Joint Design Problem Research Articles

Maximin Joint Design of Transmit Waveform and Receive Filter Bank for MIMO-STAP Radar Under Target Uncertainties

This letter deals with the joint design of transmit waveform and receive filter bank for airborne multiple-input multiple-output (MIMO) radar under the target uncertainties. Assuming that the spatial angle and the Doppler frequency of the target are unknown, we formulate the maximin joint design problem by maximizing the worst-case signal-to-interference-plus-noise ratio (SINR) under the energy constraint, flexible modulus constraint, and similarity constraint on the transmit waveform. To tackle this problem, we develop a computationally efficient algorithm based on iterative feasible point pursuit successive convex approximation (FPP-SCA). Numerical results are provided to demonstrate the effectiveness and robustness of the proposed algorithm.

Collaborative strategic and tactical planning for one-way station-based carsharing systems with trip selection and vehicle relocation

ABSTRACT Carsharing emerges as a flexible and sustainable transportation mode to deal with severe urban challenges. This paper addresses a joint design problem of carsharing systems by an integer linear programming (ILP) model. Strategic planning of stations’ number, location and parking capacities and tactical planning of the fleet size and initial vehicle distribution are collaboratively optimized by considering all-day vehicle relocation and dynamic trip selection at the operational level. For the first time, vehicle relocation and demand selection are integrated with the joint optimization of strategic and tactical planning. We present a greedy heuristic algorithm embedding ILP to solve the problem. The case study of Chengdu demonstrates that the algorithm is computationally efficient when dealing with large-scale problems. The results identify how vehicle relocation and trip selection work in different scenes of travel time and demands and we conclude operators should target at medium-long trips by discount or promotion.

Liner shipping network - transaction mechanism joint design model considering carbon tax and liner alliance

As a typical system to reduce costs and improve efficiency, the liner shipping system has an important role in economic globalization. In recent years, with the government’s strengthening of carbon emission control and the popularity of liner alliances, the decision-making problems faced by liner companies have become more complicated. Motivated by this setting, this paper investigates a liner shipping network - transaction mechanism joint design problem (LSTD). The government’s levy of carbon taxes on liner companies and liner alliance cooperation strategies are considered. To solve the abovementioned problem, a biobjective optimization model is established. The model takes the total profit of the alliance and the fairness of the distribution of alliance profit as two objectives and optimizes the alliance liner shipping network design scheme (i.e., composition of the fleet, calling sequence of the ports on the routes, and sailing speed of ships) and the alliance transaction mechanism (i.e., slot transaction price among members). To solve the model, the biobjectives are set with different priorities to transform the model into a two-stage model. A genetic algorithm is designed and embedded into the inverse optimization model. Numerical experiments are performed based on China’s actual import/export transportation data. Two sensitivity analyses for different freight rates and carbon tax rates are conducted. The experimental results show that the proposed model and algorithm can effectively solve the LSTD and provide necessary decision support for the operation of the liner alliance.

Intelligent Reflecting Surface-Assisted Multi-Antenna Covert Communications: Joint Active and Passive Beamforming Optimization

This article investigates the intelligent reflecting surface (IRS)-aided multi-antenna covert communications. In particular, with the help of an IRS, a favorable communication environment can be established via controllable intelligent signal reflection, which facilitates the covert communication between a multi-antenna transmitter (Alice) and a legitimate full-duplex receiver (Bob) in the existence of a watchful warden (Willie). In order to shelter the desired communication, Bob generates jamming signals with a varying power to confuse Willie. The beamforming vector employed by Alice and the passive phase shifts of the IRS are optimized jointly to maximize the covert rate under the constraints of the successful detection probability at Willie and the communication outage experienced by Bob. We focus on the worst case by characterizing the minimum successful detection probability at Willie. The formulated problem is non-convex, due to the coupling between the beamforming vector of Alice and the phase shifts of the IRS, and the unit modulus constraint on the phase shifts of the IRS. To tackle the above issues, we first employ the penalty dual decomposition (PDD) method to handle the coupling effect. After that, we apply the successive convex approximation (SCA) method to develop an iterative algorithm for locating a Karush-Kuhn-Tucker (KKT) solution of the joint design problem. Moreover, we show that our proposed iterative algorithm can be adapted to handle the multi-antenna Willie case. Simulation results validate the effectiveness of the proposed iterative algorithm and show the great potential brought by the IRS for covert communications.

