Successive Approximation Technique Research Articles

Weighted Sum Secrecy Rate Maximization for Joint ITS- and IRS-Empowered System.

In this work, we investigate a novel intelligent surface-assisted multiuser multiple-input single-output multiple-eavesdropper (MU-MISOME) secure communication network where an intelligent reflecting surface (IRS) is deployed to enhance the secrecy performance and an intelligent transmission surface (ITS)-based transmitter is utilized to perform energy-efficient beamforming. A weighted sum secrecy rate (WSSR) maximization problem is developed by jointly optimizing transmit power allocation, ITS beamforming, and IRS phase shift. To solve this problem, we transform the objective function into an approximated concave form by using the successive convex approximation (SCA) technique. Then, we propose an efficient alternating optimization (AO) algorithm to solve the reformulated problem in an iterative way, where Karush-Kuhn-Tucker (KKT) conditions, the alternating direction method of the multiplier (ADMM), and majorization-minimization (MM) methods are adopted to derive the closed-form solution for each subproblem. Finally, simulation results are given to verify the convergence and secrecy performance of the proposed schemes.

Joint resource allocation and user association for multi-cell integrated sensing and communication systems

The densification of the orthogonal frequency division multiplexing (OFDM) based fifth-generation communication systems, as well as the requirement of integrating sensing and communication functionalities, has promoted the development of integrated-sensing-and-communication (ISAC) dense cellular networks (DCNs). In the OFDM-based ISAC-DCN, multiple base stations simultaneously serve mobile users and sense targets based on the echo of downlink communication signals. In this paper, we establish the interference model in ISAC-DCN for sensing and communication. Focusing on the interference management of the ISAC-DCN, we investigate the multi-dimension resource allocation problem. In particular, we aim to maximize the network utility by jointly optimizing sub-band allocation, user association, and transmission power under the sensing signal-to-interference-plus-noise ratio constraint to be solved iteratively. The mixed-integer optimization problem is decoupled into three sub-problems. Specifically, a greedy genetic sub-band allocation scheme is proposed for sub-band allocation to reduce total interference. We employ the successive convex approximation technique to transform the transmission power control sub-problem and solve it via geometric programming. Simulation results illustrate the trade-off between sensing and communication performances, and show that the proposed algorithm significantly improves the network utility and achieves higher detection probability.

Joint resource and trajectory optimization for secure UAV-based relay NOMA system

In this paper, we propose and analyze the maximal secrecy capability of an unmanned aerial vehicle (UAV)-based relay non-orthogonal multiple access system. In this system, a UAV acts as an aerial relay station to assist transmission from a source to two users in the presence of a terrestrial eavesdropper and a friendly jamming UAV. The minimum achievable secrecy rate (MASR) of two users is derived as a metric indicator to quantify the security of the system under the assumption that the location of the eavesdropper is imprecise. In order to maximize MASR, an efficient iterative algorithm is proposed to optimize system parameters, i.e., the trajectory of UAVs, transmit and jamming powers, as well as power allocation factors. Since the objective function and constraints of the total problem are non-convex, the successive convex approximation and block coordinate descent techniques are applied to find efficient approximate solutions. Simulation results demonstrate that the proposed algorithm significantly enhances the MASR of the system compared to benchmark schemes.

The Analytical Methods Of Volterra Integral Equations Of The Second Kind

This paper discussed the analytic methods to solve Second order Volterra integral equations form using different methods. The domain decomposition method is the first technique. Depending on the hypothesis, the solution is by sequence. The second method is the successive approximation technique, which is used Picard iteration method. The third method used Laplace transformation. The modified decomposition technique is used as the fourth method. Depending on the Taylor series, the fifth method is called the series method. The last method solves the VIE using the functional correction technique called the variational iteration method. We introduce some examples to illustrate these methods.

