A new hybrid heuristic multiuser detector for DS‐CDMA communication systems

The paper presents a study of the application of the noncoherent digital matched filter (DMF) detector, with multibit quantisation of the input signal and impulse response, to a digital correlator receiver used in CDMA applications with 'structured code' sequences. The use of structured code sequences with an optimum matched filter produces small crosscorrelation magnitudes. However, such an optimum filter requires a multilevel impulse response and its implementation as a DMF detector requires that the impulse response be quantised and digitised, thereby increasing the filter complexity. In the present study an optimum DMF detector with structured code sequences is evaluated and compared with an equivalent analogue matched filter detector using the signal to noise ratio (SNR) at the correlator output as a performance measure. The results are used to identify the minimum amount of input signal and impulse particular load-sharing response quantisation required in order to reduce circuit complexity while maintaining an acceptable system performance. >

Energy detection (ED) technique is a vastly used sensing technique in CRNs because of its operational simplicity. However at low SNR, the performance of ED is badly degraded. Matched Filter (MF) detection is an alternate sensing technique at low SNR, as it increases SNR of the received signal. MF detector gives far better performance when compared to ED at low SNR. But the problem with MF detector is that it must have priori knowledge about Primary User (PU) signal, therefore we need dedicated MF detector for each PU. Motivated by above drawback of ED and MF in this paper we proposed a new MF technique by which requirement of priori knowledge about PU signal can be eliminated as well as performance at low SNR is improved. At the MF detector front end, we perform blind estimation of PU signal parameters and accordingly update the coefficient of MF transfer function. Blind Estimation of signal parameters solves the problem of having priori information about PU signal for MF detector. Performance analysis and comparison of ED, conventional MF detector and proposed MF detector also have been done in this paper which show that the proposed MF detector perform better than ED and almost same as the conventional MF detector.

This paper introduces an adaptive ( /spl mu/ + /spl lambda/ ) evolutionary algorithm (EA) for multiuser detection (MUD) in direct sequence code division multiple access (DS-CDMA) systems. The EA based multiuser detector takes the output of parallel interference cancellation (PIC) detector as the initial population and adopts maximum likelihood decision rule to detect the user bit sequences. During an EA running, the offspring population size is adaptively adjusted according to the current success probability. The major advantage of the adaptive EA based multiuser detection scheme is that it obtains rather good bit error rate (BER) performance with lower computational complexity. Monte Carlo simulation results show the EA based multiuser detector can always converge to the optimal solution with a small number of generations. Numerical analysis shows its computational time is polynomial complexity and less than other heuristic search algorithms.

Optimum multiuser detection (OMD) in direct-sequence code-division multiple access (DS-CDMA) communication systems is a combinatorial optimization problem and has been proven NP-complete. Many heuristics have been presented to solve this problem, but few of them consider the fitness landscape of OMD carefully. In this paper, we analyze the fitness landscape of OMD, including the neighborhood structure and the distribution of local optima. Numerical results give hints on how to design efficient heuristic algorithms for the problem. A meta-heuristic algorithm considering the analysis results is proposed With a proper local search and a well-chosen perturbation strategy, the proposed algorithm can find the (near) optimal solution rapidly with lower computational complexity. Simulation results show it outperforms other heuristic multiuser detection algorithms when the number of users is large. In the condition of small number of users, it can achieve the bit error rate (BER) bound of OMD.

A heuristic search algorithm is given that determines and resolves the Hamiltonian circuit problem in directed graphs. The heuristic information of each vertex is a set composed of its possible path length values from the starting vertex, which is obtained by the path length extension algorithm. A detailed analysis of algorithm examples shows that the heuristic algorithm can greatly reduce the number of processing nodes compared to the backtracking algorithm without information; meanwhile, the algorithm’s time complexity is related to the number of loops in the directed graph. If a directed graph G(V, E) only contains loops that contain the starting point, then the Hamiltonian problem of G can be determined in polynomial time. The algorithm is implemented using C++ and Python programming, and all of the test graphs are generated by the random graph algorithm. The experiments show that the heuristic algorithm can reduce the number of extended nodes by at least 10% in directed random graphs with no or only a few loops. This heuristic algorithm can save time in applications that perform tasks while extending nodes because it reduce the number of extended nodes.