A bimodal transit system for large cities: cost efficiency and environment friendliness

This paper proposes a bimodal urban transit system with point-to-point transit (PPT) services considering the emission. PPT is designated to serve passengers with long travel distances, connecting two transport hubs with few intermediate stops. We focus on the bimodal system of FRT and PPT, formulating the joint design problem as a mixed-integer program through a parsimonious continuum model. The relative location between FRT and PPT stations varies over space, which complicates route choice analysis. We overcome the challenge by approximating the costs to access transit stations at an aggregate origin-destination level. This is validated by Monte Carlo simulation. Our findings show that the proposed system is both more cost-efficient and eco-friendly than the regular system in cities with high travel demand. However, its performance deteriorates as demand drops. Such a demand threshold is systematically examined in cities with different features such as geographical sizes, PPT speed, and income levels.

Joint Design of the Transmit and Receive Beamforming via Alternating Direction Method of Multipliers for LPI of Frequency Diverse Array MIMO Radar in the Presence of Clutter

In view of the limitation of the phased-array on suppressing the range-dependent interference, a joint design of the transmit and receive beamforming via the Alternating Direction Method of Multipliers (ADMM) method for Low Probability of Intercept (LPI) of Multiple-Input Multiple-Output with Frequency Diverse Array (FDA-MIMO) radar in the presence of clutter is proposed. The problem of joint design is to maximize the performance of target parameter estimation, and minimize the transmit energy at the target region which enhances LPI capability. Following a weighted sum of the performance metric, the original problem is firstly recasts to a multiple-ratio Fractional Programming (FP) problem. Subsequently, an iterative algorithm is developed. Concretely, at each iteration, the transmit beamforming matrix is optimized by employing ADMM method and the quadratic approximation algorithm. Moreover, the computational complexity of the proposed algorithm is discussed. Numerical simulations are provided to demonstrate the effectiveness of the proposed algorithm.

Wireless Index Coded Transmission by Spatial Multiplexing With Multiple Antennas

Index coding, a.k.a. coded multicasting, is one of the promising transmission schemes that can reduce the number of transmissions by leveraging the stored data in the users' memory. With multiple antennas, index coding gain can be further multiplied by spatially multiplexing but joint design of index code and beamforming has not been addressed well in literature. This paper figures out the gain of spatially multiplexed index code by jointly designing the multicast beamformer and index code. Since the optimal joint design problem itself is NP-hard, we take a two-step approach. At the first step, for an arbitrary index code, conjugate gradient-based multi-group multicast beamforming is designed. At the second step, the index code minimizing the total transmission time is selected. Our simulation results exhibit a substantial gain of spatially multiplexed index code by the proposed scheme.

Joint design of transmit beamspace and reduced‐dimension receiver filter for MIMO radar STAP

In this article, the problem of the transmit beamspace (TB) matrix design in multiple-input multiple-output (MIMO) radar is considered. To reduce the computational burden and to focus the energy within optimal spatial sector, a joint design method of the TB matrix and a reduced-dimension space-time adaptive processing (STAP) filter is proposed based on the maximum signal-to-clutter-plus-noise ratio (SCNR). Firstly, the signal model of the reduced-dimension STAP for TB-based MIMO radar is established. Then, using the maximum SCNR as the design metric, the joint design model of the TB matrix and the reduced-dimension STAP filter is presented. Furthermore, the cyclic optimisation method is exploited to deal with the non-convex joint design problem with the energy constraint. Finally, simulation experiments demonstrate the joint design method of the TB matrix and the STAP filter not only has better output SCNR performance but also owns lower computational burden when compared with the other existing methods.

Computationally efficient evolutionary optimisation for joint reservoir design-operations

This study contributes to emerging computationally efficient methods to account for multi-purpose reservoir operations at the dam design stage. In many cases, the water supply reliability objective introduces binary variables into an otherwise simpler linear programming (LP) formulation. This has led to widespread use of evolutionary methods such as particle swarm optimisation (PSO). Combining the branch and bound (BB) method with a hybrid PSO–LP, this study presents a new BB–PSO–LP algorithm to solve the joint design and operations problem. The proposed BB–PSO–LP algorithm was benchmarked against classical versions of the BB algorithm and PSO–LP. Superiority of the BB–PSO–LP algorithm was demonstrated by finding slightly better solutions in a shorter time. That said, PSO–LP also led to acceptable results in a similar time. Although the BB algorithm was found to be efficient in problem solving, under certain conditions it is difficult for the BB method to find a feasible solution.

