Femtocell is a promising technology to improve network performance with low-power and cost-beneficial small base stations. However, the interference-limited reality in femtocell networks makes interference and resource management the key to achieving the benefits of femtocell networks. In this paper, the following contributions are made step by step: first, on the basis of the interference temperature model (ITM) in cognitive radio (CR) technique and the network architecture of the third generation partnership project (3GPP) long term evolution advanced (LTE-A), the problem model of optimizing the capacity of the femtocell-reused subchannel is established under the frequency partitioning strategy, jointly considering the average interference constraint and the instantaneous interference constraint. Second, utilizing the convex theory, optimal power allocation of the femtocell-reused subchannel is derived. Third, under Rayleigh fading channel, closed-form expressions of the subchannel reusable probability and capacity are derived. At last, numerical results are conducted to confirming our analytical results, which could provide theoretical guidance for frequency resource allocation of femtocell network deployments.

In this paper we consider interference-aware uplink transmission schemes for multicell multiple-input multiple-output (MIMO) system. Unlike conventional transmission schemes without considering the interference probably caused to other cell, we jointly optimize the transceiver beamforming vectors to maximize the desired signals while removing the intercell interference. Specifically, for a two-cell system where each transmitter is equipped with two antennas, we derive the closed-form expression for the transmit scheme called coordinated beamforming (CBF) via generalized-eigen analysis. Moreover, when asymmetric interference is considered, we give a balancing beamforming (BBF) scheme where the interfering transmitter is to strike a compromise between maximizing the desired signal and minimizing the generated interference. Simulation results show that both schemes perform better than conventional schemes under different scenarios

Multi-cell multi-user multiple-input multiple-output (MC-MU-MIMO) is a promising technique to eliminate inter-user interference and inter-cell cochannel interference in wireless telecommunication systems. As the large number of users in the system and the limited number of simultaneously supportable users with MC-MU-MIMO, it is necessary to select a subset of users to maximize the total throughput. However, the fully centralized user selection algorithms used in single cell system, which will incur high complexity and backhaul load in multi-cell cooperative processing (MCP) systems, are not suitable to MC-MU-MIMO systems. This article presents a two cascaded user selection method for MCP systems with multi-cell block diagonalization. In this paper, a local optimal subset of users, which can maximize the local sum capacity, is first chosen by the greedy method in every cooperative base station in parallel. Then, all the cooperative base stations report their local optimal users to the central unit (CU). Finally, the global optimal users, which can maximize the global sum capacity of MCP systems, are selected from the aggregated local optimal users at the CU. The simulation results show that the proposed method performs closely to the optimal and centralized algorithm. Meanwhile, the complexity and backhaul load are reduced dramatically.

This paper proposes rate-maximized (MR) joint subcarrier pairing (SP) and power allocation (PA) (MR-SP&PA), a novel scheme for maximizing the weighted sum rate of the orthogonal-frequency-division multiplexing (OFDM) relaying system with a decode-and-forward (DF) relay. MR-SP&PA is based on the joint optimization of both SP and power allocation with total power constraint, and formulated as a mixed integer programming problem in the paper. The programming problem is then transformed to a convex optimization problem by using continuous relaxation, and solved in the Lagrangian dual domain. Simulation results show that MR-SP&PA can maximize the weighted sum rate under total power constraint and outperform equal power allocation (EPA) and proportion power allocation (PCG).

A high accuracy frequency synchronization method is proposed for the 3rd generation partnership project (3GPP) long term evolution advanced (LTE-A) downlink receiver in time division duplexing (TDD) mode. In general, cyclic prefix (CP) correlation based fractional frequency offset (FFO) estimation method and primary synchronization signal (PSS) differential correlation based integer frequency offset (IFO) estimation method are applied for LTE-A frequency synchronization. However, the polarity of CP based FFO estimation result may get reversed when system FFO is closer to the edge of frequency estimation range on account of noise interference; PSS based IFO estimation has performance degradation in low signal noise ratio (SNR). We propose polarity detection aided CP based FFO estimation and frequency domain enhanced differential correlation based IFO estimation to obtain higher accuracy of frequency synchronization. Computer simulation shows that the proposed method greatly outperforms the conventional methods, especially in low SNR scenario.

In this paper, a frequency domain decision feedback equalizer is proposed for single carrier transmission with time-reversal space-time block coding (TR-STBC). It is shown that the diagonal decision feedback equalizer matrix can be calculated from the frequency domain channel response. Under the perfect feedback assumption, the proposed equalizer can approach matched filter bound (MFB). Compared with the existing time domain decision feedback equalizer, the proposed equalizer exhibits better performance with the same equalization complexity.

