Joint resource and trajectory optimization in a UAV-enabled dual-function radar-communication network

Cognitive radio and heterogeneous wireless networks are important candidate techniques for the fifth generation (5G) communication systems. On the other hand, propagation properties of radio channels in a physical layer are exploited to design efficient secure transmission schemes for 5G wireless networks. In this work, a security-aware joint power and subchannel allocation problem based on the inter-network cooperation is investigated at cognitive heterogeneous networks via imperfect spectral sensing. The security-aware joint power and subchannel allocation is subject to constraints in available subchannels, proportional fairness secrecy transmission rates among secondary mobile terminals (MTs), total interference power threshold, and total power for each secondary MT. In order to determine the impact of imperfect spectral sensing to resource allocation, we first represent the interference power at primary base station in terms of the misdetection probability and false alarm probability. Then, the security-aware power and subchannel allocation problem with inter-network cooperation is formulated as a biconvex optimization problem, and an optimal security-aware power and subchannel allocation algorithm is proposed utilizing the dual decomposition method. Compared to the heuristic power and subchannel allocation algorithms, numerical simulation results show that the proposed algorithms improve the total secrecy throughput and guarantee the proportional fairness among different secondary MTs.

This paper studies the case of an overlay cognitive radio network where the primary user leases spectral resources to the secondary user in exchange for cooperation, considering that both type of users have specific quality of service requirements. We investigate the problem of joint power and time allocation for the secondary access during the cooperative phase, with a view to optimizing the effective capacity of the primary user given an average energy constraint for the secondary user. Afterwards, the optimal power allocation of the secondary user for its own transmission phase is investigated in order to maximize the effective capacity of the secondary link. The proposed joint power and time allocation mechanism is compared with an optimal time/constant power allocation scheme and a less sophisticated baseline allocation scheme, i.e. power allocation under constant time and its superiority is proven for various network parameters. The reference model of one primary---one secondary user is extended to a general multi user cognitive radio network through the proposed pairing mechanism based on matching theory. Particularly, considering the remarks of the reference scenario, we propose two different matching schemes (with/without consideration of primary users' quality of service requirements) and we confirm their superiority compared to other matching mechanisms.

The explosive demands for mobile broadband service bring a major challenge to 5G wireless networks. Device-to-device communication, adopting side links for user-direct communication, is regarded as a main technical source for offloading large volume of mobile traffic from cellular base station. This article investigates the joint power and subcarrier allocation scheme for device-to-device communication in 5G time division duplex systems. In time division duplex system, instead of utilizing an exclusive portion of the precious cellular spectrum, device-to-device pairs reuse the subcarriers occupied by cellular users, thus producing harmful interference to cellular users in both uplink and downlink communication, and strongly limiting the spectrum efficiency of the system. To this end, we focus on the maximization of device-to-device throughput while guaranteeing both uplink and downlink channel quality of service of cellular users as well as device-to-device pairs. The problem is formulated as a mixed integer non-linear programming (MINLP) problem. To make it tractable, we separate the original MINLP problem into two sub problems: power allocation and sub-carrier reusing. The former is to develop optimal power allocation for each device-to-device pair and each cellular user, with the constraints of maximum power and quality of service. It is solved by geometric programming technique in convex optimization method. The latter is derived as a one-to-many matching problem for scheduling multiple subcarriers occupied by cellulars to device-to-device pairs. It is solved by Hungarian method. Simulation results show that the proposed scheme significantly improves system capacity of the device-to-device underlay network, with quality of service of both device-to-device users and cellular users guaranteed.

In this paper, we explore a joint subcarrier assignment and power allocation with statistical delay QoS guarantees for full-duplex OFDMA systems. Effective capacity theory which evaluates wireless channel capacity from a novel view of link layer provides a theory basis for modeling the system capacity under the conditions of delay QoS constrains. After modeling the aggregate effective capacity of the system, we formulate joint subcarrier assignment and power allocation problem as a mixed integer programming problem, whose target is to maximize the aggregate effective capacity. To solve this integer programming problem, we allocate initial power on each subcarrier and decompose it into two subproblems, which are subcarrier allocation problem and power allocation problem respectively. Further, we propose an alternative algorithm to achieve joint subcarrier assignment and power allocation, which can meet the “subcarrier continuity” constraint of uplink resource allocation. Simulations have shown that the performance of our proposed scheme is favorite. Additionally, we investigate the effect of the residual selfinterference, inter-node interference (INI) and statistical delay QoS exponent on the performance of systems.

