In this paper, we analyze the best relay selection scheme for the soft-decision-and-forward (SDF) cooperative networks with multiple relays. The term `best relay selection' implies that the relay having the largest end-to-end signal-to-noise ratio is selected to transmit in the second phase transmission. The approximate performances in terms of pairwise error probability (PEP) and bit error rate (BER) are analyzed and compared with the conventional multiple-relay transmission scheme where all the relays participate in the second phase transmission. Using the asymptotics of the Fox's H-function, the diversity orders of the best relay selection and conventional relay scheme for the SDF cooperative networks are derived. It is shown that both have the same full diversity order. The numerical results show that the best relay selection scheme outperforms the conventional one in terms of bit error rate.

A key technical challenge in heterogeneous wireless networks (HWNs) is the support of vertical handoff. It allows mobile users to switch connections among networks. In this paper, we propose and evaluate the application of VIKOR for vertical handoff. VIKOR is a Multiple Attribute Decision Making (MADM) method which makes decisions based on an aggregating function representing closeness to the ideal solution. We conducted simulation experiments to compare the performance of VIKOR for vertical handoff with other decision schemes such as SAW, TOPSIS, and WMC. We considered voice, data, and cost-constrained connections. Our results show that VIKOR is able to obtain satisfactory to excellent performance in the four different types of connections being considered.

In this letter, we propose a distributed switch-and-stay combining network with partial relay selection and show that the system spectral efficiency can be improved via adaptive modulation. Analytical expressions for the achievable spectral efficiency and average bit error rate of the proposed system over Rayleigh fading channels are derived for an arbitrary switching threshold. Numerical results are gathered to substantiate the analytical derivation showing that in terms of spectral efficiency, the system with single relay outperforms that with more than one relay at high signal-to-noise ratios (SNRs) and the optimal switching threshold can significantly improve the system performance at medium SNRs.

In next-generation networks, ultra-high speed transfer capability will become necessary to support a variety of advanced multimedia services. The Optical Wavelength Division Multiplexing (WDM) network is seen as one of promising networks. To deal with various multimedia services, the network should support not only unicast transmission but also multicast transmission. However, IP multicast has several problems, for example, all routers must have multicast functions. IP multicast needs routers with high overheads and excessive energy consumption. Hence, optical multicasting in optical WDM networks is a promising solution for future internet services. A tree-shared multicasting concept has been proposed to support multicast transmissions in optical WDM networks. This method assembles multiple multicast traffic streams into one burst and the burst is delivered using a shared-tree. However, this method can not be applied to dynamic multicasting. This paper proposes a novel WDM multicast design method for dynamic traffic demand using multiple shared-trees, which includes shared-tree generation/selection and wavelength assignment, for the purpose of simplifying the routing process and receiving multicast traffic efficiently. We evaluate its performance from the viewpoints of the burst loss probability and the number of redundant and useless transfers whose data is discarded at the egress edge nodes.

In this letter, we propose an antenna selection single frequency network precoding (AS-SFNP) scheme for downlink cooperative multiple-input multiple-output (MIMO) systems, which efficiently improves system capacity with low feedback overhead and low complexity.

This letter presents a criterion for selecting a transmit antenna subset when ZF detectors followed by Rake combiners are employed for spatial multiplexing (SM) ultra-wideband (UWB) multiple input multiple output (MIMO) systems. The presented criterion is based on the largest minimum post-processing signal to interference plus noise ratio of the multiplexed streams, which is obtained on the basis of QR decomposition. Simulation results show that the proposed antenna selection algorithm considerably improves the BER performance of the SM UWB MIMO systems when the number of multipath diversity branches is not so large and thus offers diversity advantages on a log-normal multipath fading channel.

A three-stage Clos-network switch with input queues is attractive for practical implementation of a large-capacity packet switch. A scheme that configures the first, second, and third stages in that sequence by performing iterative matchings based on random selections is called the staged random scheduling scheme. Despite the usefulness of such a switch, the literature provides no analytical formula that can accurately calculate its throughput. This paper develops a formula to calculate the throughput analysis of the staged random scheduling scheme for one and multiple iterations used in an input-queued Clos-network switch under uniform traffic. This formula can be used to verify simulation models for very large switches. The introduced derivation considers the processes of the selection scheme at each stage of the switch. The derived formula is used in numerical evaluations to show the throughput of large switch sizes. The results show that the staged random scheduling scheme with multiple iterations for a Clos-network switch with VOQs without internal expansion approaches 100% throughput under uniform traffic. Furthermore, evaluations of the derived formulas are used in a practical application to estimate the number of iterations required to achieve 99% throughput for a given switch size. In addition, the staged random scheduling scheme in an input-queued Clos-network switch is modeled and simulated to compare throughput estimations to those obtained with the derived formulas. The simulation results support the correctness of the derived formulas.

