As Large Language Models (LLMs) have become capable of generating long and descriptive code summaries, accurate and reliable evaluation of factual consistency has become a critical challenge.However, previous evaluation methods are primarily designed for short summaries of isolated code snippets.Consequently, they struggle to provide fine-grained evaluation of multi-sentence functionalities and fail to accurately assess dependency context commonly found in real-world code summaries.To address this, we propose ReFEree, a referencefree and fine-grained method for evaluating factual consistency in real-world code summaries.We define factual inconsistency criteria specific to code summaries and evaluate them at the segment level using these criteria along with dependency information.These segment-level results are then aggregated into a fine-grained score.We construct a code summarization benchmark with human-annotated factual consistency labels.The evaluation results demonstrate that ReFEree achieves the highest correlation with human judgment among 13 baselines, improving 15-18% over the previous state-of-the-art.Our code and data are available at https: //github.com/bsy99615/ReFEree.git.

In this study, a semi-blind channel estimation algorithm for uplink multiuser orthogonal frequency division multiplexing with receiver diversity is proposed. The proposed algorithm combines simple multiuser channel estimates obtained from pilot subcarriers with subspace-based channel estimates to obtain accurate estimates for the multiuser channel gains. Minimum mean square error linear diversity combining is then performed based on the estimated channel. Simulation results show that a performance gain in excess of 1–2 dB is achievable by the proposed approach.

The decision feedback equalizer (DFE) is widely used in practice for intersymbol interference (ISI) mitigation because of its low complexity. The performance of the DFE can be enhanced by arbitrating between the output of a standard DFE and a second DFE that operates on the time-reversed received sequence. The combination is called a bidirectional DFE (BDFE). Several previous papers have discussed how to arbitrate between the outputs of the forward and reverse equalizers to make hard decisions. We show that the arbitration algorithms proposed by other researchers are inefficient and propose a trellis-based conflict resolution (TBCR) algorithm that simultaneously provides better performance and lower complexity. We further show how the TBCR approach can be used to create soft-output BDFE algorithms for use with soft-input decoding of an error-control code. The soft-output BDFE algorithms offer better performance than previously proposed algorithms while requiring lower complexity than the maximum a posteriori equalizer. We also propose a novel method to reduce the computational complexity of BDFE algorithms by simultaneously computing the coefficients of the forward and reverse equalizers.

In this paper, a receiver algorithm for OFDM with receiver diversity is proposed.The proposed receiver uses a new multi-user generalization of the maximum a posteriori probability (MAP) selection diversity combining followed by successive interference cancellation with least-square projection for multi-user detection.Simulation results show that the proposed approach achieves near-maximal-ratiocombining performance with much lower complexity.

A combination of multi-carrier modulation (e.g., OFDM) and spread-spectrum (e.g., DS-CDMA) called MC-SS is considered as a potential candidate for the wireless 4G technology. Most present synchronization techniques for OFDM can also be used for the downlink MC-SS system. However, they are not suitable for the uplink MC-SS system because every user's signal arrives at the base station asynchronously. In this paper, we propose a patterned timing synchronization algorithm which can estimate the signal arrival times for every user. Simulation results show that the performance of the proposed timing synchronization algorithms are close to ideal in the presence of multipath fading channels with frequency offsets up to several carrier spacings.

Kalman channel estimators are optimal (MMSE) linear estimator. However, the direct implementation of the Kalman equations is too complex to be applied to practical OFDM systems with many subcarriers. In this paper, low-complexity structures for Kalman channel estimators are proposed for OFDM systems with and without virtual carriers that use a circular constellation. Simulation results show that the proposed low-complexity Kalman channel estimators almost achieve the performance of the perfect channel estimator.

In this paper, a receiver algorithm for OFDM with receiver diversity is proposed. The proposed receiver uses a new multi-user generalization of the maximum a posteriori probability (MAP) selection diversity combining followed by successive interference cancellation with least-square projection for multi-user detection. Simulation results show that the proposed approach achieves near-maximal-ratio-combining performance with much lower complexity.

In this paper, we propose a reduced complexity tree-based multi-user detection algorithm called relative threshold tree pruning (RTTP) for MC-CDMA. Two enhancements to RTTP - user ordering and user partitioning - are also investigated. Simulation results show that RTTP outperforms the conventional M-algorithm in both error performance and complexity.

A baseband receiver for DVB-T OFDM with variable number of subcarriers is presented in this paper. The proposed receiver consists of a blind mode detector, time and frequency synchronizer, and channel estimator. The blind mode detector uses a novel algorithm to detect the number of OFDM subcarriers in the received signal. Time and frequency synchronization is performed after the transmission mode is detected. Finally, channel estimation is performed using ID or 2D interpolation. Simulation results show that the proposed mode detection and synchronization algorithms are close to ideal, and good overall performance is achievable.

A receiver algorithm for OFDM with receiver diversity is proposed. The proposed receiver uses a new two-step blind channel estimation algorithm referred to as the subspace-MMSE channel estimator. Minimum mean square error linear diversity combining is then performed based on the estimated channel. Simulation results show that performance gain of 1-3 dB is achievable by the proposed approach.