For the next generation wireless communication system, Multi carrier Code Division Multiple Access (MC-CDMA) has become very appealing scheme due to due to high data rates and interference limiting factors. In this research work we analyzes the capacity of MC-CDMA system using adaptive power and sub-channel allocation under perfect, imperfect and predicted channel state information (CSI). The transmitter and receiver both have been targeted in this paper. The proposed scheme is a novel idea that divides the sub-channels into small groups. Each group is assigned power according to the Eigen values and gain of the each sub-channel in that specific group. The receiver calculates optimal feedback vector for the transmitter by calculating the bit error rates using the above strategy and selects the optimal feedback vector. The simulations show that the usage of joint power and sub-channel allocation outperforms the capacity of the system. The capacity of the system is highly improved using adaptive power and sub-carriers allocation along with CSI prediction. The simulation results have been carried out for other power allocation schemes available in literature and results have been compared with perfect, imperfect and predicted CSI.

This paper investigates the application of multi-agent reinforcement learning (MARL) algorithm to solve the joint spectrum and power allocation problem (JSPAP) in wireless network. The objective of JSPAP is to optimize the subband selection and transmit power levels for links, with the aim of maximizing the sum-rate utility function. To address the JSPAP with discrete subband selection and continuous power allocation, most existing algorithms rely on a centralized optimizer and the instantaneous global channel state information, which can be challenging to implement in large wireless networks with time-varying subbands. To conquer such limitation, a two-stage MARL algorithm is proposed, which comprises a top layer network for selecting subbands across all links and a bottom layer network for determining the transmit power levels for all transmitters. By utilizing the value decomposition technique in the top layer network, the links can cooperatively select transmission subbands, effectively resolving non-stationarity issues in wireless network. Additionally, in the bottom layer network of the proposed two-stage MARL algorithm, each transmitter selects the transmit power level based solely on the local information, thereby effectively reducing computational burden. Empirical experiments demonstrate the effectiveness of the proposed two-stage MARL algorithm by comparison with the state-of-the-art RL algorithms and fractional programming algorithms.

A novel multibeam satellite system design is proposed in this paper based on jointly optimizing power and carrier allocation in order to best match the asymmetric traffic requests. This design introduces higher and asymmetric interference levels throughout the coverage. However, both power and bandwidth will be used more efficiently. Even though the problem of power and bandwidth allocation has been addressed in terrestrial wireless communications, it is new in satellite systems and since architecture and channel are different, available results and algorithms are not applicable to satellite payload systems. In this paper we formulate the resource allocation problem as max-min SINR balancing based on the recently introduced axiomatic-based interference model, but in addition, we also optimize the carrier allocation when performing the SINR balancing problem. An analytical solution for the optimal carrier allocation is proposed and we iteratively find the optimal power allocation for each beam. The Shannon (upper bound) and current state-of-the art PHY layer technology: DVB-S2 are proposed to be implemented in order to obtain the gap between them. Simulation results show significant improvements in terms of power gain, spectral efficiency and traffic matching ratio comparing with conventional system, which is designed based on uniform bandwidth and power allocation.

Join trajectory optimization and communication design for UAV-enabled OFDM networks

The combination of a multihop relay system and orthogonal frequency-division multiplexing (OFDM) modulation is a promising way to increase the capacity and coverage area. For the OFDM two-hop relay system with separate power constraints, joint subcarrier matching and power allocation is considered in this paper, which uses the ldquodecode-and-forwardrdquo relay strategy. The aforementioned problem can be formulated as a mixed binary integer programming problem, which is prohibitive when trying to find the global optimum. By separating the subcarrier matching and the power allocation, the optimal scheme, i.e., the optimal joint subcarrier matching and power allocation, is presented in this paper. After that, a suboptimal scheme with less complexity is also proposed, which can also be used to better understand the effects of power allocation. Simulation results show that the capacity of the optimal scheme is almost equivalent to the upper bound of the system capacity, and the capacity of the suboptimal scheme is close to that of the optimal scheme. In addition, simulation results also show that the one-to-one subcarrier matching is almost optimal, although it simplifies the system architecture.