Joint Dimension Assignment and Compression for Deterministic Parameter Vector Estimation in Distributed Multisensor Networks

This article considers distributed estimation of an unknown deterministic parameter vector in a bandwidth constrained multisensor network with a fusion center (FC). Due to the stringent bandwidth requirements, each sensor compresses its observation as a low-dimensional vector via a linear transformation. Then, the FC linearly combines all received compressed data to estimate the deterministic parameter vector based on the best linear unbiased estimator. The problem of interest is to jointly design the dimension assignment (i.e., the compression dimension of each sensor) and the corresponding compression matrix when the total number of compression dimensions is given. Such a joint design problem is formulated as an optimization problem with rank and linear matrix equality constraints, which is shown to be NP-hard for the first time. In addition, penalty decomposition (PD), successive quadratic upper-bound minimization method of multipliers (SQUM-M), and SQUM-M-block coordinate descent (SQUM-M-BCD) are proposed to solve it approximately. Furthermore, we show that any accumulation point of the sequence generated by the PD satisfies the Karush-Kuhn-Tucker conditions of the equivalent formulation of the joint design problem; the SQUM-M admits the same convergence property as the PD under some conditions. Numerical experiments corroborate the merits of PD and SQUM-M-BCD as compared with existing strategies for the heterogeneous scenario, and further illustrate the effectiveness of the proposed algorithms for the correlated noise case.

Intelligent Reflecting Surface Assisted Mobile Edge Computing for Internet of Things

This letter studies the impact of an intelligent reflecting surface (IRS) on computational performance in a mobile edge computing (MEC) system. Specifically, an access point (AP) equipped with an edge server provides MEC services to multiple Internet of Thing (IoT) devices that choose to offload a portion of their own computational tasks to the AP with the remaining portion being locally computed. We deploy an IRS to enhance the computational performance of the MEC system by intelligently adjusting the phase shift of each reflecting element. A joint design problem is formulated for the considered IRS assisted MEC system, aiming to optimize its sum computational bits and taking into account the CPU frequency, the offloading time allocation, transmit power of each device as well as the phase shifts of the IRS. To deal with the non-convexity of the formulated problem, we conduct our algorithm design by finding the optimized phase shifts first and then achieving the jointly optimal solution of the CPU frequency, the transmit power and the offloading time allocation by considering the Lagrange dual method and Karush-Kuhn-Tucker (KKT) conditions. Numerical evaluations highlight the advantage of the IRS-assisted MEC system in comparison with the benchmark schemes.

Non-Orthogonal Wireless Backhaul Design for Cell-Free Massive MIMO: An Integrated Computation and Communication Approach

In cell-free massive multiple-input-multiple-output system with wireless backhaul, the distributed access points (APs) and the center processing unit (CPU) are connected via wireless links. Hence, the limited backhaul bandwidth becomes a critical challenge to uplink transmission. To save the bandwidth while maintaining high transmission accuracy, we propose to deploy non-orthogonal transmissions in backhaul link and jointly optimize the detection computation mappings at the APs and the CPU under the non-orthogonal backhaul. First, we formulate the joint design problem subject to backhaul bandwidth constraint aiming at a better end-to-end transmission accuracy. Then, the non-trivial problem is parameterized and solved with a novel model-driven deep neural network, where wireless backhaul is integrated as a neural computing layer by exploiting the reciprocity between non-orthogonal transmission and additive operation. Evaluations show that, the proposed integration method outperforms the conventional approaches by a margin in both backhaul bandwidth cost and the symbol error rate.

Refined Mathematical Model of the Stress State of Adhesive Lap Joint: Experimental Determination of the Adhesive Layer Strength Criterion

