Distributed Power Allocation for Spectral Coexisting Multistatic Radar and Communication Systems Based on Stackelberg Game

This paper investigates the problem of low probability of intercept-based optimal power allocation (LPI-OPA) for an integrated multistatic radar and communication system, which consists of multiple transmitters operating at different frequencies, a radar receiver, and a communication receiver (CR). The integrated multistatic radar and communication system is capable of fulfilling the requirements of both radar and communication subsystems. The key tenet of the integrated system is to minimize the total power consumption by optimizing the transmit power allocation at each transmitter for radar waveforms and information signals, which is constrained by a predetermined target detection performance for the RR and a desired information rate for the CR. Since the analytical closed-form expression of the probability of detection is not tractable, its upper bound is derived. We analytically show that the resulting optimization problem can be reformulated as two subproblems, which can be solved by an efficient solution procedure based on the approach of linear programming and the Karush–Kuhn–Tuckers optimality conditions. Simulation results are provided to show that the LPI performance of the integrated multistatic radar and communication system can significantly be enhanced by employing our proposed LPI-OPA scheme.

In this paper, by considering an imperfect detection environment, a joint node selection and power allocation (JNPA) strategy for multi-static radar (MSR) system with the task of multi-target tracking (MTT) is proposed. Current resource allocation works are often studied based on an ideal detection precondition that the sensors detect targets with the probability of detection ( PD) equals to 1. It is practically impossible to achieve due to the influence of signal radiation attenuation and fluctuating of target amplitudes. To address this drawback, the proposed JNPA takes the practical case where PD ≤ 1 into consideration. The centralized fusion framework is adopted by the MSR to obtain the global MTT results. Accordingly, the posterior Cramér-Rao lower bound (PCRLB) with PD ≤ 1 is derived and utilized as the tracking performance metric of single target. Then, an overall cost function (OCF) is established to describe the global MTT performance. By utilizing the OCF as the objective function and combining with system resource constraints, the JNPA is formulated as an optimization problem. The optimization problem is non-convex. Next, we propose a two-step method to solve it. Simulation results verify the effectiveness of the proposed JNPA strategy.

In this letter, we investigate the power allocation problem in nonorthogonal multiple access (NOMA) heterogeneous networks (HetNets), where the macrocell base station (MBS) and small cell base station (SBS) compete to maximize their own throughput with minimum rate requirement of each user. The throughput of MBS and SBS is formulated based on the equivalent channel gain, and the power allocation problem is captured as a Stackelberg game. A distributed power allocation algorithm is proposed to achieve the Stackelberg equilibrium. Simulation results demonstrate the convergence of the proposed algorithm. Meanwhile, it is verified that the combination of NOMA and HetNets possesses great potential to improve the spectrum efficiency compared to the orthogonal multiple access based HetNets.

This paper investigates the problem of non-cooperative game theoretic power allocation (NGTPA) for distributed multiple-radar architectures in a spectrum sharing environment, where multiple radars coexist with a communication system in the same frequency band. The primary objective of the multiple-radar system is to minimize the power consumption of each radar by optimizing the transmission power allocation, which is constrained by a predefined signal-to-interference-plus-noise ratio requirement for target detection and a maximum interference tolerant limit for communication system. Since each radar is rational and selfish to maximize its own utility, we utilize the non-cooperative game theoretic technique to tackle the distributed power allocation problem. Taking into consideration the target detection performance and received interference power at the communication receiver, a novel utility function is defined and employed as the optimization criterion for the NGTPA strategy. Furthermore, the existence and uniqueness of the proposed game’s Nash equilibrium point are analytically proved. An iterative power allocation algorithm with low computational complexity and fast convergence is developed, where the optimal value of each radar’s transmission power is simultaneously updated at the same time step. Numerical simulations are provided to verify the analysis and evaluate the performance of the proposed strategy as a function of the system parameters. It is shown that the distributed algorithm is effective for power allocation and could protect the communication system with limited implementation overhead.