Energy-efficient resource allocation and user grouping for multi-IRS aided MU-MIMO system

In this paper, we consider a multiple-intelligent reflecting surface (IRS)-aided multi-user multiple-input-multiple-output (MU-MIMO) down-link communication system, wherein a base station (BS), equipped with multiple antennas, serves massive single-antenna users. In order to mitigate high inter-user interference, we divide all the users into a single near-user (NU) group and multiple cell-edge user (CEU) groups based on their connection with BS. In particular, we assign a single IRS for each CEU group such that the BS communicates and serves the set of CEUs using their corresponding IRS. Subsequently, we formulate a problem of energy-efficient resource allocation design subject to optimal beamforming design at BS and multiple IRS under given power constraints. Due to the non-convexity of the formulated resource allocation problem, we proposed two different alternating optimization frameworks which solve the joint optimization problem based on 1) minimum mean square error (MMSE) and Riemannian conjugate gradient (RCG) and 2) fractional programming and successive convex approximation techniques for active beamforming vectors at the BS and passive beamforming vectors for IRSs, respectively. Simulation results confirm that the MMSE-RCG-based resource allocation design attains fast convergence when compared to sequential fractional programming (SFP) based solution. Moreover, the MMSE-RCG attains better energy efficiency whereas the SFP renders better spectral performance. Nevertheless, both the proposed solution achieve 70%–80% better performance than random phase-shift settings.

Deep learning-driven multi-objective dynamic switch migration in software defined networking (SDN)/network function virtualization (NFV)-based 5G networks

This paper investigates the problem of switch migration in software defined networking (SDN)/network function virtualization (NFV)-based fifth generation (5G) networks in which SDN controllers are deployed as virtualized network functions (VNFs). The problem of switch migration is how to properly transfer the traffic of some SDN switches to one or multiple other virtualized SDN controllers in order to cope with the traffic fluctuations and the changing network topology. The highly dynamic environment of 5G networks requires the simultaneous consideration of multiple conflicting objectives in this problem including controllers load-balancing and network stability. In this paper, this multi-objective problem is formulated as a mixed integer non-linear program (MINLP) which is computationally expensive to solve. Therefore, a mathematical-based solution method is first provided based on the successive convex approximation (SCA) technique for the single-objective problem in which preference parameters are given to describe associated importance of the objectives. Taking into account the time restrictions for practical developments, an extended deep learning (DL) approach is then proposed to produce multiple mappings over multiple objectives. The verification results show that the proposed multi-objective DL algorithm can generate high quality Pareto Fronts close to those of the state-of-the-art algorithms based on optimization with orders of magnitude speedup in the computational time.

UAV Anti-Jamming Communications With Power and Mobility Control

Unmanned aerial vehicle (UAV)-enabled air-ground integrated communication systems are vulnerable to jamming attack, mainly due to the high probability with line-of-sight wireless channel conditions. Although a few UAV anti-jamming transmission schemes have been recently proposed, the fundamental performance limits by simultaneously considering the UAV and jammer’s mobility have not yet been reported. These observations motivate us to formulate an interactive reward region (IRR) characterization problem for the scenario that the UAV and the jammer compete with each other to maximize their respective rewards via power and mobility control. Then, we propose a transmit and jamming power optimization algorithm to characterize the IRR with power control, by leveraging successive convex approximation technique, where two transmit power design schemes are given in closed-form expressions from a game-theoretic perspective. Furthermore, the IRR with joint power and mobility control is characterized based on alternating optimization, in which both the UAV and jammer can simultaneously adjust their power and trajectories. Finally, extensive simulation results reveal that joint transmit power and trajectory optimization can enlarge the IRR, and increasing the UAV mobility and flight time length improves the UAV reward.

Joint Power Control and Passive Beamforming Design for RIS-Assisted Secure Communication Systems

In this paper, considering the problem of signal blockage in the scenario of dense buildings, we study joint transmit power and phase shift design for the reconfigurable intelligent surface (RIS) assisted secure uplink communication systems. In particular, a friendly user with better channel state information than other users is selected as a jammer to interfere the eavesdropper. We aim to maximize the secrecy rate under the constant-modulus constraint for RIS phase shift. To handle the non-convex problem, we propose an alternating optimization based algorithm, where the successive convex approximation technique and manifold optimization are utilized for the optimization of transmission power and RIS phase shift, respectively. By introduce RIS into this system, the problem of signal blockage is solved. Moreover, simulation results show that the proposed scheme improves the system secrecy rate compared with the other benchmark schemes.