In this paper, we focus on the resource scheduling in the downlink of long term evolution advanced (LTE-A) assuming equal power allocation among subcarriers. Considering the backward compatibility, the LTE-A system serves LTE-A and long term evolution (LTE) users together with carrier aggregation (CA) technology. When CA is applied, a well-designed resource scheduling scheme is essential to the LTE-A system. Joint scheduling (JS) and independent scheduling (INS) are two resource scheduling schemes. JS is optimal in performance but with high complexity. Whereas INS is applied, the LTE users will acquire few resources because they can not support CA technology. And the system fairness is disappointing. In order to improve the system fairness without bringing high complexity to the system, an improved proportional fair (PF) scheduling algorithm base on INS is proposed. In this algorithm, we design a weigh factor which is related with the number of the carriers and the percentage of LTE users. Simulation result shows that the proposed algorithm can effectively enhance the throughput of LTE users and improve the system fairness.

Accumulated low density parity check (LDPCA) codec is proposed for DISCOVER project in distributed video coding (DVC), which offers flexible coding rate. Although it can use feedback channel to adapt the rate to the correlation of the video, but in real applications, using feedback channel can not always be possible. To solve this problem, some researchers proposed estimating the code rate at the encoder but the performance was not very good. Based on their researches, this paper considers the impact of convergence rate for iteration on rate estimate, which can be calculated using its check matrix. As a pilot study, this paper pays attention to the regular LDPCA codec. At the same time, it considers the impact of deviation in the estimated crossover probability, which gives some constraints to rate estimate. In the experiment, the proposed algorithm can improve the rate-distortion performance by up to 1 dB–1.2 dB.

The virtual network embedding/mapping problem is a core issue of the network virtualization. It’s mainly concerned with how to map virtual network requests to the substrate network efficiently. Previous researches mainly focus on developing heuristic algorithms for general topology virtual network request. In this paper, we propose a new algorithm for the hub-and-spoke (HAS) topology virtual request. The characters of the HAS topology are exploited to develop the new algorithm. The simulation results show that the new algorithm greatly enhances the performance of revenue/cost (R/C) ratio while the HAS topology request arrives.

Extracting the parameters of the multipath with high accuracy can be achieved by using high-resolution algorithm for time-domain ultra wideband (UWB) channel modeling. The CLEAN algorithm has been used as such a high-resolution algorithm for UWB time-domain characterization. This paper presents a compressed sensing (CS) based high-resolution deconvolution algorithm for time-domain UWB channel modeling. UWB wireless channels are a prime example of long and sparse channel impulse response (CIR). Furthermore, the dictionary of parameterized waveforms that closely matches the waveform of multipath leads to that the UWB channel measurement signal is more compactly represented. By adjusting the parameter of dictionary, CIRs of different resolutions can be obtained. The matching pursuit (MP) algorithm is used as the signal reconstruction method for CS and outputs the CIR directly. We also demonstrated that if the dictionary of CS is designed specifically, MP is an equivalent of single template CLEAN. Finally, the computation complexity of CS-MP is analyzed and comparison of MP and CLEAN is performed. Simulation results show that compared to CLEAN, the proposed CS-MP deconvolution algorithm can achieve a comparable performance with much fewer samplings.

The cognitive radio (CR) technology is believed to improve the spectrum efficiency. However, the interference problem has become a critical issue due to the coexistence of primary systems and CR systems. In this paper, the interferences in CR based cellular networks are discussed. Interference scenarios are analyzed, considering different interference sources. Meanwhile, an improved model named ‘Cognitive Interference Ring’ is introduced to describe the interference range of each secondary user (SU). Depending on the above analysis, graph coloring based dynamic power allocation (GCDPA) scheme is proposed for interference avoidance. Simulation results demonstrate that in CR based cellular networks, the interferences to primary users (PUs) can be effectively mitigated with the proposed GCDPA scheme, and the system throughput and power efficiency are both improved.

Video accelerator is developed for better user experience in video sharing websites such as YouTube. PPLive video accelerator (PPVA), which has the largest number of users in China, is based on peer-to-peer (P2P) system. The number of videos and peers in PPVA is by orders of magnitude many times larger than which in traditional P2P video on demand (VoD) system. As a result, even though the resource is sufficient, due to unfairness assignment, the quality of service can hardly satisfy all users. In this paper, we concentrate on the assignment of the fundamental resources in PPVA: storage and bandwidth. The problem of storage assignment is formulated as a nonlinear program (NLP) regarding the number of request as a random variable. The results show that the influence of the variance of requests is not negligible and proportional approach is appropriate only when the mean is much larger than the variance. The criteria about how to locate videos to appropriate peers are also presented, taking into account constrains such as the utilization of total bandwidth, the probability of bandwidth competition and the fairness between videos. Furthermore, the heuristic algorithms of allocating upload bandwidth in centralized and distributed fashion are proposed and evaluated against a widely used strategy (equal allocation) with respect to the balance among videos. Simulation results demonstrate that both algorithms can lead to significant performance improvement.

