Joint Power Allocation for Radar and Communication Co-Existence

A cognitive radio (CR) network refers to a secondary network operating in a frequency band originally licensed/allocated to a primary network consisting of one or multiple primary users (PUs). A fundamental challenge for realizing such a system is to ensure the quality of service (QoS) of the PUs as well as to maximize the throughput or ensure the QoS, such as signal-to-interference-plus-noise ratios (SINRs), of the secondary users (SUs). In this paper, we study single-input multiple output multiple access channels (SIMO-MAC) for the CR network. Subject to interference constraints for the PUs as well as peak power constraints for the SUs, two optimization problems involving a joint beamforming and power allocation for the CR network are considered: the sum-rate maximization problem and the SINR balancing problem. For the sum-rate maximization problem, zero-forcing based decision feedback equalizers are used to decouple the SIMO-MAC, and a capped multi-level (CML) water-filling algorithm is proposed to maximize the achievable sum-rate of the SUs for the single PU case. When multiple PUs exist, a recursive decoupled power allocation algorithm is proposed to derive the optimal power allocation solution. For the SINR balancing problem, it is shown that, using linear minimum mean-square-error receivers, each of the interference constraints and peak power constraints can be completely decoupled, and thus the multi-constraint optimization problem can be solved through multiple single-constraint sub-problems. Theoretical analysis for the proposed algorithms is presented, together with numerical simulations which compare the performances of different power allocation schemes.

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.

We consider power adaptation strategies for binary phase-shift keying signals in Rayleigh fading channels under the assumption that channel state information is provided at both the transmitter and the receiver. We first derive a closed-form expression for the optimal power adaptation that minimizes average bit-error rate (BER) subject to average and peak transmission power constraints. Then, we analyze the average BER for channel inversion power adaptation with the same constraints. Our results show that the performance difference between the optimal power adaptation and the channel inversion becomes negligibly small as available average transmission power increases and/or peak-to-average power ratio decreases. We also find that an optimal peak-to-average power ratio exists that minimizes the average BER in the channel inversion scheme.

This paper considers a two user Gaussian multiple-input single-output (MISO) broadcast channel with a per-antenna peak power constraint (or simply peak power constraint). It is more realistic to consider the peak power constraint on each transmit antenna because each antenna is equipped with its own power amplifier in many practical implementations. Assuming the perfect channel state information (CSI) at the transmitter, we propose an achievable scheme using a dirty-tape coding (DTC). The uniform input in a fixed range of the DTC scheme helps to control the peak power of the transmit signal easily. We also present an optimization algorithm that finds the capacity achieving beamforming vectors and power allocations under a per-antenna average power constraint used in our achievable scheme. Simulation results show that as the transmit power increases, the achievable rate region under the peak power constraint is getting close to the capacity region under the reduced per-antenna average power constraint by 1/3. Compared to a non-DTC scheme based on minimum mean square error (MMSE) beamforming, the proposed scheme performs better.

In this paper, we characterise the joint interference alignment (IA) and power allocation strategies for a K-user multicell multiple-input multiple-output (MIMO) Gaussian interference channel. We consider a MIMO interference channel with blind-IA through staggered antenna switching on the receiver. We explore the power allocation and feasibility condition for cooperative cell-edge (CE) mobile users (MUs) by assuming that the channel state information is unknown. The new insight behind the transmission strategy of the proposed scheme is premeditated (randomly generated transmission strategy) and partial cooperative CE MUs, where the transmitter is equipped with a conventional antenna, the receiver is equipped with a reconfigurable multimode antenna (staggered antenna switching pattern), and the receiver switches between preset T modes. Our proposed scheme assists and aligns the desired signals and interference signals to cancel the common interference signals because the received signal must have a corresponding independent signal subspace. The capacity for a K-user multicell MIMO Gaussian interference channel with reconfigurable multimode antennas is completely characterised. Furthermore, we show that the proposed K-user multicell MIMO scheduling and K-user L-cell CEUs partial cooperation algorithms elaborate the generalisation of K-user IA and power allocation strategies. The numerical results demonstrate that the proposed intercell interference scheme with partial-cooperative CE MUs achieves better capacity and signal-to-interference plus noise ratio (SINR) performance compared to noncooperative CE MUs and without intercell interference schemes.