Energy Consumption Minimization of Rotary-Wing UAVs for Data Distribution

In this letter, we investigate a rotary-wing unmanned aerial vehicle (UAV)-supported data distribution system. Specifically, a UAV transmits data to a group of ground users, aiming to minimize the UAV energy consumption via trajectory optimization. To cope with the non-convex optimization problem that involves continuous time variables, we first consider a benchmark “hovering-to-communicate” scheme where the UAV sequentially visits a series of hovering positions to transmit data. In this case, the formulated problem can be transformed into the traveling salesman problem and solved. Next, we consider a generalized “flying-to-communicate” scheme where the UAV sends data to ground users while flying. In this case, the formulated problem can be reduced to a tractable form with a finite number of variables via trajectory discretization, whose approximately optimal solution is derived by successive convex approximation technique. Simulation results demonstrate that the proposed algorithm can effectively reduce the UAV energy consumption.

Routing and Resource Scheduling for Air-Ground Integrated Mesh Networks

Due to the advantage of achieving scalable connectivity and low-latency communications, air-ground integrated mesh networks (AGIMNs) will play an important role in the next generation wireless communication systems. However, due to the heterogeneous character of AGIMNs, it is challenging to manage the network and optimize the communication resources for improving the end-to-end (E2E) performance. Therefore, in this paper, we study a joint routing and time-frequency resource scheduling problem aiming at minimizing the total weighted E2E delay for heterogeneous AGIMNs. To capture the features of this complex system, we mathematically model the network constraints and formulate an optimization problem. To solve the original nonconvex problem, a suboptimal solution is proposed. First, we propose an optimal minimum-weight routing method, in which the hop count, conflict delay, and contention delay are taken into consideration. Then, we transform the time-frequency resource scheduling subproblem into a series of tractable problems for maximizing the number of active links through channel assignment in successive time slots. Finally, the successive convex approximation (SCA) technique is utilized to solve the channel assignment problem per time slot. Extensive simulation results show the performance superiority of the proposed solution compared to the benchmarks in terms of the total E2E delay.

Joint Design of Horizontal and Vertical Polarization Waveforms for Polarimetric Radar via SINR Maximization

For an extended target with different polarimetric response, one way of improving the detection performance is to exploit waveform diversity on the dimension of polarization. In this paper, we focus on joint design of transmit signal and receive filter for polarimetric radars with local waveform constraints. Considering the signal-to-interference-plus-noise ratio (SINR) as the figure of merit to optimize, where the average Target-Impulse-Response Matrix (TIRM) within a certain Target-Aspect-Angle (TAA) interval is employed as the target response, the waveform is decomposed and then designed for both horizontal and vertical polarization segments, subject to energy and similarity constraints. An iterative algorithm is proposed based on the majorization-minimization (MM) method to solve the formulated problem. The developed algorithm guarantees the convergence to a B-stationary point, where in each iteration, optimal horizontal and vertical transmit waveforms are respectively solved by using the feasible point pursuit and successive convex approximation (FPP-SCA) technique. Experiment results show the effectiveness of the proposed algorithm, the robustness of the output SINR against the TAA change, and the advantages of polarization diversity and local design.

Robust Beamforming Design for Cache-Enabled C-RAN With Fronthaul Multicast

In this article, we study a robust beamforming design problem in the cached-enable cloud radio access network, where the imperfect channel state information of the access links are considered. Two transmission schemes, nonsimultaneous transmission (NST) scheme and simultaneous transmission (ST) scheme, are developed. To improve the system performance, the cooperative transmission technique is applied. In NST scheme, the base stations (BSs) obtain the uncached required files from the central processor (CP) and then transmit all required files to users. Based on this, we design an effective transmission model based on cache files and the qualities of the fronthaul link channels, and formulate a transmission delay minimization problem. Then, an alternative optimization algorithm is developed to solve it. In ST scheme, the BSs obtain the uncached files from the CP and transmit the cached files to users simultaneously. We divide two cases according to the access link transmission rate and cached files. For each case, we formulate an optimization problem by jointly designing the fronthaul multicast beamforming and access link transmission beamforming. Next, an iterative algorithm is developed via the L0 norm approximation, successive convex approximation and semidefinite relaxation techniques. Finally, simulation results show the proposed schemes can reduce the transmission delay better than the conventional multicast schemes.