In Long-Term Evolution (LTE)-Advanced, a heterogeneous network in which femtocells and picocells overlay macrocells is being extensively discussed in addition to traditional well-planned macrocell deployment to improve further the system throughput. In heterogeneous network deployment, cell selection as well as inter-cell interference coordination (ICIC) is very important to improve the system and cell-edge throughput. Therefore, this paper investigates three cell selection methods associated with ICIC in heterogeneous networks in the LTE-Advanced downlink: Signal-to-interference plus noise power ratio (SINR)-based cell selection, reference signal received power (RSRP)-based cell selection, and reference signal received quality (RSRQ)-based cell selection. The results of simulations (4 picocells and 25 sets of user equipment are uniformly located within 1 macrocell) that assume a full buffer model show that the downlink cell and cell-edge user throughput levels of RSRP-based cell selection are degraded by approximately 2% and 11% compared to those for SINR-based cell selection under the condition of maximizing the cell-edge user throughput due to the impairment of the interference level. Furthermore, it is shown that the downlink cell-edge user throughput of RSRQ-based cell selection is improved by approximately 5%, although overall cell throughput is degraded by approximately 6% compared to that for SINR-based cell selection under the condition of maximizing the cell-edge user throughput.

Cognitive radio is a promising approach to ensuring the coexistence of heterogeneous wireless networks since it can perceive wireless conditions and freely switch among different network modes. When there are many network opportunities, how to decide the appropriate network selection for CR user's current service is the main problem we study in this paper. We make full use of the intelligent characteristic of CR user and propose a fuzzy learning based network selection scheme, in which the network selection choice is made based on the estimated evaluations of available networks. Multiple factors are considered when estimating these evaluations. Both the outer environment factors directly sensed by CR user (signal strength of the available network and network mode), and also the factor that cannot be determined beforehand and is learnt by our scheme (the bandwidth allocated by the optional network) are considered. From several interactions with the wireless environment, the experience of network selection behavior is accumulated which will direct our scheme to make a proper decision of the network. Two simulations verify that our scheme could not only guarantee a better bandwidth requirement of CR user compared with other three network selection methods, but also shows it to be a reasonable scheme for utilizing the available resources of these networks.

This paper presents novel hybrid push-pull protocols for peer-to-peer video streaming. Our approaches intend to reap the best of push- and pull-based schemes by adaptively switching back and forth between the two modes according to video chunk distributions. The efficacy of the proposed protocols is validated through an evaluation study that demonstrates substantial performance gains.

This letter has retrained an MQDF classifier on the retraining set, which is constructed by samples locating near classification boundary. The method is evaluated on HCL2000 and HCD Chinese handwriting sets. The results show that the retrained MQDF outperforms MQDF and cascade MQDF on all test sets.

The measure of the goodness, or inversely the cost, of concatenating synthesis units plays an important role in concatenative speech synthesis. In this paper, we present a probabilistic approach to concatenation modeling in which the goodness of concatenation is measured by the conditional probability of observing the spectral shape of the current candidate unit given the previous unit and the current phonetic context. This conditional probability is modeled by a conditional Gaussian density whose mean vector has a form of linear transform of the past spectral shape. Decision tree-based parameter tying is performed to achieve robust training that balances between model complexity and the amount of training data available. The concatenation models are implemented for a corpus-based speech synthesizer, and the effectiveness of the proposed method was confirmed by an objective evaluation as well as a subjective listening test. We also demonstrate that the proposed method generalizes some popular conventional methods in that those methods can be derived as the special cases of the proposed method.

This letter presents a novel user scheduling algorithm that provides a maximum sum-rate based on zero-forcing beamforming (ZFBF) in multiple-input multiple-output (MIMO) systems. The proposed technique determines primary user pairs in which the sum-rate exceeds a predetermined threshold. To determine the threshold, we define the maximum-sum-rate criterion (MSRC) derived from the extreme value theory (EVT). Applying the MSRC in ZFBF-based user scheduling, we find that the performance of the proposed method is comparable to that of the exhaustive searching scheme which has a greater computational load. Through computer simulations, we show that the proposed method outperforms the very well-known correlation-based method, semi-orthogonal user selection (SUS), yielding a sum rate that is about 0.57 bps/Hz higher when the transmit SNR is 10 dB with perfect CSI at BS and the numbers of users and transmit antennas in a cell are 100 and 4, respectively.