A new mathematical model of adhesive joint was developed and experimentally validated, in which the Vlasov–Pasternak two-parameter elastic bed model was used to simulate the stress state of the bonding layer. According to this elastic model, the adhesive layer was regarded as a membrane located in the middle of the adhesive layer thickness, between which and the bearing layers there were elastic elements. The outer bearing layers were regarded as beams in the Timoshenko approximation. The tangential stresses were considered to be constant across the thickness of the adhesive layer, and the normal (cleavage) stresses to be variable. This approach allows one to describe different types of boundary conditions for tangential stresses in the adhesive layer at the ends of the adhesive line, viz.: there are no tangential stresses at the edge of the adhesive joint if there are no adhesive spews, or the tangential stresses reach a maximum at the edge of the adhesive line if these spews exist. The problem was reduced to a system of ordinary differential equations in displacements and rotation angles of bearing layers. The system was solved by the matrix method. It was found that the sagging of the adhesive greatly reduced the maximum cleavage stresses in the adhesive layer. Tensile tests of adhesive lap-jointed metal rods were carried out. The proposed model was used to evaluate the strength criterion of the adhesive layer. It was shown that the best approximation of calculated and experimental data was provided by the maximum principal stress criterion. The proposed approach can be used to solve joint design problems.

Joint User Grouping, Scheduling, and Precoding for Multicast Energy Efficiency in Multigroup Multicast Systems

This paper studies the joint design of user grouping, scheduling (or admission control) and precoding to optimize energy efficiency (EE) for multigroup multicast scenarios in single-cell multiuser MISO downlink channels. Noticing that the existing definition of EE fails to account for group sizes, a new metric called multicast energy efficiency (MEE) is proposed. In this context, the joint design is considered for the maximization of MEE, EE, and scheduled users. Firstly, with the help of binary variables (associated with grouping and scheduling) the joint design problem is formulated as a mixed-Boolean fractional programming problem such that it facilitates the joint update of grouping, scheduling and precoding variables. Further, several novel optimization formulations are proposed to reveal the hidden difference of convex/ concave structure in the objective and associated constraints. Thereafter, we propose a convex-concave procedure framework based iterative algorithm for each optimization criteria where grouping, scheduling, and precoding variables are updated jointly in each iteration. Finally, we compare the performance of the three design criteria concerning three performance metrics namely MEE, EE, and scheduled users through Monte-Carlo simulations. These simulations establish the need for MEE and the improvement from the system optimization.

Joint Trajectory Design and Resource Allocation for Secure Transmission in Cache-Enabled UAV-Relaying Networks With D2D Communications

With the exponential growth of data traffic, the use of caching and device-to-device (D2D) communication has been recognized as an effective approach for mitigating the backhaul bottleneck in unmanned aerial vehicle (UAV)-assisted networks. In this article, we investigate the issue of secure transmission in a cache-enabled UAV-relaying network with D2D communications in the presence of an eavesdropper. Specifically, both UAVs and D2D users are equipped with cache memory, which can prestore some popular content to collaboratively serve users. Considering the fairness among users, we formulate an optimization problem to maximize the minimum secrecy rate among users, by jointly optimizing the user association and UAV scheduling, transmission power, and UAV trajectory over a finite period. The joint design problem is a nonconvex mixed-integer programming problem. To efficiently solve this problem, we propose an alternating iterative algorithm based on the block alternating descent and successive convex approximation methods. Specifically, the user association and UAV scheduling, UAV trajectory, and transmission power are optimized alternately in each iteration, and the convergence of the algorithm is proven. Extensive numerical results show that the proposed joint design scheme significantly outperforms other benchmark schemes in terms of the secrecy rate.

Optimized joint LiFi coordinated multipoint joint transmission clustering and load balancing for hybrid LiFi and WiFi networks

Hybrid light fidelity (LiFi) and wireless fidelity (WiFi) networks provide a promising solution for future indoor wireless communication. Hybrid networks face the challenge of imbalanced cell load due to the uneven distribution of user equipment (UE), and they need to suppress inter-cell interference induced by the dense deployment of LiFi access points (APs). In this paper, with the aim to efficiently establish UE-AP associations and allocate resources, we propose a potential game (PG) aided design of joint user-centric LiFi coordinated multipoint (CoMP) joint transmission (JT) clustering and load balancing (LB). In our design, we formulate the joint design problem as a mixed-integer nonlinear programming (MINLP) problem, which integrates the requirement for the minimum data rate of each UE. With the aim to maximize the system throughput under proportional fairness constraints, we propose to build a PG model to solve the MINLP problem. Then we derive the Nash equilibrium based on the best-response dynamics, and thus a suboptimal solution is attained. Thanks to the space diversity gain brought by our joint LiFi CoMP-JT clustering and LB design, the signal-to-interference-plus-noise ratio can be improved. Thus, when the physical layer configurations remain the same, hybrid networks can achieve larger capacity and better reliability performances. Simulation results validate that hybrid networks using our presented design would achieve better throughput and satisfaction performances than benchmarks.