Recently, echo state networks (ESN) have aroused a lot of interest in their nonlinear dynamic system modeling capabilities. In a classical ESN, its dynamic reservoir (DR) has a sparse and random topology, but the performance of ESN with its DR taking another kind of topology is still unknown. So based on complex network theory, three new ESNs are proposed and investigated in this paper. The small-world topology, scale-free topology and the mixed topology of small-world effect and scale-free feature are considered in these new ESNs. We studied the relationship between DR architecture and prediction capability. In our simulation experiments, we used two widely used time series to test the prediction performance among the new ESNs and classical ESN, and used the independent identically distributed (i.i.d) time series to analyze the short-term memory (STM) capability. We answer the following questions: What are the differences of these ESNs in the prediction performance? Can the spectral radius of the internal weights matrix be wider? What is the short-term memory capability? The experimental results show that the proposed new ESNs have better prediction performance, wider spectral radius and almost the same STM capacity as classical ESN’s.

Tone model (TM) integration is an important task for mandarin speech recognition. It has been proved to be effective to use discriminatively trained scaling factors when integrating TM scores into multi-pass speech recognition. Moreover, context-dependent (CD) scaling can be applied for better interpolation between the models. One limitation of this approach is a large number of parameters will be introduced, which makes the technique prone to overtraining. In this paper, we propose to induce context-dependent model weights by using automatically derived phonetic decision trees. Question at each tree node is chosen to minimize the expected recognition error on the training data. First order approximation of the minimum phone error (MPE) objective function is used for question pruning to make tree building efficient. Experimental results on continuous mandarin speech recognition show the method is capable of inducing the most crucial phonetic contexts and obtains significant error reduction with far fewer parameters, compared with that obtained by using manually designed context-dependent scaling parameters.

An effective text representation scheme dominates the performance of text categorization system. However, based on the assumption of independent terms, the traditional schemes which tediously use term frequency (TF) and document frequency (DF) are insufficient for capturing enough information of a document and result in poor performance. To overcome this limitation, we investigate exploring the relationships between different terms of the same class tendency and the way of measuring the importance of a repetitive term in a document. In this paper, a group of novel term weighting factors are proposed to enhance the category contribution for each term. Then, based on a novel strategy of generating passages from document, we present two schemes, the weighted co-contributions of different terms corresponding to the class tendency and the weighted co-contributions for each term in different passages, to achieve improvements on text representation. The prior scheme works in a dimensionality reduction mode while the second one runs in the conventional way. By employing the support vector machine (SVM) classifier, experiments on four benchmark corpora show that the proposed schemes could achieve a consistent better performance than the conventional methods in both efficiency and accuracy. Further analysis also confirms some promising directions for the future works.

Anti-worm is an effective way to fight against malicious worm and has been followed closely by malicious worm researchers recently. However, active and passive confronting technologies in peer-to-peer (P2P) networks have not been studied in depth. This paper introduces both of them to fight against malicious worm in P2P networks. To study their effectiveness in P2P networks, this paper takes the topology degree in P2P networks into consideration and puts forward a four-state propagation model for active anti-worm and a five-state propagation model for passive anti-worm respectively. Both of the models are simplified in the case that size of a P2P network is large enough. The simulation results have not only validated the effectiveness of our propagation models but also evaluated the excellent performance of both active anti-worm and passive anti-worm.

Wavelength decomposition approach has been proposed to compute blocking probability (BP) of fixed routing in wavelength-routed optical networks (WRONs) without wavelength conversions. By means of wavelength decomposition, a WRON can be regarded as a set of different layers (colors), in which blocked traffic in one layer is overflowed to another layer. A novel iterative scheme is put forward in case of BP matching used to characterize the overflow traffic from one layer to another in this paper. Furthermore, the analysis of BP based on the improved wavelength decomposition approach is derived and results show that it yields higher calculation accuracy.

A fully integrated 40 Gbit/s optical receiver analog front-end (AFE) including a transimpedance amplifier (TIA) and a limiting amplifier (LA) for short distance communication is described in this paper. The proposed TIA employs a modified regulated cascode (RGC) configuration as input stage, and adopts a third order interleaving active feedback gain stage. The LA utilizes nested active feedback, negative capacitance, and inductor peaking technology to achieve high voltage gain and wide bandwidth. The tiny photo current received by the receiver AFE is amplified to a single-ended voltage swing of 200 mV(p p). Simulation results show that the receiver AFE provides conversion gain of up to 83 and bandwidth of 34.7 GHz, and the equivalent input noise current integrated from 1 MHz to 30 GHz is about 6.6 ?A(rms).