the devices, is presented. In order to solve this NP-hard problem, an iterative solution is devised. Particularly, we decompose it into two sub-problems, namely, LAs selection and device scheduling sub-problem and computation frequency allocation sub-problem. By solving theses sub-problems iteratively, a FedD2D (federated learning with D2D communications) scheme is proposed. MATLAB simulations are conducted to verify the effectiveness of the proposed FedD2D scheme over FL conventional schemes.

Federated learning (FL) has emerged as a distributed machine learning (ML) technique to train models without sharing users’ private data. In this paper, we introduce a decentralized FL scheme that is called federated learning empowered overlapped clustering for decentralized aggregation (FL-EOCD). The introduced FL-EOCD leverages device-to-device (D2D) communications and overlapped clustering to enable decentralized aggregation, where a cluster is defined as a coverage zone of a typical device. The devices located on the overlapped clusters are called bridge devices (BDs). In the proposed FL-EOCD scheme, a clustering topology is envisioned where clusters are connected through BDs, so as the aggregated models of each cluster is disseminated to the other clusters in a decentralized manner without the need for a global aggregator or an additional hop of transmission. To evaluate our proposed FL-EOCD scheme as opposed to baseline FL schemes, we consider minimizing the overall energy-consumption of devices while maintaining the convergence rate of FL subject to its time constraint. To this end, a joint optimization problem, considering scheduling the local devices/BDs to the CHs and computation frequency allocation, is formulated, where an iterative solution to this joint problem is devised. Extensive simulations are conducted to verify the effectiveness of the proposed FL-EOCD algorithm over FL conventional schemes in terms of energy consumption, latency, and convergence rate.

Resource allocation is investigated for offloading computational-intensive tasks in dual-hop mobile edge computing (MEC) system. The envisioned system has both the cooperative access points (APs) with the computing capability and the MEC servers. A user-device (UD), therefore, first uploads a computing task to the nearest AP, and the AP can either locally process the received task or offload to MEC server. To utilize the radio resource blocks (RRBs) in the APs efficiently, we exploit the non-orthogonal multiple access (NOMA) for offloading the tasks from the UDs to the AP(s). In order to investigate the trade-off between latency and energy consumption, this work considers minimizing a weighted-sum that consists of latency and energy consumption, subject to UDs’ rate threshold, tasks’ time-delay, computational frequency scaling, and transmit power allocation constraints. With a joint consideration of all such factors, the problem is NP-hard and its global optimal solution is computationally intractable. A graph-theoretical approach is employed to solve the problem efficiently. Specifically, a novel joint MEC graph-based approach is devised, which solves the scheduling among the UDs, APs, and RRBs, the transmit power control, and the local computational frequency scaling problem(s) jointly. The joint MEC approach achieves near-optimal performance with high computational complexity. To strike a suitable balance between the performance and computational complexity of the resource allocation, a low complexity, yet efficient, pruning graph approach is also devised. The efficiency of the proposed graph-based approaches over several benchmark schemes is verified via extensive simulations.

We consider resource allocation for offloading computational-intensive tasks in a mobile-edge computing (MEC) system, where each loT device's task can be processed at the MEC server, access points (APs), or locally at loT device itself. The envisioned system has both cooperative APs with a computing capability and multiple radio resource blocks (RRBs) and a MEC server. We aim to study the trade-off between minimizing the energy consumption and maximizing the effective system capacity, which is the number of loT devices with a successful task processing. For this objective, we exploit non-orthogonal multiple access (NOMA) to schedule a set of loT devices to the set of MEC's subcarriers and RRBs of the APs. Due to the intractability of the energy consumption minimization problem, we split it into two sub-problems. The first sub-problem considers the offloading decision and local computation allocation, while the second sub-problem considers the loT device scheduling and power allocation. Leveraging graph-theory, we propose an approach for solving the two sub-problems. Numerical results are presented to depict the trade-off between minimizing the energy consumption and maximizing the effective system capacity of the proposed approach over benchmark schemes.