Joint Slot Scheduling and Power Allocation in Clustered Underwater Acoustic Sensor Networks

Dear Editor This letter deals with the joint slot scheduling and power allocation in clustered underwater acoustic sensor networks (UASNs), based on the known clustering and routing information, to maximize the network's energy efficiency (EE). Based on the block coordinated decent (BCD) method, the formulated mixed-integer non-convex problem is alternatively optimized by leveraging the Kuhn-Munkres algorithm, the Dinkelbach's method and the successive convex approximation (SCA) technique. Numerical results show that the proposed scheme has a better performance in maximizing EE compared to the separate optimization methods.

UAV-Aided Vehicular Short-Packet Communication and Edge Computing System Under Time-Varying Channel

In this paper, a novel UAV-aided vehicular edge computing (VEC) network is proposed, where the vehicle and on-board UAV provide multi-access edge computing (MEC) service for the roadside Internet of Things (IoT) devices. In this system, considering the time-varying channel, we derive the lower bound of signal-to-noise ratio (SNR) based on the first-order Gauss-Markov process. Then, with the short-packet transmission, we maximize the total amount of computation by jointly optimizing the communication scheduling, the trajectories of the vehicle and on-board UAV, and the computing resource, subject to the mobility, connection and computation constraints. The formulated optimization problem is a mix-integer non-convex problem. To efficiently solve it, we propose an alternative algorithm based on the Lagrangian dual decomposition and successive convex approximation technique. Extensive simulation results are provided to show the performance gain of the proposed algorithm.

Hybrid Offline-Online Design for Reconfigurable Intelligent Surface Aided UAV Communication

This letter considers the reconfigurable intelligent surface (RIS)-aided unmanned aerial vehicle (UAV) communication systems in urban areas under the general Rician fading channel. A hybrid offline-online design is proposed to improve the system performance by leveraging both the statistical channel state information (S-CSI) and instantaneous channel state information (I-CSI). For the offline phase, we aim to maximize the expected average achievable rate based on the S-CSI by jointly optimizing the RIS’s phase-shift and UAV trajectory. The formulated stochastic optimization problem is difficult to solve due to its non-convexity. To tackle this problem, we propose an efficient algorithm by leveraging the stochastic successive convex approximation (SSCA) techniques. For the online phase, the UAV adaptively adjusts the transmit beamforming and user scheduling according to the effective I-CSI. Numerical results verify that the proposed hybrid design performs better than various benchmark schemes, and also demonstrate a favorable trade-off between system performance and CSI overhead.

Resource-Aware Video Delivery in Fog Radio Access Networks: A Joint QoE and QoS Perspective

In this paper, the video delivery strategy is studied under the cooperative framework of cache-enabled fog radio access networks (F-RAN), where multicast beamforming and video-quality selection are both taken into account in terms of different scenarios. In the user-centered scenario, an optimization problem regarding to the network quality of experience (QoE) improvement is first formulated under the essential quality of service (QoS) requirements of the users. Furthermore, an energy efficiency maximization scheme is proposed in the user-dense scenario to improve the resource utilization when network resources may be insufficient to guarantee the QoS requirements of all the users. In particular, a user scheduling scheme is studied in this case. More specifically, the non-convex constraints and fractional object functions in the aforementioned problems are dealt with by successive convex approximation (SCA) technique and the Dinkelbach approach. In order to solve the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$l_{0}$</tex-math></inline-formula> -norm problem caused by user scheduling, we introduce the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$l_{1}$</tex-math></inline-formula> -norm approximation to improve the sparsity of the solution. Finally, the effectiveness of the proposed schemes is validated by extensive simulations.

