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52
Semidefinite relaxation of quadratic optimization problems
- SIGNAL PROCESSING MAGAZINE, IEEE
, 2010
"... n recent years, the semidefinite relaxation (SDR) technique has been at the center of some of very exciting developments in the area of signal processing and communications, and it has shown great signifi-cance and relevance on a variety of applications. Roughly speak-ing, SDR is a powerful, computa ..."
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Cited by 161 (11 self)
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n recent years, the semidefinite relaxation (SDR) technique has been at the center of some of very exciting developments in the area of signal processing and communications, and it has shown great signifi-cance and relevance on a variety of applications. Roughly speak-ing, SDR is a powerful, computationally efficient approximation technique for a host of very difficult optimization problems. In particular, it can be applied to many nonconvex quadratically constrained quadratic programs (QCQPs) in an almost mechanical fashion, including the following problem: min x[Rn x T
MIMO broadcasting for simultaneous wireless information and power transfer
- IEEE Trans. Wirless Commun
, 2013
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QoS-based transmit beamforming in the presence of eavesdroppers: An artificial-noise-aided approach
- IEEE Tans. Signal Process
, 2011
"... Abstract—Secure transmission techniques have been receiving growing attention in recent years, as a viable, powerful alternative to blocking eavesdropping attempts in an open wireless medium. This paper proposes a secret transmit beamforming approach using a quality-of-service (QoS)-based perspectiv ..."
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Cited by 31 (7 self)
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Abstract—Secure transmission techniques have been receiving growing attention in recent years, as a viable, powerful alternative to blocking eavesdropping attempts in an open wireless medium. This paper proposes a secret transmit beamforming approach using a quality-of-service (QoS)-based perspective. Specifically, we establish design formulations that: i) constrain the maximum allowable signal-to-interference-and-noise ratios (SINRs) of the eavesdroppers, and that ii) provide the intended receiver with a satisfactory SINR through either a guaranteed SINR constraint or SINR maximization. The proposed designs incorporate a relatively new idea called artificial noise (AN), where a suitable amount of AN is added in the transmitted signal to confuse the eavesdroppers. Our designs advocate joint optimization of the transmit weights and AN spatial distribution in accordance with the channel state information (CSI) of the intended receiver and eavesdroppers. Our formulated design problems are shown to be NP-hard in general. We deal with this difficulty by using semidefinite relaxation (SDR), an approximation technique based on convex optimization. Interestingly, we prove that SDR can exactly solve the design problems for a practically representative class of problem instances; e.g., when the intended receiver’s instantaneous CSI is known. Extensions to the colluding-eaves-dropper scenario and the multi-intended-receiver scenario are also examined. Extensive simulation results illustrate that the proposed AN-aided designs can yield significant power savings or SINR enhancement compared to some other methods. Index Terms—Artificial noise, physical-layer secure communica-tions, semidefinite relaxation, transmit beamforming. I.
On Active Learning and Supervised Transmission of Spectrum Sharing Based Cognitive Radios by Exploiting Hidden Primary Radio Feedback
, 2009
"... This paper is concerned with wireless spectrum sharing between a cognitive radio (CR) or so-called secondary radio link and a primary radio (PR) link. Supposing that the PR adapts its transmit power and/or rate upon receiving the interference from the CR and such adaptations are then observed by the ..."
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Cited by 16 (1 self)
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This paper is concerned with wireless spectrum sharing between a cognitive radio (CR) or so-called secondary radio link and a primary radio (PR) link. Supposing that the PR adapts its transmit power and/or rate upon receiving the interference from the CR and such adaptations are then observed by the CR, there usually exists a hidden feedback loop from PR to CR, whereby the CR learns the PR’s deployed transmit strategy without the need of a dedicated feedback channel from the PR. This interesting interaction between PR and CR is exploited in this paper for the design of CR’s learning and transmission. First, this paper proposes a new active learning method for the CR: By initiatively probing the PR with interference and then observing its transmit power/rate adaptations, the CR estimates the CR-to-PR channel gain and utilizes this knowledge to predict the resulting PR’s performance degradations due to different CR’s transmit power levels. Second, with cognitions acquired via the active learning, the CR is proposed to design a supervised transmission to effectively trade off between protecting the PR’s transmission and maximizing the CR’s channel capacity. This paper analyzes the CR’s channel capacity with or without canceling the PR’s interference at the CR receiver, and characterizes the conditions for each case when the CR is able to sustain a capacity increase with transmit power under the PR’s “feedback” interference.