This paper presents a novel chunk selection strategy for peer-to-peer video streaming, called enr-first selection policy, which simultaneously considers both block rarity and playback deadline. The policy intends to boost overall network-wide streaming performance, while ensuring good playback continuity of individual peers. The efficacy of the proposed scheme is validated through our performance evaluation study that demonstrates a substantial gain.

This paper introduces a random selection cooperation scheme that takes the Decode-and-Forward (DF) approach to solve the unfairness problem in selection cooperation. Compared to previous work which obtained fairness but introduced performance loss, the proposed scheme guarantees fairness without performance loss. Its essence is to randomly select from the relays that can ensure the successful communication between the source and the destination, rather than to select the best relay. Both a theoretical analysis and simulation results confirm that the proposed scheme could achieve fairness and introduce no performance loss. We also discuss the conditions under which the proposed scheme is practical to implement.

In this paper, we consider a nonparametric regression problem using a learning machine defined by a weighted sum of fixed basis functions, where the number of basis functions, or equivalently, the number of weights, is equal to the number of training data. For the learning machine, we propose a training scheme that is based on orthogonalization and thresholding. On the basis of the scheme, vectors of basis function outputs are orthogonalized and coefficients of the orthogonalized vectors are estimated instead of weights. The coefficient is set to zero if it is less than a predetermined threshold level assigned component-wise to each coefficient. We then obtain the resulting weight vector by transforming the thresholded coefficients. In this training scheme, we propose asymptotically reasonable threshold levels to distinguish contributed components from unnecessary ones. To see how this works in a simple case, we derive an upper bound for the generalization error of the training scheme with the given threshold levels. It tells us that an increase in the generalization error is of O(log n/n) when there is a sparse representation of a target function in an orthogonal domain. In implementing the training scheme, eigen-decomposition or the Gram–Schmidt procedure is employed for orthogonalization, and the corresponding training methods are referred to as OHTED and OHTGS. Furthermore, modified versions of OHTED and OHTGS, called OHTED2 and OHTGS2 respectively, are proposed for reduced estimation bias. On real benchmark datasets, OHTED2 and OHTGS2 are found to exhibit relatively good generalization performance. In addition, OHTGS2 is found to be obtain a sparse representation of a target function in terms of the basis functions.

In this letter, we investigate the Nth-best user selection scheme for amplify-and-forward cooperative systems over Rayleigh fading channels. We deduce the probability density function, the cumulative density function, and the moment generating function of the end-to-end signal-to-noise ratio of the system. Then, the respective closed-form expressions of the average symbol error probability and the outage probability at the destination are derived. The diversity order obtained in the scheme increases with user number but becomes less as the selection sequence number N increases. Simulation results verify the analytical results.

This work describes a 12-bit 100 MS/s 0.13 µm CMOS three-stage pipeline ADC with various circuit design techniques to reduce power and die area. Digitally controlled timing delay and gate-bootstrapping circuits improve the linearity and sampling time mismatch of the SHA-free input network composed of an MDAC and a FLASH ADC. A single two-stage switched op-amp is shared between adjacent MDACs without MOS series switches and memory effects by employing two separate NMOS input pairs based on slightly overlapped switching clocks. The interpolation, open-loop offset sampling, and two-step reference selection schemes for a back-end 6-bit flash ADC reduce both power consumption and chip area drastically compared to the conventional 6-bit flash ADCs. The prototype ADC in a 0.13 µm CMOS process demonstrates measured differential and integral non-linearities within 0.44LSB and 1.54LSB, respectively. The ADC shows a maximum SNDR and SFDR of 60.5 dB and 71.2 dB at 100 MS/s, respectively. The ADC with an active die area of 0.92 mm2 consumes 19 mW at 100 MS/s from a 1.0 V supply. The measured FOM is 0.22 pJ/conversion-step.

We present a new structure for parallel affine projection (AP) filters with different step-sizes. By observing their error signals, the proposed alternating AP (A-AP) filter selects one of the two AP filters and updates the weights of the selected filter for each iteration. As a result, the total computations required for the proposed structure is almost the same as that for a single AP filter. Experimental results show that the proposed alternating selection scheme extracts the best properties of each component filter, namely fast convergence and small steady-state error.

This paper addresses the problem of sensor selection in wireless sensor networks (WSN) subject to a distortion constraint. To do so, first, a cost function is derived based on the spatial correlation obtained using the best estimation of the event source. Then, a new adaptive algorithm is proposed in which the number of active sensors is adaptively determined and the best topology of the active set is selected based on the add-one-sensor-node-at-a-time method. Simulations results show that the active sensors selected using the proposed cost function have less event distortion. Also, it is shown that the proposed sensor selection algorithm is near optimum and it has better performance than other algorithms with regard to the computational burden and distortion.