The multiuser detection is a signal processing tech- nique which enhances the performance and capacity in DS- CDMA technology. Since its complexity issue was too expensive to handle, there was focus on the suboptimal detectors. In this paper we propose a hybrid method called adaptive differential interference cancellation method (ADIC) which is a hybrid of the differential interference cancellation method and which is then combined in a adaptive way to determine a weight matrix which will map the outcome of the differential interference cancellation method to come out with a better performance. This paper also looks into the computational cost of different methods using the floating point operation (flops) which is a good estimation of the comparative computational cost if not the exact one. I. INTRODUCTION The conventional DS-CDMA match filter detector fails to combat these issues due three prominent drawbacks: unrealis- tic ucorrelated code, near-far problem (even with strict power control (1)), and tough multiple access interference (MAI). Many advanced signal processing approaches have been made to improve the performances of these kinds of detectors. In this paper we will study the different approaches that were made in this field and implement these methods to reach a conclusion about the detection methods. As the name of the topic suggests the focus of our work will be study and propose cost effective better performing multiuser detection method. We will explore Multiuser Detection Techniques in Synchronous CDMA environment. Multi-user detection deals with the development and ap- plication of joint demodulation and interference cancellation cancellation techniques for improved detection of a desired set of digital signals in the presence of multiple access interfer- ence, intersymbol interference and noise. Since veterbi forward dynamic algorithm (2) is used in case of Optimum multiuser detection (3) the complexity grows in with the increase in the number of users. Hence the complexity is immense which increases exponentially with the increase in the number of users. Looking ahead for lower complexity hence lesser cost of implementation, the paper proposes the use of sub-optimal linear detectors (4). The sub-optimal detector the paper next focuses on is Decorrelator type of Detector (5) which is optimal in least squares. This detector out performs the optimal detector in many respects. Then the Minimum Mean Square Error (MMSE) linear detector is proposed which tries to minimize the squared error from the required transformation. It is one of the most popular detectors in use due to its easiness to implement and its lower complexity. But then in the above detection methods the decisions made were based on certain linear transformation criterion and there are missing efforts to check the performance in between the detection process to improvise the transformation method better detection. So then the paper proposes Adaptive multiuser detection. Then the paper focuses on Interference cancellation methods which utilize the feedback to reduce Multiple Access Interference (MAI) in the detected bits. Under the Interference Cancellation umbrella the Successive Interference Cancellation (SIC) (6), (7) method is explored and simulated to observe the results. Then the next method which performs better than SIC and more commonly used in real life, called Parallel Interference Cancellation (PIC) (6), (8) is also implemented. Before going ahead to implement the final proposed detection method, this paper implements Differential Interference Cancellation (DIC) (9) method. And then this paper finally proposes an Inter- ference Cancellation method called the Adaptive Differential Interference Cancellation Multiuser Detection method which is cost effective in comparison to other Interference Cancellation methods as we will require lesser hardware (multipliers as in the SIC or Parallel Interference Cancellation (PIC)) for that matter. More ever since we are using adaptive method the desired BER can be further reduced. This proposed method will be discussed in detail in the following section.

This tutorial presents a novel search system—the Attractor-Based Search System (ABSS)—that can solve the Traveling Salesman Problem very efficiently with optimality guarantee. From the perspective of dynamical systems, a heuristic local search algorithm for an NP-complete combinatorial problem is a discrete dynamical system. In a local search system, an attractor drives the search trajectories into the vicinity of a globally optimal point in the solution space, and the convergence of local search trajectories makes the search system become a global and deterministic system. The attractor contains a small set of the most promising solutions to the problem. The attractor can reduce the problem size exponentially, and thus make the exhaustive search feasible. Therefore, this new search paradigm is called optimizing with attractor. The ABSS consists of two search phases: local search phase and exhaustive search phase. The local search process is used to quickly construct the attractor in the solution space, and the exhaustive search process is used to completely search the attractor to identify the optimal solution. Therefore, the exact optimal solution can be found quickly by combining local search and exhaustive search. This tutorial introduces the concept of an attractor in a local search system, and describes the process of optimizing with the attractor, using the Traveling Salesman Problem as the study platform.

A binary social spider algorithm for uncapacitated facility location problem

Application layer peer to peer (P2P) networks are considered to be the most important development for next generation Internet infra-structure. For these systems to be effective, load balancing among the peers is critical. Early structured P2P systems rely on the randomness of object ID generated with a consistent hash function to avoid the load imbalance issue, and has been known to result in an imbalance factor in the number of items stored at a node, even in the case of a homogeneous system where all nodes have the same capacity. We propose to address the problem systematically using an optimization based approach, and show that the problem of rearranging loads among the peers can be cast in the framework of a generalized assignment problem. The dual space local search (DSLS) heuristic algorithm is proposed, which minimizes the load movement involved in an efficient manner. We demonstrate the superior performance of our proposal in general, and its advantages over previous strategies. We also explore other important issues vital to the performance for the virtual server framework, such as the performance ramification of peer registration strategies. Significantly, we characterize systematically the effect of heterogeneity on load balancing algorithm performance, and the conditions in which heterogeneity may be easy or hard to deal with.