We consider the problem of the joint design of transmit and receive beamforming (TRBF) vectors for integrated sensing and communication systems in the signal-dependent interference scenario. The dual-functional base station can communicate with downlink users and detect targets simultaneously. By adopting the signal-to-interference-plus-noise ratio (SINR) as performance metrics for both radar and communication, we propose the joint design of the TRBF strategy to manifest the balance of radar and communication in both perfect and imperfect channel state information scenarios. Meanwhile, the upper bound performance of the SINR for target detection is analyzed. Numerical results show the SINR trade-offs between target detection and user communication.

In this paper, a joint power and subcarrier allocation scheme in single-cell uplink OFDMA systems is proposed based on game theory. Our goal is to maximize the power efficiency of each user under peak power constraint. For the purpose, a utility function is designed which can enhance the rate per joule of energy consumed greatly, and the existence and uniqueness of the Nash equilibrium of the function is proved. To bring Pareto improvement, and to overcome the far-near effect, the pricing scheme is introduced. The simulation results show that the power efficiency of the proposed algorithm increases greatly over that of the MaxRt+WF (maximal marginal rate subcarrier and waterfilling power allocation) and MaxFA+WF (fixed subcarrier allocation and waterfilling power allocation).

Diversity-multiplexing tradeoff for cooperative MIMO radar and communications system

The integration of optical satellite links in the next-generation networks and in the fifth generation cellular systems has been proposed in order to guarantee the handling of the extreme data traffic growth and the high-pitched demand for networks’ resources. The optical satellite communication downlink is studied and more specifically, a geostationary satellite with multiple transmitters and an optical ground station with multiple receiving terminals are considered. In this contribution a novel power allocation methodology is proposed for the downlink. The allocation methodology takes into account the scintillation effects due to atmospheric turbulence and maximizes the ergodic network capacity under total expected power and peak power constraints. The analytical optimizing schemes are based on convex optimization methods and have been inspired by waterfilling algorithm. We present emulated power allocation results using real experimental downlink data from ARTEMIS optical satellite campaign and then we investigate the performance of the proposed algorithm with extended numerical results and comparison with other allocation policies. In particular, the new power allocation strategy achieves the highest spectral efficiency, according to the power constraints, for various channel conditions and attenuation profiles and also surpasses two simple baseband allocation methods by intelligently taking advantage of the number of channels and the total expected power.

This paper develops an amplify-and-forward relaying scheme for multiuser wireless cooperative networks under frequency-selective block-fading. Single-carrier frequency division multiple-access with frequency-domain equalization technique is employed at both the relay and destination to combat the inter-block and inter-symbol interference caused by multipath propagation. With the assumption that the full channel state information (CSI) is available at the destination, the relay only knows the uplink channels while no CSI is available at the sources, two power allocation schemes are developed for the relay: (i) to minimize the total transmit power at the relay while maintaining the signal-to-interference-plus-noise ratio (SINR) for each user at the destination above a certain level, and (ii) to maximize the worst SINR among all the users subject to a constraint on total relay power. In the first problem, it is shown that SINR adaptation is needed not only to guarantee a feasible solution but also to significantly reduce the transmit power at the relay for certain channel conditions. In the second problem, a flexible multi-level water-filling scheme is developed, which can be easily modified to adapt to the different channel conditions as well as different quality-of-service provision strategies.

In wireless communication systems with multiple users, the near-far effect and the cochannel interferences decrease the spectral efficiency and system performance. An efficient known method to combat these destructive effects is to properly adjust the transmit powers. A practical and efficient way for power control (PC) in cellular communication systems is the autonomous distributed PC (DPC). In this method, the transmit power of each link is independently modified based on the received signal-to-interference-plus-noise ratio (SINR) reported from the other side of that link. It is known that in a perfect scenario where the reported SINRs are exact, this algorithm converges to the optimum transmit powers. However, in the case of erroneous SINR reports, the transmit powers deviate from their optimum values. This paper addresses the destructive effect of inaccurate SINR reports on the performance of a DPC algorithm. Then, independent variable step-sizes that update the transmit powers are proposed, and convergence of the proposed DPC algorithm to the optimum solution of the original problem is analytically shown. Additionally, a protocol to adaptively tune the step-sizes in our algorithm, which improves the performance of PC in the presence of inaccurate SINR reports, is proposed. Computer simulations are used to study the performance of the proposed method in terms of both the speed of converges and the steady-state accuracy of link qualities.

