Nonconvex utility-based power allocation for cognitive radio MIMO system over fading channels

Cognitive Radio Network is a form of communication where licensed frequency band of the Primary users (PUs) are made available to the Secondary user (SU) with constraint interference to the PUs. In this work, we have investigated a novel model considering interweave approach for spectrum access, with multiple primary users and single secondary user (SU). Multiple antennas have been considered at both the primary users as well as the secondary users. The activity of primary users are modeled as Poisson process. In addition, we also propose a new approach of sensing for a MIMO cognitive radio network using energy detection. The proposed method provides a closed form expression for probability of detection (P d ) and probability of false alarm (P f ) in a Multiple Input Multiple Output (MIMO) channel. Further, the throughput of secondary user as well as interference on primary users caused due to secondary user transmissions, has been computed for the proposed model.

Generalized gradient projection neural network models are proposed to solve nonsmooth convex and nonconvex nonlinear programming problems over a closed convex subset of ℝ n . By using Clarke’s generalized gradient, the neural network modeled by a differential inclusion is developed, and its dynamical behavior and optimization capabilities both for convex and nonconvex problems are rigorously analyzed in the framework of nonsmooth analysis and the differential inclusion theory. First for nonconvex optimization problems, the quasiconvergence results similar to those achieved by previous work are proved. For convex optimization problems, the global convergence results are proved, i.e. any trajectory of the neural network converges to an asymptotically stable equilibrium point, which is an optimal solution of the primal problem whenever it exists. This result remains valid even if the initial condition is chosen outside the feasible set. In addition, an asymptotic control result involving a Tykhonov-like regularization shows that any trajectory of the revised neural network can be forced to converge toward a particular optimal solution of the primal problem. Finally, two simulation optimization algorithms for solving the optimization and control problems are designed respectively, and three typical simulation experiments are illustrated to present the accuracy and efficiency of theoretical convergence results of this paper.

Ant Colony Algorithm is a novel stochastic optimization algorithm using artificial ants releasing pheromone on the path, characterized with a positive feedback, distributed computation and parallel algorithm. It has strong robustness and is easy to combine with other methods in optimization. The slow step of convergence and being easy to be trapped in local optimum is the most shortcomings, although it is widely applied in optimization problems. Based on Ant Colony Algorithm using gridding method and combined with chaos theory, Chaos- Ant Colony Algorithm is put forward. In the Chaos- Ant Colony Algorithm, some MAX-MIN Ant System idea is used to limit the pheromone remained in the path. Improvements are made in initialization and update of pheromone. The feasibility of the proposed Chaos- Ant Colony Algorithm is validated by experiments. The writers of papers should and must provide normalized electronic documents in order for readers to search and read papers conveniently.

This thesis describes the analytical design and the performance analysis of optimum receivers for Multiple Input - Multiple Output (MIMO) fading channels. In particular, a novel Optimum Receiver for separately-correlated MIMO channels is proposed. This novel pilot-aided receiver is able to process jointly the pilot symbols, transmitted within each time frame as a preamble, and the information symbols and to decode the transmitted data in a single step, avoiding the explicit estimation of the channel matrix. The optimum receiver is designed for the following two scenarios, corresponding to different transmission schemes and channel models: 1) Narrowband Rician fading MIMO channel with spatial separate correlation; 2) MIMO-OFDM Rician fading channel with space and frequency separate correlation. For each system the performance of the optimum receiver is studied in detail under different channel conditions. The optimum receiver is compared with: - the ideal Genie Receiver, knowing perfectly the Channel State Information (CSI) at no cost; - the standard Mismatched Receiver, estimating the CSI in a first step, then using this imperfect estimate in the ideal channel metric. Since the optimum receiver requires the knowledge of the channel parameters for the decoding process, an estimation algorithm is proposed and tested. Moreover, a complexity analysis is carried out and methods for complexity reduction are proposed. Furthermore, the narrowband receiver is tested in realistic conditions using measured channel samples. Finally, a blind version of the receiver is proposed

In genetic engineering, developing a breed with a desired trait is a search and optimization problem that sometimes requires many generations of field and laboratory experiments for an optimal solution to be found. The nature of the problem requires that a stochastic optimization algorithm be applied in the metaheuristic search rather than using a deterministic or mathematical approach. In the search for drought-tolerant cowpea, this study applied a genetic algorithm as a predictive analytics tool in the genetic engineering of three native cowpea landraces (Dan muzakkari, Gidigiwa, and Dan mesera) selected from Northern Nigeria (specifically from Kontagora in Niger State of Nigeria). The three cowpea species were subjected to mutagenic treatments using gamma irradiation and Ethyl Methane Sulphonate (EMS). Doses applied include 200, 400, 600, and 800 Gray of gamma irradiation and 0.372% v/v of EMS. Both treated and untreated cowpea landraces were planted and observed. Mutation-induced breeding aims to deepen the drought-tolerant trait of the cowpea mutants to survive conditions in drought-prone Northern Nigeria. The statistical analysis of the agro-morphological and yield parameters of the first mutant generation (M1 generation) indicates that mutagenic treatments have a positive impact on both the yield and the survival of the three landraces as all the treated landraces yielded better than the control, particularly the treatments combination of 600gray and 372% v/v of EMS. Also, the predictive outcomes of the computational simulation that was implemented in Python programming indicate that these local cultivars are developing drought-tolerant genetic variability. For the three computational experiments, the stochastic optimizer (genetic algorithm) converged at the 9412th, 9717th, and 14338th generations respectively. Such predictive analytics information is useful for guiding decision-making by researchers and breeders in the crop improvement program.