In this paper, shape-parameter-dependent matched filter (MF) detectors are proposed for moving target detection in K-distributed clutter, which are specified by a single parameter α [0, 1], the α-MF detectors for short. The α-MF detectors include the MF and normalized matched filter (NMF) detectors as special examples with α = 0 and 1. The parameter α can be chosen to match clutter characteristics. An empirical formula is given that the optimal parameter α equals the number of integrated pulses divided by it plus the shape parameter of K-distributed clutter. It is proved that the α-MF detectors are constant false alarm rate (CFAR) with respect to the scale parameter, clutter covariance matrix, and Doppler steering vector. The properties of adaptive α-MF (α-AMF) detectors are discussed. For K-distributed clutter, the optimal α-MF detectors are superior to the MF and NMF detectors and are comparable with the optimal K-distributed detectors (OKDs) of high computational cost. Finally, real high-resolution sea clutter data are available to verify the proposed detectors. The optimal α-AMF detectors under K-distributed clutter model are competitive in performance with the adaptive generalized likelihood ratio test linear threshold detectors (GLRT-LTDs) that are optimal for the compound-Gaussian clutter with the inverse Gamma texture.

In this paper, we perform an analytical comparison of two well-known detectors-the matched filter detector (MFD) and the orthogonal subspace projection (OSP) detector for the subpixel target detection in hyperspectral images under the assumption of the structured model. The OSP detector (equivalent to the least squares estimator) is a popular detector utilizing background signature information. On the other hand, the MFD is intended for a model without background information, and it is often used for its simplicity. The OSP detector seems to be more reliable because it removes the interference of background signatures. However, it has been demonstrated in the literature that sometimes the MFD can be more powerful. In this paper, we show analytical results explaining the relationship between the two detectors beyond the anecdotal evidence from specific hyperspectral images or simulations. We also give some guidelines on when the MFD may be more beneficial than the OSP, and when the OSP is better because of being more robust against a wide range of conditions.

Train dispatching in railway transport is responsible for organization and coordination of train operation work. High level dispatching policy plays a crucial role not only in improving transport efficiency and effectiveness, but also in ensuring traffic safety and enhancing service quality. In view of Vague Set's fuzziness, Tabu Search's rapidity and Heuristic Algorithm's universality, a heuristic algorithm for dispatching policy based on Vague Set's evaluation and optimization is provided in this paper: first the definition of train dispatching model establishes the dispatching foundation, then schedule candidates generated from pending resources in proper order by greedy principle accept the evaluation of Vague Set, at last iterated optimization gives out final train operation policy. The introduction of neighbor search through priority, Tabu list and rollback mechanism ensures stability, reliability and high-effect of algorithm. With innovation, this method is used for online/offline train diagram's generation and optimization in the field of traffic dispatching. The experimental results show that the method is feasible and higher system robustness.

Based on the Antibody Clonal Selection Theory of immunology, we put forward a novel clonal selection algorithm for multiuser detection in Code-division Multiple-access Systems. By using the clonal selection operator, the new algorithm can carry out the global search and the local search in many directions rather than one direction around the same individual simultaneously. After discussing the main characters of the new algorithm, especially the convergence and complexity, the performance of the proposed receiver, named by CAMUD, is evaluated via computer simulations and compared to that of other suboptimal schemes as well as to that of Optimal Multiuser detector (OMD) and conventional detector in CDMA systems over Multi-Path Channels. When compared with the OMD scheme, the CAMUD is capable of reducing the computational complexity significantly. On the other hand, when compared with standard genetic algorithm and improved genetic algorithm, theoretical analysis and Monte Carlo simulations show that the CAMUD with same complexity has optimal performance in eliminating MAI and near-far resistance. The simulations also show that the CAMUD performs quite well even when the number of active users and the length of the transmitted packet are considerably large.

Planning of distribution substations, feeders and sectionalizing switches using heuristic algorithms

The prohibitive - exponential in the number of users - computational complexity of the maximum-likelihood multiuser detector for direct-sequence code-division multiple-access communications has fueled an extensive research effort for the development of low-complexity multiuser detection alternatives. We show that we can efficiently and effectively approach the error rate performance of the optimum multiuser detector as follows. We utilize a multiuser zero-forcing or minimum mean-square error (MMSE) linear filter as a preprocessor and we establish that the output magnitudes, when properly scaled, provide a reliability measure for each user bit decision. Then, we prepare an ordered, reliability-based error search sequence of length linear in the number of users; it returns the most likely user bit vector among all visited options. Numerical and simulation studies for moderately loaded systems that permit exact implementation of the optimum detector indicate that the error rate performance of the optimum and the proposed detector are nearly indistinguishable over the whole predetection. signal-to-noise ratio range of practical interest. Similar studies for higher user loads (that prohibit comparisons with the optimum detector) demonstrate error rate performance gains of orders of magnitude in comparison with straight decorrelating or MMSE multiuser detection.