A new criterion for optimizing the coefficients of multi-input multi-output (MIMO) decision feedback equalizers (DFE) is proposed for asymmetric signal constellations. It is shown that the new criterion is a generalization of the minimum mean square error (MMSE) criterion. The proposed MIMO DFE can be applied to the equalization of Gaussian minimum shift keying (GMSK) signals. Simulation results indicate that the proposed method achieves a performance gain of as much as 2 dB without increasing equalization complexity.

Conventional linear multi-user detectors (MUD) such as the decorrelating MUD and MMSE MUD require the inversion of a matrix or solution of a system of linear equations. The high complexity involved makes direct matrix inversion or direct solution of the system of linear equations difficult especially when the number of users is large. In this paper, we propose a new adaptive linear multi-stage MUD that has lower complexity. Parameters of the proposed structure can be optimized offline or update on the fly according to channel conditions. Simulation results show that the proposed MUD with a few numbers of stages almost achieves the performance of the linear MMSE MUD.

Conventional multi-stage multi-user detectors (MUD) such as the parallel interference cancellation (PIC) MUD suffer from error propagation. In this paper, we propose a new multi-stage MUD in which reliability weighting factors are applied to reduce error propagation. An algorithm is derived for optimizing both the reliability weighting factors and decision order for the proposed structure. Simulation results show that the proposed MUD almost achieves the single user bound with just a few stages.

We design a complete baseband system of a high data rate packet-type OFDM receiver. For mobility support and channel-gain phase tracking, we propose a simple pilot tone insertion method that not only facilitates low-complexity channel gain tracking but also enables carrier frequency tracking. Besides, an efficient mechanism for packet boundary detection is also provided. In addition to channel gain tracking and packet boundary detection, timing synchronization, coarse frequency offset estimation, and fine frequency offset tracking issues are also discussed and simulated.

We propose a novel low-complexity multi-user detection algorithm based on tree search for a unified signal model that applies to both direct sequence code-division multiple access (DS-CDMA) and multi-carrier CDMA (MC-CDMA) systems. Simulation results show that the proposed algorithm outperforms most previously proposed multi-user detection schemes, and can achieve near-optimal (maximum likelihood) performance with very low complexity.

The maximum likelihood sequence estimator (MLSE) has been shown to be an effective measure against inter-symbol interference. An estimate of the discrete-time equivalent channel is usually required in order to perform sequence estimation. In practice, the length (or memory) of the channel is unknown to the receiver for wireless communication channels. In this paper, upper and lower bounds for the performance loss due to channel estimator memory mismatch are derived for unbiased estimators, while approximate expressions for the irreducible BER floor caused by channel estimator memory mismatch are derived for biased estimators. Simulation results are also provided for random channels to verify the correctness of the analysis.

The cyclic prefix (CP) is typically employed in conventional orthogonal frequency division multiplex (OFDM) transmission systems. The CP, however, consumes bandwidth and power, and is undesirable for applications such as wireless communications. We propose an MMSE multi-input multi-output frequency domain decision-feedback equalizer (MIMO FDDFE). A novel channel interpolation algorithm is also proposed. Simulation results show that, with the proposed algorithm, an OFDM system without CP actually outperforms a conventional OFDM system with CP in both bit error rate (BER) and bandwidth efficiency.

Space-time parallel interference cancellation (ST-PIC) is an effective multi-stage multiuser detection scheme for wideband code-division multiple access (W-CDMA). However, the realization of ST-PIC requires accurate channel estimate, a sufficient number of stages, and knowledge of all user's spreading waveforms. With inaccurate channel estimate or insufficient number of stages, the residual multiple access interference (MAI) degrades performance significantly. Furthermore, the performance of ST-PIC is also affected by the presence of interference for which the spreading waveforms are unknown. This paper introduces a new structure for enhancing the performance of ST-PIC in these situations. Simulation results show that a bit error rate (BER) reduction of as much as an order of magnitude is achievable using the proposed structure in the presence of strong MAI.

OFDM is a promising multi-carrier modulation technique that can effectively mitigate impairments caused by wireless communication channels. In some OFDM standards (e.g., IEEE 802.11a), windowing is employed to reduce sensitivity to frequency offset and phase noise, and at the same time conserve bandwidth. However, windowing causes the loss of orthogonality between subcarriers. In this paper, we proposed a decision feedback orthogonality restoration filter for minimizing orthogonality loss due to both windowing and inter-block interference. Simulation results using IEEE 802.11a parameters show that the proposed scheme achieves significant performance improvement.

This paper presents a new method for combating error propagation in a decision-feedback equalizer (DFE) referred to as the hybrid decision-aided equalizer. Simulation results show that the performance of the hybrid decision-aided equalizer is significantly (1.5-2 dB) better than the DFE, and is in fact close to that of the maximum likelihood sequence estimator (MLSE). Detailed complexity analysis shows that the complexity of the hybrid decision-aided equalizer is significantly lower than that of the MLSE. An iterative version of the hybrid decision-aided equalizer is also presented in this paper in which effective stopping rules are implemented. It is shown that a significant additional gain in performance (1 to 2 dB) is achieved with a moderate increase in complexity.