Secure Task Offloading for MEC-Aided-UAV System

The promise of unmanned aerial vehicles (UAVs) combined with the mobile edge computing (MEC), named MECaided-UAV extends the MEC application to offer new flexible, low-latency computing services and considerable utilities for pervasive sensing of the world. In MEC, the UAV with limited onboard energy and computation resources needs to offload its tasks to resource-rich ground base stations (GBSs) servers. Because of the broadcast nature of line-of-sight (LoS) channels, one of the key challenges in task offloading is to guarantee that confidential data is offloaded safely to the GBSs without being intercepted by eavesdroppers (Eves). In this MEC-aided-UAV system, the GBSs help the UAV compute the offloaded tasks and transmit the artificial noise (AN) to suppress the vicious Eves.We make the first attempt to study the maximum-minimum average secrecy capacity problem, including joint optimization of the trajectory and transmit power of the UAV, the transmit power of AN, the local computation ratio, and the selection of GBSs with consideration of the practical constraints of completion delay of the tasks, maximum velocity, and the power consumption. The optimization issue is confirmed as a mixed-integer nonconvex problem. Thereafter, a low-complexity iterative algorithm with the block coordinate descent method and successive convex approximation technique is put forward to get its suboptimal solution. In addition, the convergent solution can be achieved by solving the subproblems in turn. Evaluation results validate that the proposed secure offloading scheme significantly effectiveness the baselines by 17.4%-71.2% on the maximum-minimum average secrecy capacity

UAV Placement for VR Reconstruction: A Tradeoff Between Resolution and Delay

Unmanned aerial vehicle (UAV) can be combined with virtual reality (VR) to collect the rapidly changing physical environment by leveraging its high flexibility, so as to provide an immersive experience for metaverse users. In this letter, we study the UAV placement problem in UAV-assisted VR systems where UAVs are deployed to capture images of ground targets and then transmit them to ground users for virtual environment reconstruction. The goal is to minimize the transmission delay while satisfying the image resolution requirement. Firstly, the UAV placement problem is formulated into a non-convex optimization problem. Then, an efficient algorithm is proposed to obtain a high-quality solution by adopting the block coordinate descent and successive convex approximation techniques with low complexity. Finally, the numerical results are provided and show that considerable benefits can be obtained with a suboptimal UAV placement. Specifically, the proposed scheme can reduce the transmission delay by over 15 ms compared to the conventional scheme when the received signal-to-noise ratio at the reference distance is 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> and the resolution requirement is 0.5.

Energy-Efficient RSMA for Multigroup Multicast and Multibeam Satellite Communications

This letter studies energy-efficient rate-splitting multiple access (RSMA) for multigroup multicast and multibeam satellite communication systems. Specifically, we minimize the unmet system capacity, which means the rate difference between the requested user rate and the effective achieved rate, and communication energy consumption by optimizing the power allocation and rate allocation under the per-feed power and sum radiated power constraints. To solve the non-convex optimization problem, auxiliary variables and epigraph method are introduced to transform the original problem into a difference of convex functions programming. Then, the successive convex approximation (SCA) technique is used to obtain a suboptimal solution. Numerical results verify the superiority of our proposed solution compared to the benchmark methods.

URLLC-oriented secure communication for UAV relay-assisted network

In this paper, we investigate the unmanned aerial vehicle (UAV) relay-assisted secure short packet communication. The UAV acts as a decode-and-forward relay to transmit control signals from the source to the actuators in the presence of a ground eavesdropper (EV) whose imperfect channel state information is available at the UAV. Specially, non-orthogonal multiple access is adopted in our work to achieve more connections and improve the fairness of communication and the short packets are employed for data transmission to reduce the latency. Explicitly, we maximize the minimum average secrecy throughput among all actuators by jointly optimizing the UAV trajectory, transmit power and blocklength allocation, which generates a challenging optimization problem. Therefore, we propose an iterative algorithm based on block coordinate descent method and successive convex approximation technique to handle the non-convex problem. Numerical results show that the proposed scheme has better performance compared to the benchmark schemes.