Showing papers by "Yin Sun published in 2012"

Optimal power allocation for two-way decode-and-forward OFDM relay networks

Abstract: This paper presents a novel two-way decode-and-forward (DF) relay strategy for Orthogonal Frequency Division Multiplexing (OFDM) relay networks. This DF relay strategy employs multi-subcarrier joint channel coding to leverage frequency selective fading, and thus can achieve a higher data rate than the conventional per-subcarrier DF relay strategies. We further propose a low-complexity, optimal power allocation strategy to maximize the data rate of the proposed relay strategy. Simulation results suggest that our strategy obtains a substantial gain over the per-subcarrier DF relay strategies, and also outperforms the amplify-and-forward (AF) relay strategy in a wide signal-to-noise-ratio (SNR) region.

Optimal Real-Time Spectrum Sharing Between Cooperative Relay and Ad hoc Networks

Abstract: Optimization based spectrum sharing strategies have been widely studied. However, these strategies usually require a great amount of real-time computation and significant control delay, and thus are hard to be fulfilled in practical scenarios. This paper investigates optimal real-time spectrum sharing between a cooperative relay network (CRN) and a nearby ad hoc network. Specifically, we optimize the spectrum access and resource allocation strategies of the CRN so that the average traffic collision time between the two networks can be minimized while maintaining a required throughput for the CRN. The development is first for a frame-level setting, and then is extended to an ergodic setting. For the latter setting, we propose an appealing optimal real-time spectrum sharing strategy via Lagrangian dual optimization. The proposed method only involves a small amount of real-time computation and negligible control delay, and thus is suitable for practical implementations. Simulation results are presented to demonstrate the efficiency of the proposed strategies.

Capacity of Compound MIMO Gaussian Channels with Additive Uncertainty

Abstract: This paper considers reliable communications over a multiple-input multiple-output (MIMO) Gaussian channel, where the channel matrix is within a bounded channel uncertainty region around a nominal channel matrix, i.e., an instance of the compound MIMO Gaussian channel. We study the optimal transmit covariance matrix design to achieve the capacity of compound MIMO Gaussian channels, where the channel uncertainty region is characterized by the spectral norm. This design problem is a challenging non-convex optimization problem. However, in this paper, we reveal that this problem has a hidden convexity property, which can be exploited to map the problem into a convex optimization problem. We first prove that the optimal transmit design is to diagonalize the nominal channel, and then show that the duality gap between the capacity of the compound MIMO Gaussian channel and the min-max channel capacity is zero, which proves the conjecture of Loyka and Charalambous (IEEE Trans. Inf. Theory, vol. 58, no. 4, pp. 2048-2063, 2012). The key tools for showing these results are a new matrix determinant inequality and some unitarily invariant properties.

Trapping wave forming method of originating broadband signal with variable parameters

Abstract: The invention relates to a trapping wave forming method of originating broadband signals with variable parameters, belonging to the technical field of communication signal treatment. The method comprises the following steps: at a signal transmission end, detecting the number n of useful narrow-band signals in real time and the bandwidth and frequency band distribution of each useful narrow-band signal, constructing n numbered sub-trappers corresponding to the useful narrow-band signals according to the bandwidth and frequency band distribution of the useful narrow-band signals at the transmission end, connecting the n numbered sub-trappers in serial to form a cascade trapper, trapping broadband transmission signals by the cascade trapper to obtain broadband signals with trapping wave corresponding to the frequencies of the useful narrow-band signals, continuously detecting the useful narrow-band signals, and updating the parameters of the trapper if parameters are found to be changed.The method is characterized in that when the narrow-band signal is detected in real time, parameters of a trapper are updated in real time according to detected results, thereby effectively inhabiting the interference of the broadband transmission signals on the narrow-band signal with variable parameters.

Optimal Distributed Power Allocation for Decode-and-Forward Relay Networks

Abstract: This paper presents a fully distributed power allocation al gorithm for decode-and-forward (DF) relay networks with a large number of sources, relays, and destination nodes. The well known mathematical decomposition based distributed optimization techniques cannot directly be applied to DF relay networks, because the achievable rate of DF relaying is not strictly co ncave, and thus the local power allocation subproblem may have non-unique solutions. We resolve this non-strict concavity problem by using the idea of proximal point method, which adds some quadratic terms to make the objective function strictly concave. While traditional proximal point methods require a two-layer nested iteration structure, our proposed algorithm has a single-layer iteration structure , which is desirable for on-line implementation. Moreover, our algorithm only needs local information exchange among the source, relay, and destination nodes of each DF relay link, and can easily adapt to variations of network size and topology. In this paper, we establish the convergence and optimality of our fully distributed single-layer iterative algorithm. Numerical results are provided to illustrate the benefits of our proposed algorithm. Index terms− Decode-and-forward, distributed power allocation, wireless relay network.

Noncoherent amplify-and-forward cooperative OFDM in block fading channels

Abstract: In this paper, we investigate noncoherent detection methods for amplify-and-forward cooperative OFDM systems in frequency-selective block fading channels. In particular, by exploiting the time-frequency relationship of OFDM, a noncoherent detector is proposed for phase-shift keying modulated signals. The proposed noncoherent detector uses only one OFDM block signal for data detection, and therefore is suitable for relatively fast time-varying environments. We show that the noncoherent detection can be cast as a quadratic maximization problem, which can be efficiently solved by the convex approximation technique, namely, semidefinite relaxation. Simulation results demonstrate that the noncoherent detector exhibits near-coherent performance and outperforms channel-estimation-aided detection methods.

Optimal Power Allocation for OFDM-Based Wire-Tap Channels with Arbitrarily Distributed Inputs

Abstract: In this paper, optimal power allocation is investigated for maximizing the secrecy rate of orthogonal frequency division multiplexing (OFDM) systems under arbitrarily distributed input signals. Considering the discrete inputs are used in practical systems rather than the commonly assumed Gaussian inputs, we focus on secrecy rate maximization under more practical finite discrete constellations in this paper. It is known that the secrecy rate achieved by Guassian distributed inputs is concave with respect to the transmission power. However, we prove that the secrecy rate of finite discrete constellations is non-concave, which makes traditional convex optimization methods not applicable to our problem. To address this non-concave power allocation problem, we propose an efficient power allocation algorithm. Its gap from optimality vanishes asymptotically at the rate \(O(1/\sqrt{N})\), and its complexity grows in order of O(N), where N is the number of sub-carriers. Numerical results are provided to illustrate the benefits and significance of the proposed algorithm.