OFDM is a promising multi-carrier modulation technique that can effectively mitigate impairments caused by wireless communication channels. In some OFDM standards (eg, IEEE 802.11a), windowing is employed to reduce sensitivity to frequency offset and phase noise, and at the same time conserve bandwidth. However, windowing causes the loss of orthogonality between subcarriers. We propose a decision feedback orthogonality restoration filter for minimizing orthogonality loss due to both windowing and inter-block interference. Simulation results using IEEE 802.11a parameters show that the proposed scheme achieves significant performance improvement.

The rapid development of the Internet and the World Wide Web has created a global network that will soon become a physical embodiment of the entire human knowledge and a complete integration of the global information activities. The traditional approach to access the network is through a computer physically tied to the network. As broad-band wireless takes off, the traditional tethered approach will gradually become obsolete. It is believed that one of the most natural and user-friendly approached for accessing the network will be via human voice, and the integration of spoken language processing technologies with broad-band wireless technologies will be a key to the evolution of a broad-band wireless information community. This paper offers a vision of the above concept. Technical considerations and some typical example applications of accessing the information and services using voice in a broad-band wireless environment are discussed. Fundamentals of spoken language processing technologies that are crucial in such a broad-band wireless environment are briefly reviewed. Technical challenges caused by the unique nature of wireless mobile communications are also presented along with some possible solutions.

Decision-feedback equalization (DFE) is an effective means for mitigating the effects of intersymbol interference (ISI). In general, the computation of the DFE filter coefficients is computationally intensive. Although several fast methods have been proposed in the literature, these methods either give approximate solutions to the DFE optimization problem or are "optimal" only under certain assumptions. This paper describes a new fast algorithm for FIR DFE optimization that eliminates the limitations of the previously methods while obtaining the exact "optimal" solutions. Simulation results show that by using the new algorithm, substantial bit error rate improvement of 2-3 dB can be obtained without significantly increasing complexity.

Indoor high-speed wireless data networks encounter signal fading and delay-spread multipath propagation. Hence, the realization of low error rate transmission requires measures to combat the performance degradation due to both signal fading and intersymbol interference (ISI). Receiver diversity has been known to be an efficient way of coping with the former problem, while adaptive equalization could be used to mitigate the effects of the latter. Incorporation of receiver diversity with adaptive equalization is therefore desirable. We propose a novel selection-diversity approach with an adaptive decision-feedback equalizer (DFE). In this method, selection is done on a symbol-by-symbol basis such that the output of the branch with the lowest estimated a posteriori probability of error is used as the final decision. This final (and hence more reliable) decision is used to adapt the DFE for all diversity branches. It is shown in this paper that the proposed selection rule is optimal for selection-diversity in the maximum a posteriori probability (MAP) sense. A very simple selection metric can be derived from this selection rule and practical ways of computing the selection metric are also presented. Simulation results show that the proposed method is very efficient. It is capable of achieving almost the same performance as an optimal [least squares (LS)], but computationally intensive, combining diversity approach. Furthermore, at an average bit error rate (BER) of 10/sup -4/, a gain of approximately 1.25 dB can be achieved over a previously proposed selection-diversity equalization approach.

A set of new approaches is proposed to model the Mandarin syllables for accurate recognition with limited training data while specially considering the effect of tones, including improved initial values and state transition topologies, and making use of the durational cue. The results show that these approaches are very useful practically.

The first successfully implemented real-time Mandarin dictation machine, which recognizes Mandarin speech with very large vocabulary and almost unlimited texts for the input of Chinese characters into computers, is described. The machine is speaker-dependent, and the input speech is in the form of sequences of isolated syllables. The machine can be decomposed into two subsystems. The first subsystem recognizes the syllables using hidden Markov models. Because every syllable can represent many different homonym characters and form different multisyllabic words with syllables on its right or left, the second subsystem is needed to identify the exact characters from the syllables and correct the errors in syllable recognition. The real-time implementation is on an IBM PC/AT, connected to three sets of specially designed hardware boards on which seven TMS 320C25 chips operate in parallel. The preliminary test results indicate that it takes only about 0.45 s to dictate a syllable (or character) with an accuracy on the order of 90%.>

The recognition of a total of 408 very confusing Mandarin syllables is very difficult because this vocabulary consists of 38 confusing sets, each of which can have as many as 19 syllables. The recognition of these 408 syllables becomes even more difficult when only very limited training data are available. A special direct-concatenation approach for training hidden Markov models (HMMs) to recognize these syllables with very limited training data is developed in which each syllable is divided into INITIAL and FINAL parts and 408 right-context-dependent INITIAL HMMs and 38 left-context-independent FINAL HMMs are separately trained and the transition region carefully taken account of, and then these INITIAL and FINAL HMMs are directly concatenated to form syllable recognition. Experimental results show that this approach can utilize the very limited training data most efficiently and provide significant improvements in recognition performance. Although the results are obtained for Mandarin syllables, the approach is believed to be equally helpful for the recognition of other confusing vocabularies.>