Energy efficiency optimization in MIMO heterogeneous wireless powered communication networks

This paper studies the energy efficiency (EE) optimization problem in multiple-user multiple-input–multiple-output heterogeneous wireless powered communication network (HWPCN) in which the users in small cells can harvest energy from the small cell base stations in downlink and use harvested energy for uplink transmission. We consider the joint design problem of wireless energy transmission and wireless information transmission precoding matrices subject to the transmit power constraints at each BS and each user. Since the optimization problem under consideration is highly nonlinear non-convex fractional programming in design variables, it is mathematically challenging to obtain the optimal solutions. To tackle with this difficulty, we employ the difference of convex programming to obtain the concave lower bound of the achievable sum rates. Then, we apply the Dinkelbach approach to develop an efficient iterative algorithm in which the convex optimization problems are solved. Simulation results are provided to investigate the EE of the proposed algorithm in HWPCNs.

Robust cognitive transmit waveform and receive filter design for airborne MIMO radar in signal-dependent clutter environment

This paper investigates the robust cognitive joint design of transmit waveform and receive filter to improve the system performance of airborne multiple-input multiple-output (MIMO) radar. Considering the target Doppler frequency and spatial cone angle uncertainties are present, we formulate the averaged signal-to-clutter-plus-noise ratio (SCNR) as the optimization goal. Specifically, the averaged SCNR is maximized under only constant modulus constraint and that under constant modulus and similarity constraints. Four iterative optimization algorithms are developed to deal with the joint design problem. The first kind of iterative optimization algorithm is based on semidefinite programming (SDP) relaxation and randomization or rank-one decomposition techniques. The second kind of computational efficient iterative optimization algorithm utilizes the fractional programming and power method-like iteration. The proposed algorithms can achieve a monotonic output SCNR enhancement and are robust against the inaccuracies of target parameters. Several simulations results are implemented to validate the superiority of the proposed algorithms considering the output SCNR, space-time beampattern and computational complexity.

Joint design of interference alignment and power splitting in SWIPT networks

In this study, the authors investigate the joint design problem of interference alignment (IA) and power splitting (PS) in simultaneous wireless information and power transfer (SWIPT) networks. The existing joint design method is generally divided into two steps. First, IA is performed, and then the PS ratio is optimised. To obtain IA and SWIPT, a new joint design multi-objective alternating optimisation (MOAO) algorithm is proposed. Different from the existing joint design method, the proposed MOAO algorithm not only maximises the sum harvested power after PS, but also maximises the sum signal-to-intereference-plus-noise ratio (SINR) by jointly designing the transmitting precoding matrices, the receiving interference suppression matrices, and the PS ratio. For this multi-objective optimisation problem, a weighted optimisation method is proposed, which compromises between maximising the sum harvested power and maximising the sum SINR. Simulation validates that the sum achievable rate and sum harvested power of the proposed MOAO algorithm are significantly superior to those of the current scheme under the reasonable weighting coefficients.

Joint BATS Code and Periodic Scheduling in Multihop Wireless Networks

Batched sparse (BATS) code is a promising technology for reliable data transmission in error-prone environments. A BATS code consists of an outer code and an inner code. It has been shown that a well-designed inner code is critical to the performance of BATS. Though great efforts have been made in the design of inner codes, the impact of scheduling (that determines when inner codes take place at nodes in the path) on inner codes is still unclear. In this paper, we study the joint design of inner code and scheduling in multihop wireless networks. We first introduce a new network utility from which to associate inner codes with scheduling, and formulate the coding constraint and the scheduling constraint using independent sets of a graph. With the utility, the joint design problem is then transformed to a network utility maximization problem under the constraints. We next exploit the property of the proposed optimization problem and reveal the relation between the expected batch transfer matrix rank and maximal independent sets. In the light of their relationship, we propose joint coding and scheduling rules and show that a periodic scheduling can be used to achieve provable performance guarantees. However, under most realistic coding settings, the proposed coding and scheduling problem is NP-hard. In order to meet the practical needs of the implementation, we develop greedy algorithms that attempt to iteratively improve the current best solution. Numerical results show that our algorithms enables us to approach the utilization of multi-hop wireless networks with a relatively low end-to-end delay.