Distributed machine learning (ML) algorithms are imperative for the next-generation Internet of Things (IoT) networks, thanks to preserving the privacy of users’ data and efficient usage of the communication resources. Federated learning (FL) is a promising distributed ML algorithm where the models are trained at the edge devices over the local data sets, and only the model parameters are shared with the cloud server (CS) to generate global model parameters. Nevertheless, due to the limited battery life of the edge devices, improving the energy-efficiency is a prime concern for FL. In this work, we investigate a resource allocation scheme to reduce the overall energy consumption of FL in the relay-assisted IoT networks. We aim at minimizing the overall energy consumption of IoT devices subject to the FL time constraint. FL time consists of model training computation time and wireless transmission latency. Toward this goal, a joint optimization problem, considering scheduling the IoT devices with the relays, transmit power allocation, and computation frequency allocation, is formulated. Due to the NP-hardness of the joint optimization problem, a global optimal solution is intractable. Therefore, leveraging graph theory, joint near-optimal, and low-complexity suboptimal solutions are proposed. Efficiency of our proposed solutions over several benchmark schemes is verified via extensive simulations. Simulation results show that the proposed near-optimal scheme achieves 6, 4, and 2 times lower energy consumption, respectively, compared to the considered fixed, computation adaptation, and power adaptation schemes. Such an appealing energy efficiency comes at the cost of slightly increased FL time compared to the fixed and computation only adaptation schemes.

In this paper, we leverage reinforcement learning (RL) and cross-layer network coding (CLNC) for efficiently pre-fetching requested contents to the local caches and delivering these contents to requesting users in a downlink fog-radio access network (F-RAN) with device-to-device (D2D) communications. In the considered system, fog access points (F-APs) and cache-enabled D2D (CE-D2D) users are equipped with local caches that alleviate traffic burden at the fronthaul and facilitate rapid delivery of the users’ contents. To this end, the CLNC scheme optimizes the coding decisions, transmission rates, and power levels of both F-APs and CE-D2D users, and RL scheme optimizes caching strategy. A joint content placement and delivery problem is formulated as an optimization problem with a goal to maximize system sum-rate. The problem is an NP-hard problem. To efficiently solve it, we first develop an innovative decentralized CLNC coalition formation (CLNC-CF) switch algorithm to obtain a stable solution for the content delivery problem, where F-APs and CE-D2D users utilize CLNC resource allocation. By considering statistics of channel and users’ content request into account, we then develop a multi-agent RL algorithm for optimizing the content placement at both F-APs and CE-D2D users. Simulation results show that the proposed joint CLNC-CF-RL framework can effectively improve the sum-rate by up to 30%, 60%, and 150%, respectively, compared to: 1) an optimal uncoded algorithm, 2) a standard rate-aware-NC algorithm, and 3) a benchmark classical NC with network-layer optimization.

Cloud radio access networks (C-RANs) are promising paradigms for the fifth-generation (5G) networks due to their interference management capabilities. In a C-RAN, a central processor (CP) is responsible for coordinating multiple Remote Radio Heads (RRHs) and scheduling users to their radio resource blocks (RRBs). In this paper, we develop a novelcross-layer network coding (CLNC)approach that proposes to optimize RRH’s transmit powers and user’s rates in making the coding decisions. As such, cross-layer throughput of the network is maximized. The joint user scheduling, file encoding, and power adaptation problem is solved by designing a subgraph for each RRB, in which each vertex represents potential user-RRH associations, encoded files, transmission rates, and power levels (PLs) for one RRB. It is then shown that the C-RAN throughput maximization problem is equivalent to a maximum-weight clique problem over the union of all such subgraphs, called herein the CRAN-CLNC graph. Numerical results revealed that the proposed joint and iterative schemes offer improved throughput performances as compared to the existing algorithms in the literature. Compared to our proposed joint scheme, our proposed iterative scheme has a certain degradation, roughly in the range of 9%–14%. This small degradation in the throughput performance of the iterative scheme comes at the achieved low computational complexity as compared to the high complexity of the joint scheme.

The device-to-device communication-aided fog radio access network, referred to asD2D-aidedF-RAN, takes advantage of caching at enhanced remote radio heads (eRRHs) and D2D proximity for improved system performance. For D2D-aided F-RAN, we develop a framework that exploits the cached contents at eRRHs, their transmission rates/powers, and previously received contents by different users to deliver the requesting contents to users with a minimum completion time. Given the intractability of the completion time minimization problem, we formulate it at each transmission by approximating the completion time and decoupling it into two subproblems. In the first subproblem, we minimize the possible completion time in eRRH downlink transmissions, while in the second subproblem, we maximize the number of users to be scheduled on D2D links. We design two theoretical graphs, namelyinterference-awareinstantly decodable network coding (IA-IDNC) andD2D conflictgraphs to reformulate two subproblems as maximum weight clique and maximum independent set problems, respectively. Using these graphs, we heuristically develop joint and coordinated scheduling approaches. Simulation results show that the proposed two approaches achieve a considerable performance gain in terms of the completion time minimization.