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

In this paper, we investigate the physical layer security of multiuser multi-input single-output (MU-MISO) communication and colocated multi-input multi-output (MIMO) radar coexistence systems, in which the strong radar signals are exploited as inherent jamming signals to disrupt mali-cious receptions. The transmit beamformers of communication and radar systems are jointly designed to ensure the secure transmission by minimizing the maximum eavesdropping signal-to-interference-plus-noise ratio (SINR) on multiple legitimate users, while satisfying the quality-of-service (QoS) of legitimate transmission, the requirement of radar target detection, and the transmit power constraints of radar and communication systems. An efficient fractional programming (FP) and semi-definite relaxation (SDR) based algorithm is proposed to solve the non-convex optimization problem. Simulation results verify the advancement of the proposed joint transmit beamforming on secure transmission for radar and communication coexistence systems and the effectiveness of the associate design algorithm.

Most resource allocation algorithms are based on interference power constraint in cognitive radio networks. Instead of using conventional primary user interference constraint, we give a new criterion called allowable signal to interference plus noise ratio (SINR) loss constraint in cognitive transmission to protect primary users. Considering power allocation problem for cognitive users over flat fading channels, in order to maximize throughput of cognitive users subject to the allowable SINR loss constraint and maximum transmit power for each cognitive user, we propose a new power allocation algorithm. The comparison of computer simulation between our proposed algorithm and the algorithm based on interference power constraint is provided to show that it gets more throughput and provides stability to cognitive radio networks.

Spectrum-efficient mode selection and fair resource allocation for D2D-enabled uplink/downlink MC-NOMA networks

In this paper, a primary (licensed) user leases part of its resources to independent secondary (unlicensed) terminals in exchange for a tariff in dollars per bit, under the constraint that secondary transmissions do not cause excessive interference at the primary receiver (PRX). The PRX selects a power allocation (PA) for the secondary user that maximizes the secondary rate (and thus its revenue) and enforces it by the following mechanism: Upon violation of a predefined interference level, PRX keeps silencing randomly selected secondary users, until the aggregate secondary interference is below the required threshold. This mechanism ensures that secondary users may not be willing to deviate from the allocated PA. Specifically, the scenario gives rise to a Stackelberg game, in which the primary determines the PA and a Nash equilibrium (NE) constraint is imposed on the PA to ensure that secondary users do not have incentives to deviate, given their knowledge of the silencing mechanism run at the PRX. In principle, the primary should find the set of all PAs that are NE and among them choose the one that maximizes the aggregate secondary utility, and thereby the revenue of the primary. For the most general setting of channel gains, we investigate the conditions for NE for a subset of PAs. When the scenario is symmetric in the sense that all secondary users have the same channel gains in the direct/interfering links, we prove that only two optimal power allocations exist. Finally, for the case of general channel gains with strong interference, we show that there is a unique NE of the game.

This paper investigates the distributed power allocation problem for coordinated multipoint (CoMP) transmissions in distributed antenna systems (DAS). Traditional duality-based optimization techniques cannot be directly applied to this problem, because the non-strict concavity of the CoMP transmission's achievable rate with respect to the transmission power induces that the local power allocation subproblems have non-unique optimum solutions. We propose a distributed power allocation algorithm to resolve this non-strict concavity difficulty. This algorithm only requires local information exchange among neighboring base stations serving the same user, and is thus flexible with respect to network size and topology. The step-size parameters of this algorithm are determined by only local user access relationship (i.e., the number of users served by each antenna), but do not rely on channel coefficients. Therefore, the convergence speed of this algorithm is quite robust to channel fading. We rigorously prove that this algorithm converges to an optimum solution of the power allocation problem. Simulation results are presented to demonstrate the effectiveness of the proposed power allocation algorithm.