Fair resource allocation and SBS selection for MC-NOMA-based HetNets supporting D2D communications

We consider two-way amplify-and-forward relaying in a multichannel system with two end nodes and a single relay, using a two-slot multi-access broadcast (MABC) relaying strategy. We investigate the problem of joint subchannel pairing and power allocation to maximize the achievable sum-rate in the network, under an individual power budgets at each node. To solve this challenging joint optimization problem, an iterative approach is proposed to decompose the problem into pairing optimization and joint power allocation optimization, and solve them iteratively. For the joint power allocation at the relay and the two end nodes, we propose another iterative optimization procedure to optimize the power at the two end nodes and at the relay iteratively. By using different forms of optimization parameters, the sum-rate maximization problem turns out to be convex and the optimal solutions can be obtained for each subproblem. The simulation performance demonstrates the gain of joint optimization approach over other pairing-only or power-allocation-only optimization approaches.

Heterogeneous network is a novel network architecture proposed in Long-Term-Evolution~(LTE), which highly increases the capacity and coverage compared with the conventional networks. However, in order to provide the best services, appropriate resource management must be applied. In this paper, we consider the joint optimization problem of user association, subchannel allocation, and power allocation for downlink transmission in Multi-cell Multi-association Orthogonal Frequency Division Multiple Access (OFDMA) heterogeneous networks. To solve the optimization problem, we first divide it into two subproblems: 1) user association and subchannel allocation for fixed power allocation; 2) power allocation for fixed user association and subchannel allocation. Subsequently, we obtain a locally optimal solution for the joint optimization problem by solving these two subproblems alternately. For the first subproblem, we derive the globally optimal solution based on graph theory. For the second subproblem, we obtain a Karush-Kuhn-Tucker (KKT) optimal solution by a low complexity algorithm based on the difference of two convex functions approximation (DCA) method. In addition, the multi-antenna receiver case and the proportional fairness case are also discussed. Simulation results demonstrate that the proposed algorithms can significantly enhance the overall network throughput.

SummaryWe investigate the problem of joint downlink wireless backhaul bandwidth (WBB) and power allocation in heterogeneous cellular networks (HCNs). A WBB partitioning scheme is considered, which allocates the whole bandwidth between the macrocell and small cells for data transmission and backhauling. We formulate an optimization problem to maximize the weighted sum logarithmic utility function by jointly optimizing WBB portion and fronthaul power allocation of each base station with consideration of the backhaul capacity limitation on each small cell. In order to solve this joint optimization problem, we propose a hierarchical two‐level approach and decompose the original problem into two independent subproblems: the WBB allocation at the macrocell base station (MBS) and the power allocation at both the MBS and small cell base stations (SBSs). Accordingly, the optimal WBB portion and power allocation solutions are obtained, respectively. Furthermore, we develop a distributed algorithm to implement the joint WBB and power allocation. Numerical results verify the effectiveness of the proposed approach and analyze the impact of the weighted coefficient and backhaul capacity limitation on the network performance. In addition, significant performance gains can be achieved by the proposed approach over the benchmark.

This paper considers the joint power allocation and subcarrier pairing problem in decode-and-forward (DF) relaying orthogonal-frequency-division multiplexing (OFDM) system, and proposes a strategy, called MW-RA, to maximize the weighted sum rate with total power constraint. MW-RA is formulated as a mixed integer programming problem and then transformed to a convex optimization problem by continuous relaxation, and solved in the Lagrangian dual domain. Simulation results show that MW-RA can maximize the weighted sum rate under total power constraint and outperform equal power allocation (EPA).

Macrocells may suffer serious uplink interference introduced by the deployment of co-channel femtocells. In this paper, an interference-aware pricing-based resource allocation algorithm for co-channel femtocells is proposed to alleviate their interference to macrocells without degrading the femtocell's capacity. The subchannel and power allocation problem is modeled as a non-cooperative game. A suboptimal subchannel allocation algorithm and an optimal power allocation algorithm are proposed to implement the resource allocation game. Simulation results show that the proposed algorithm not only improves the capacity of the macrocell but also the total capacity of the two-tier network, as compared with the unpriced subchannel allocation and Modified Iterative Water Filling (MIWF) power allocation algorithm.