Cognitive energy harvesting and transmission from a network perspective
- in Proc. IEEE Int. Conf. Commun. Syst. (ICCS), 2012
"... Abstract—Wireless networks can be self-sustaining by har-vesting energy from radio-frequency (RF) signals. Building on classic cognitive radio networks, we propose a novel method for network coexisting where mobiles from a secondary network, called secondary transmitters (STs), either harvest energy ..."
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Cited by 8 (1 self)
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Abstract—Wireless networks can be self-sustaining by har-vesting energy from radio-frequency (RF) signals. Building on classic cognitive radio networks, we propose a novel method for network coexisting where mobiles from a secondary network, called secondary transmitters (STs), either harvest energy from transmissions by nearby transmitters from a primary network, called primary transmitters (PTs), or transmit information if PTs are sufficiently far away; STs store harvested energy in rechargeable batteries with finite capacity and use all available energy for subsequent transmission when batteries are fully charged. In this model, each PT is centered at a guard zone and a harvesting zone that are disks with given radiuses; a ST harvests energy if it lies in some harvesting zone, transmits fixed-power signals if it is outside all guard zones or else idles. Based on this model, the spatial throughput of the secondary network is maximized using a stochastic-geometry model where PTs and STs are modeled as independent homogeneous Poisson point processes (HPPPs), under the outage constraints for coexisting networks and obtained in a simple closed-form. It is observed from the result that the maximum secondary throughput decreases linearly with the growing PT density, and the optimal ST density is inversely proportional to the derived transmission probability for STs. I.
Max-min SINR coordinated multipoint downlink transmission–duality and algorithms
- IEEE Trans. Signal Process
, 2012
"... Abstract—This paper considers the max-min weighted signal-to-interference-plus-noise ratio (SINR) problem subject to multiple weighted-sum power constraints, where the weights can represent relative power costs of serving different users. First, we study the power control problem. We apply nonlinear ..."
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Cited by 8 (3 self)
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Abstract—This paper considers the max-min weighted signal-to-interference-plus-noise ratio (SINR) problem subject to multiple weighted-sum power constraints, where the weights can represent relative power costs of serving different users. First, we study the power control problem. We apply nonlinear Perron-Frobenius theory to derive closed-form expressions for the optimal value and solution and an iterative algorithm which converges geometrically fast to the optimal solution. Then, we use the structure of the closed-form solution to show that the problem can be decoupled into subproblems each involving only one power constraint. Next, we study the multiple-input-single-output (MISO) transmit beamforming and power control problem. We use uplink-downlink duality to show that this problem can be decoupled into subproblems each involving only one power constraint. We apply this decoupling result to derive an iterative subgradient projection algorithm for the problem. Index Terms—Beamforming, multiple-input-multiple-output (MIMO), uplink-downlink duality.
Interference Management in Cognitive Radio Systems — a Convex Optimisation Approach
"... Abstract—We consider a cognitive radio system with N sec-ondary user (SU) pairs and a pair of primary users (PU). The SU power allocation problem is formulated as a rate maximisation problem under PU and SU quality of service and SU peak power constraints. We show our problem formulation is a geomet ..."
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Cited by 5 (1 self)
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Abstract—We consider a cognitive radio system with N sec-ondary user (SU) pairs and a pair of primary users (PU). The SU power allocation problem is formulated as a rate maximisation problem under PU and SU quality of service and SU peak power constraints. We show our problem formulation is a geometric program and can be solved with convex optimisation techniques. We examine the effect of PU transmissions in our formulations. Solutions for both low and high signal-to-interference-and-noise ratio (SINR) scenarios are provided. We show that including the PU rate in the optimisation problem leads to increased PU performance while not significantly degrading SU rate. Achievable rate cumulative distribution functions for various Rayleigh fading channels are produced. I.
Robust MIMO Cognitive Radio Systems Under Interference Temperature Constraints
- IEEE Journal on Selected Areas in Communications
, 2013
"... Abstract—Cognitive Radio (CR) systems are built on the coexistence of primary users (PUs) and secondary users (SUs), the latter being allowed to share spectral resources with the PUs but under strict interference limitations. However, such limitations may easily be violated by SUs if perfect SU-to-P ..."