We consider a multi-channel cognitive radio network (CRN) where multiple secondary users share a single channel and multiple channels are simultaneously used by a single secondary user (SU) to satisfy its rate requirement. For such an environment, we develop a user-based distributed approach based on a game theoretic formulation. The overall objective is to determine the optimal power and rate (modulation order) distribution choices that each secondary user has to make in order to maintain their quality of service (QoS). Our measure for QoS include bit error rate (BER) and minimum rate requirement. First, we present the resource allocation problem assuming a centralized architecture. Next, we formulate a game considering distributed approach and show that the game has at least one Nash Equilibrium (NE). We present an algorithm to reach NE. Simulation results demonstrate that optimal transmit power follows “reverse water filling” process and rate allocation is proportional to signal to interference plus noise ratio (SINR). We illustrate that the solution from the distributed formulation conforms the centralized solution.

Transmission of the multimedia video streaming has been an important topic in wireless communications. As a metric of video quality at the application layer, peak signal-to-noise ratio (PSNR) is affected by the capacity at the physical layer which can be improved by power controlling and precoding. This paper investigates an optimal power allocation scheme which can maximize the sum PSNR of all users in an SLNR-precoding-based multiuser multiple input single output (MISO) system. The power allocation problem can be transformed into a convex optimization problem when the system works in the high signal to interference plus noise ratio (SINR) region. Performance of the proposed power allocation scheme is evaluated through computer simulation. Results show that, when the system works in the high SINR region, performance improvement in terms of sum PSNR through power allocation is neglectable while the effects of different precoding schemes on the sum PSNR performance are obvious.

In this paper, we consider a case with imperfect channel state information (CSI) in practical cognitive MIMO systems. We first analyze interference to the primary users (PUs) and then propose a joint power allocation and feedback rate control algorithm based on game theory. The utility function of each secondary user (SU) is defined as the corresponding throughput by CSI feedback minus the price as a linear function of feedback rate. Besides, the existence of the Nash equilibrium (NE) is proven. We derive the relationship between power allocation and feedback rate, and an iterative algorithm is proposed to reach the NE. Also, with the purpose of reduce interference and increase system capacity, water-filling (WF) power allocation and zero-forcing (ZF) beamforming are proposed. Simulation results shows that the proposed algorithm is better than equally distributed feedback rate scheme, obviously.

This paper considers the minimization of transmit power in Gaussian parallel interference channels, subject to a rate constraint for each user. To derive decentralized solutions that do not require any cooperation among the users, we formulate this power control problem as a (generalized) Nash equilibrium (NE) game. We obtain sufficient conditions that guarantee the existence and nonemptiness of the solution set to our problem. Then, to compute the solutions of the game, we propose two distributed algorithms based on the single user water-filling solution: The sequential and the simultaneous iterative water-filling algorithms, wherein the users update their own strategies sequentially and simultaneously, respectively. We derive a unified set of sufficient conditions that guarantee the uniqueness of the solution and global convergence of both algorithms. Our results are applicable to all practical distributed multipoint-to-multipoint interference systems, either wired or wireless, where a quality of service in (QoS) terms of information rate must be guaranteed for each link.

The authors study robust beamforming and power allocation in cognitive relay networks using several relays in the secondary link. The channel state information is imperfect modelled by Gaussian random variables. First, minimisation of the total transmit power of relays and the secondary transmitter is considered with a constraint on the interference in the primary receiver. Also, a constraint is assumed on the received signal-to-interference plus noise ratio (SINR) in the secondary receiver. In the other scenario, maximisation of the SINR in the secondary receiver is investigated with a limit on the maximum transmit power of relays and the secondary transmitter. For each non-convex optimisation problem, an iterative and robust algorithm is derived. It is proved that the first proposed algorithm converges to the global optimum. Simulation results similarly show the convergence of both algorithms with a few number of iterations. The outperformance of the robust algorithms is shown compared with a non-robust design.