Spectrum sharing between license holders (primary users) and unlicensed secondary users has been proposed as a solution to frequency wastage. Anyhow, the coexistence among these two types of users should not affect the quality of service of primary users. In this paper, we propose an interference control approach when one primary and multiple secondary users are simultaneously transmitting on the same frequency band. We first evaluate the Signal to Interference-plus-Noise Ratio (SINR) of the primary user in the presence of multiple secondary transmissions. Assuming a constraint on the primary SINR, we explicitly compute the probability that this SINR exceeds a certain threshold in the case of cohabitation. These precise probabilities are afterwards used in the transitions between different states of the Continuous Time Markov Chain that we proposed to model the system. A new model with additional outage probabilities is then built, leading to do a cross layer design of dynamic spectrum access, by considering both the traffic queuing problem and the channel outage in physical layer. Finally, some performance metrics of the system are analyzed, namely the primary throughput, the mean number of secondary users accessing the spectrum, and the secondary deprivation rate.

Interference modelling is an important problem necessary to design and deploy wireless communication systems. Orthogonal frequency division multiplexing (OFDM) is presented as the access technique to be used for future communication systems. Therefore, it is crucial to define a quick and reliable way of estimating signal to interference plus noise ratio (SINR) for multicarrier systems. The most used approach is based on the power spectral density where the out-of-band radiation of the interfering signal is computed. In this paper, we show the inaccuracy of this technique and evaluate its deviation compared to the exact value. We also derive tight lower bound expression for the SINR evaluation. We finally validate the expression through simulation results. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .

A substantial fraction of the energy demand of buildings comes from air-conditioners (ACs), refrigerators, etc., which do not need human interaction for their continuous operations. So long as desirable temperature levels, which we refer to as thermal comfort-bands, are maintained by such background loads, users will not be concerned about when they perform their assigned functions. i.e., when they consume the energy required to function. This paper addresses the problem of maintaining thermal comfort-bands associated with background loads under Peak energy consumption constraint. Based on our empirical observations pertaining to energy consumption profiles of thermal comfort-band maintaining ACs, a) we present a feasibility criterion for maintaining the thermal comfort-band of a given set of background loads under constraints on the peak available power and (b) we propose TCBM, a new algorithm for scheduling such background loads under peak power constraint. In addition to limiting peak power demand, the TCBM algorithm avoids undesirable switching (ON and OFF) of electrical appliances to improve efficiency of the equipment and reduce failures. Results from simulation and real-life implementation demonstrate that our algorithm is superior to the existing work on background load scheduling. We also show how TCBM can adapt to changes in ambient parameters and provide the basis for efficient demand-response systems.

The Internet of Things (IoT) is a significant branch of the ongoing advances in the Internet and mobile communications. Yet, the use of a large number of IoT devices can severely worsen the spectrum scarcity problem. The usable spectrum resources are almost entirely occupied, and thus, the increasing demands of radio access from IoT devices cannot be met. To tackle this problem, the Cognitive Internet of Things (CIoT) has been proposed. In a CIoT network, secondary users, i.e., sensors and actuators, can access the licensed spectrum bands provided by licensed primary users (such as cellular telephones). Security is a major concern in CIoT networks. However, the traditional encryption method at upper layers (such as symmetric and asymmetric ciphers) may not be suitable for CIoT networks since these networks are composed of low-profile devices. In this paper, we address the security issues in spectrum-leasing-based CIoT networks using physical layer methods. Considering that the CIoT networks are cooperative in nature, we propose to employ cooperative jamming to achieve secure transmission. In our proposed cooperative jamming scheme, a certain secondary user is employed as the helper to harvest energy transmitted by the source and then uses the harvested energy to generate an artificial noise that jams the eavesdropper without interfering with the legitimate receivers. The goal is to minimize the Signal to Interference plus Noise Ratio (SINR) at the eavesdropper subject to the Quality of Service (QoS) constraints of the primary traffic and the secondary traffic. We formulate the minimization problem into a two-stage robust optimization problem based on the worst-case Channel State Information of the Eavesdropper (ECSI). By using Semi-Definite Programming (SDP), the optimal solutions of the transmit covariance matrices can be obtained. Moreover, in order to build an incentive mechanism for the secondary users, we propose an auction framework based on the cooperative jamming scheme. The proposed auction framework jointly formulates the helper selection and the corresponding energy allocation problems under the constraint of the eavesdropper's SINR. By adopting the Vickrey auction, truthfulness and individual rationality can be achieved. Simulation results demonstrate the effective performance of the cooperative jamming scheme and the auction framework.