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Cited by 4 (3 self)
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Abstract—Cognitive Radio (CR) systems are built on the coexistence of primary users (PUs) and secondary users (SUs), the latter being allowed to share spectral resources with the PUs but under strict interference limitations. However, such limitations may easily be violated by SUs if perfect SU-to-PU channel state information (CSI) is not available at the secondary transmitters, which always happens in practice. In this paper, we propose a distributed design of MIMO CR networks under global interference temperature constraints that is robust (in the worst-case sense) against SU-to-PU channel uncertainties. More specifically, we consider two alternative formulations that are complementary to each other in terms of signaling and system performance, namely: a game-theoretical design and a social-oriented optimization. To study and solve the proposed formulations we hinge on the new theory of finite-dimensional variational inequalities (VI) in the complex domain and a novel parallel decomposition technique for nonconvex sum-utility problems with coupling constraints, respectively. A major contribution of this paper is to devise a new class of distributed best-response algorithms with provable convergence. The algorithms differ in computational complexity, convergence speed, communication overhead, and achievable performance; they are thus applicable to a variety of CR scenarios, either cooperative or non-cooperative, which allow the SUs to explore the trade-off between signaling and performance.
MIMO transceiver designs for spatial sensing in cognitive radio networks
- IEEE Trans. Wireless Commun
, 2011
"... Abstract—We propose transceiver algorithms in cognitive ra-dio networks where the cognitive users are equipped with multi-ple antennas. Prior work has focused on the design of precoding matrices to suppress interference to the primary receivers. This work considers designs of precoding and decoding ..."
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Cited by 4 (2 self)
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Abstract—We propose transceiver algorithms in cognitive ra-dio networks where the cognitive users are equipped with multi-ple antennas. Prior work has focused on the design of precoding matrices to suppress interference to the primary receivers. This work considers designs of precoding and decoding matrices for spatial sensing to achieve two objectives: i) to prevent interference to the primary receivers and ii) to remove the interference, due to primary transmissions, at the secondary receiver. With single antenna primary terminals and two antenna cognitive terminals, a linear transceiver design has been introduced under a global channel state information (CSI) assumption [1]. In this letter, multiple antenna primary and cognitive terminals and three different CSI scenarios depending upon the amount of CSI are studied: i) local CSI, ii) global CSI, and iii) local CSI with side information. When local CSI is available, we leverage prior work and employ the projected-channel singular value decomposition (P-SVD). In the global CSI scenario, we propose a joint transmitter-receiver design under the assumption of full CSI of all the users at the secondary transceiver. To reduce the feedback overhead, we also propose a new iterative algorithm that exploits only local CSI with side information. In this algorithm, the secondary transmitter and receiver iteratively update precoding and decoding matrices based on the local CSI and side information (precoding/decoding matrices at the previous iteration step) to maximize the rate of the secondary link while maintaining the zero-interference constraint. Convergence is established in the special case of single stream beamforming. Numerical results confirm that the proposed joint design and the iterative algorithm show better achievable rate performance than the P-SVD technique at the expense, respectively, of CSI knowledge and side information. Index Terms—MIMO, cognitive radios, spectrum sharing, projection matrix. I.
Jointly optimal sensing and resource allocation for multiuser overlay cognitive radios,” arXiv preprint arXiv:1211.0954
, 2012
"... Abstract—Successful deployment of cognitive radios requires efficient sensing of the spectrum and dynamic adaptation of the available resources according to the sensed (imperfect) informa-tion. While most works design these two tasks separately, in this paper we address them jointly. In particular, ..."
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Cited by 3 (2 self)
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Abstract—Successful deployment of cognitive radios requires efficient sensing of the spectrum and dynamic adaptation of the available resources according to the sensed (imperfect) informa-tion. While most works design these two tasks separately, in this paper we address them jointly. In particular, we investigate an interweave cognitive radio with multiple secondary users that access orthogonally a set of frequency bands originally devoted to primary users. The schemes are designed to minimize the cost of sensing, maximize the performance of the secondary users (weighted sum rate), and limit the probability of interfering with the primary users. The joint design is addressed using nonlinear optimization and dynamic programming, which is able to leverage the time correlation in the activity of the primary network. A two-step strategy is implemented: it first finds the optimal resource allocation for any sensing scheme and then uses that solution as input to solve for the optimal sensing policy. The two-step strategy is optimal, gives rise to intuitive optimal policies, and entails a computational complexity much lower than that required to solve the original formulation. Index Terms—Cognitive radios, sequential decision mak-ing, dual decomposition, partially observable Markov decision processes. I.