Time varying acceleration coefficients particle swarm optimisation (TVACPSO): A new optimisation algorithm for estimating parameters of PV cells and modules

In this letter, the ergodic capacity maximization problem is first studied in the fading cognitive multiple-input multiple-output (MIMO) Z-interference channel (ZIC), where the interference from the primary transmitter (PT) to the secondary receiver (SR) is omitted. With the statistical channel state information (CSI) feedback, we find the optimal and the low complexity sub-optimal power strategies for fading cognitive MIMO ZIC to maximize its ergodic capacity under total power constraint, interference power constraint and rank constraint. Simulation results show that the proposed sub-optimal scheme is very efficient and almost achieves the optimal performance.

Contributors

Stochastic optimization algorithms — an application to pattern matching

Multiple Input Multiple Output (MIMO) technology is a technique that can be used to overcome multipath fading. The multipath fading is caused by signals coming from several paths that experience different attenuations, delays and phases. In a multipath condition, an impulse that sent by the transmitter, will be received by the recipient not as an impulse but as a pulse with a spread width that called delay spread. Delay spread can cause intersymbol interference (ISI) and bit translation errors from the information received. To determine the effect of delay spread on the MIMO system, then MIMO system performance research was performed on flat fading and frequency selective fading channels using the Space Time Block Code (STBC) coding technique. This research was conducted using MatLab 2018a software. The simulation results show that the MIMO STBC system performance on flat fading channels is better than the MIMO STBC system performance on the frequency selective fading channel. This result is analyzed based on the value of BER vs. Eb/No and eye diagram.

Particle swarm optimization (PSO) is a swarm intelligence based computer algorithm that is used to find a solution in the search space of an optimization problem. Adding frequency diversity, through subcarrier redundancy, in orthogonal frequency-division multiplexing (OFDM) is a popular approach to improve the robustness of the system. However, frequency redundant OFDM system is prone to high peak-to-average power ratio (PAPR), due to the fact that the same source information is transmitted on multiple subcarriers. Existing schemes such as Selective Mapping (SLM) and partial transmit sequence (PTS) are effective but difficult to implement due to the high computation complexity. In this paper, we propose a two stage PAPR reduction method. We analyze the computational complexity and extensive simulations on the PAPR and show that our scheme considerably reduces the computational complexity while achieving similar PAPR reduction as SLM and better PAPR reduction than PTS.

Range gate pull-off (RGPO) jamming is an electronic countermeasure widely used to fool radar tracking systems. Nevertheless, the research on its strategy optimization has only been tepid. Optimization model, appropriate performance metrics, as well as efficient algorithms are required to further the research in this field. The algebraic description for the objective function of the optimization of the RGPO jamming strategy is difficult to obtain, and the jamming results are not deterministic under the influence of the input noise. To address these issues, this paper models the generation of the RGPO jamming strategy as a stochastic simulation optimization (SSO) problem, and proposes an optimization algorithm of the RGPO jamming strategy with the help of SSO technologies. We propose the committee based active learning (CAL) assisted optimal computing budget allocation based particle swarm optimization (CALPSO-OCBA) to alleviate the competition between solution space search and candidate solution performance evaluation by embedding CAL into PSO-OCBA. To improve the feasibility of the proposed algorithm, a scoring scheme that does not rely on the internal knowledge of the radar tracking system (i.e., tracking model, tracking method and tracking parameters) is designed. In addition, we use four most widely used tracking problems as the benchmarks for testing the proposed optimization algorithm of the RGPO jamming strategy. Experimental results demonstrate that the proposed algorithm is highly competitive in solving the optimization problem of the RGPO jamming strategy.

Solving non-convex economic load dispatch problem via artificial cooperative search algorithm

An important transmitter adaptation technique used in multiple-input multiple-output (MIMO) communication systems is precoding based on the dominant right-singular vectors of the channel matrix. These vectors are typically estimated at the receiver, quantized, and fed-back to the transmitter via a low-rate feedback channel. We present a method for designing an efficient low-rate quantizer for the dominant right singular vectors of a MIMO channel matrix, which exploits the memory in the slow-varying channel fading process. A memory-based (recursive) quantizer is considered which quantizes the time-trajectory of points representing the channel subs-spaces on a Grassmannian manifold. The quantizer codebook is optimized with respect to an objective functions which can be directly related to MIMO channel capacity. The main contribution of this paper is the derivation of a stochastic gradient-based algorithm for codebook optimization. Numerical results obtained with channel simulations are presented, which demonstrate the MIMO channel capacity improvement achieved by using the codebooks optimized as proposed in this paper.

Convex reformulations for self-optimizing control optimization problem: Linear Matrix Inequality approach