Fog radio access networks (F-RANs) have recently been postulated as an innovative solution to improve the fronthaul capacities of cloud base stations (CBSs). This architecture extends the CBS service by involving enhanced remote radio heads (eRRHs), which can pre-store and transmit popular files at the network edge (i.e., close to the end users). This is referred to as caching, and it allows the offloading of CBS resources, e.g., time and frequency. Recent works have been proposed to use rate-aware network coding in order to exploit the previously downloaded popular files at the users’ devices. As such, the CBS offloading is maximized. However, the users’ achieved Quality of Service (QoS), and the standard F-RANs physical-layer resource optimization have not received any attention to date. This paper proposes use of an innovative cross-layer network coding (CLNC) to address the above-mentioned issues. The proposed CLNC scheme is not only aware of different users’ rates but also controls the rates by jointly optimizing coding combinations, users-eRRHs/power zones (PZs) assignments, and transmission power in the PZs. Using a graph theoretical representation, we formulate the joint cross-layer CBS offloading and QoS guarantee problem and show its NP-hardness. Joint and iterative heuristic approaches are then developed to solve this problem using greedy vertex search and coloring techniques. The proposed approaches are finally validated and tested against the existing algorithms in the literature.

Cloud radio access networks (C-RAN) gained much attention thanks to their abilities in mitigating interference and providing high data rates by coordinating multiple Remote Radio Heads (RRHs). This paper considers the use of rate aware instantly decodable network coding (RA-IDNC) as a mean to accelerate the broadcast of a set of messages to a set of users in a C-RAN setting. While previous works focus either on rate-unaware IDNC or rate adaptation for traditional single transmitter systems, this paper extends the results to C-RANs. The various ergodic capacities of the different users to the different RRHs bring a new trade-off between the number of scheduled users and the transmission rates. The proposed framework incorporates such information in the network coding decisions, so as the scheduled users, coded messages, and transmission rates reduce the overall completion time. Given the intractability of the problem, the paper proposes relaxing the optimization by an online approach involving an anticipated version of the completion time which allows mapping the possible associations between users, RRHs, coded packets, and transmission rates to vertices in a newly designed graph. Afterward, the online completion time reduction problem is shown to be equivalent to a maximum weight independent set problem over the proposed graph. Simulation results reveal that the proposed scheme achieves substantial performance gain over uncoded and NC rate-unaware algorithms.

In this paper, we consider the use of cross- layer network coding and fog radio access networks (F- RANs) as a means to jointly optimize users quality-of-service and offload the cloud servers and cellular macro base- stations. Multiple edge nodes called enhanced remote radio heads (eRRHs) are connected to a central unit known as cloud base station (CBS). The transmit frame of each eRRH consists of multiple resources blocks called power zones (PZs), each fixed at a pre-assigned power level. The various ergodic capacities of different users at different PZs/eRRHs and the CBS bring a new trade-off between the number of multiplexed users and the transmission rates of each PZ and each CBS allocated channel. The proposed framework incorporates such information in the network coding decisions. As such, the multiplexed users, encoded files, and transmission rates of each PZ/eRRH and each CBS allocated channel maximizes the throughput which is defined as the number of correctly received bits and actual CBS physical-resource offloading, respectively. The problem is first formulated using graph theory techniques, and its intractability is shown. Given the difficulty of the problem, the paper proposes a heuristic approach by dividing it into two sequential subproblems and solving each subproblem efficiently. Presented simulation results reveal that the proposed solution achieves small offloading performance degradation compared to the cross-layer QoS unaware scheme but largely maximizes the received throughput compared to the state-of- art algorithms.

In this paper, we address the problem of reducing the completion time of a radio access network to deliver a frame of messages using Rate Aware Instantly Decodable Network Coding (RA-IDNC). While previous works only considered a single base-station setting, this paper extends the results to a more modern paradigm of networks with multiple coordinated base-stations. The different rates of the base-stations to the various users will be thus incorporated in the network coding decisions, so as to schedule the coded messages and transmission rates jointly in order to reduce the overall completion time. Given the notorious intractability of the completion time reduction problem, the paper uses an online relaxation using an anticipated version of the completion time. This problem is then solved by showing that it is equivalent to a maximum weight independent set problem on a newly designed graph. An efficient multi-layer heuristic is further developed to address this problem in polynomial time. Simulation results suggest that the proposed solution outperforms the uncoded schemes.