To realize dynamic spectrum access (DSA), spectrum sensing is performed to detect the presence or absence of primary users (PUs). This paper proposes a sensing architecture. This architecture enables use cases such as DSA with PU detection using a single spectrum sensor and DSA with distributed sensing, such as cooperative sensing, collaborative sensing, and selective sensing. In this paper we focus on distributed sensing. These sensing schemes employ distributed spectrum sensors (DSSs) where each sensor uses energy detection (ED) in Rayleigh fading environment. To theoretically analyze the performance of the three sensing schemes, a closed-form expression for the probability of detection by ED with selective combining (SC) in Rayleigh fading environment is derived. Applying this expression to the PU detection problem, we obtain analytical models of the three sensing schemes. Analysis shows that at 5-dB signal-to-noise ratio (SNR) and with a false alarm rate of 0.004, the probability of detection is increased from 0.02 to 0.3 and 0.4, respectively, by cooperative sensing and collaborative sensing schemes using using three DSSs. Results also show that the selected sensing scheme matches the performance of the collaborative sensing scheme. Moreover, it provides a low false alarm rate.

Due to the advancement of software radio and RF technology, cognitive radio(CR) has become an enabling technology to realize dynamic spectrum access through its spectrum sensing and reconfiguration capability. Robust and reliable spectrum sensing is a key factor to discover spectrum opportunity. Single cognitive radios often fail to provide such reliable information because of their inherent sensitivity limitation. Primary signals that are subject to detection by cognitive radios may become weak due to several factors such as fading and shadowing. One approach to overcome this problem is to perform spectrum sensing by using multiple CRs or multiple spectrum sensors. This approach is known as distributed sensing because sensing is carried out through cooperation of spatially distributed sensors. In distributed sensing, sensors should perform spectrum sensing and forward the result to a destination where data fusion is carried out. Depending on the channel conditions between sensors (sensor-to-sensor channel) and between the sensor and the radio (user-channel), we explore different spectrum sensing algorithms where sensors provide the sensing information either cooperatively or independently. Moreover we investigate sensing schemes based on soft information combining (SC), hard information combining (HC). Finally we propose a two-stage detection scheme that uses both SC and HC. The newly proposed detection scheme is shown to provide improved performance compared to sensing based on either HC or SC alone. Computer simulation results are provided to illustrate the performances of the different sensing algorithms.

In distributed spectrum sensing, spatially distributed sensors perform radio frequency (RF) sensing and forward the result to a fusion center (FC). Cognitive radio (CR) obtains spectral information from the FC. Distributed spectrum sensing facilitates reliable discovery of spectrum opportunity while providing enhanced protection to legacy systems. The overall performance of distributed spectrum sensing depends both on the quality of sensing at the individual sensors and the forwarding scheme from the individual sensors. In this aspect the choice of media access control (MAC) plays a significant role. We can improve the system performance by optimizing the MAC and the spectrum sensing parameters jointly. In this paper we propose an enhanced MAC scheme based on existing scheduled MAC protocols to yield a high performance distributed spectrum sensing. To demonstrate our idea, we provide computer simulation by considering energy detection based distributed spectrum sensors and IEEE 802.15.4 PHY and MAC parameters.

This paper presents the efficiency of a sensing database and caching (SDB) for cognitive radio systems. The proposed SDB stores regulatory information from regulatory databases, and contains sensing information by distributed sensing schemes. Preliminary information processing for instance indexing, sorting, or applying some models or algorithms, etc. can be performed for the stored data. Available information and the results of the information processing are provided to cognitive radios in order to determine available spectrum and to facilitate dynamic spectrum access at lower sensing cost but higher sensing quality. The SDB is implemented in local networks, therefore information exchange between SDB and the cognitive radios can be realized at low latency and the amount of signaling traffic to global network can be reduced. This paper analyzes the effect of SDB and the performance evaluation was done in a certain condition. As a result, by deploying SDB a system can achieve up to 20% of reduction of sensing activities and maximum 1.3 times higher sensing quality.

In this paper network coding based relay for multi-access channel is studied. In the system, two nodes send messages to a common access point (AP). A relay assists the two nodes by forwarding a network coded version of the messages. The AP performs joint channel and network decoding to recover the two original messages from three received signals. Two schemes, soft network coding (SoftNC) and turbo network coding (TurboNC), both focusing on bitwise exclusive or (XOR) based network coding, are proposed to salvage messages from erroneous signals. SoftNC is simple and backward compatible with existing protocol stack of wireless networks, and reduces packet errors by maximal ratio combining (MRC). TurboNC improves channel efficiency by letting the relay node transmit only parity check bits of the interleaved XORed message, where reliability is retained by iterative decoding. Simulation results show that compared with the network-layer path diversity scheme, both SoftNC and TurboNC greatly improve the reliability, and TurboNC also achieves a much higher throughput. The proposed schemes are suitable for improving the performance of wireless local area networks (WLAN).

This paper addresses the coexistence issue of distributed heterogeneous networks where the network nodes are cognitive radio terminals. These nodes, operating as secondary users (SUs), might interfere with primary users (PUs) who are licensed to use a given frequency band. Further, due to the lack of coordination and the dissimilarity of the radio access technologies (RATs) among these wireless nodes, they might interfere with each other. To solve this coexistence problem, we propose an architecture that enables coordination among the distributed nodes. The architecture provides coexistence solutions and sends reconfiguration commands to SU networks. As an example, time sharing is considered as a solution. Further, the time slot allocation ratios and transmit powers are parameters encapsulated in the reconfiguration commands. The performance of the proposed scheme is evaluated in terms of the coexistence between PUs and SUs, as well as the coexistence among SUs. The former addresses the interference from SUs to PUs, whereas the latter addresses the sharing of an identified spectrum opportunity among heterogeneous SU networks for achieving an efficient spectrum usage. In this study, we first introduce a new parameter named as quality of coexistence (QoC), which is defined as the ratio between the quality of SU transmissions and the negative interference to PUs. In this study we assume that the SUs have multiple antennas and employ fixed transmit power control (fixed-TPC). By using the approximation to the distribution of a weighted sum of chi-square random variables (RVs), we develop an analytical model for the time slot allocation among SU networks. Using this analytical model, we obtain the optimal time slot allocation ratios as well as transmit powers of the SU networks by maximizing the QoC. This leads to an efficient spectrum usage among SUs and a minimized negative influence to the PUs. Results show that in a particular scenario the QoC can be increased by 30%.

In this paper, we investigate the user scheduling algorithms with statistical eigen-mode transmission (SET) for downlink multiuser multiple-input multiple-output (MU-MIMO) system by utilizing the statistical channel state information (SCSI). Given the objective of maximizing the ergodic achievable sum rate per group (EASRPG), our first proposal, the Munkres user assignment algorithm (MUAA), solves the optimal user grouping problem. Different from the conventional user grouping algorithm (e.g. max-min method), MUAA can efficiently solve the user assignment problem and acquire an optimal solution. However, some user groups of the optimal solution called “unfriendly” groups severely degrade the EASRPG by performing the multiuser SET (MU-SET) due to excessive inter-user interference. To overcome this obstacle, the MUAA with sequential iterative separation (MUAA-SIS) is proposed to find the “unfriendly” groups and switch from the MU-SET to the single-user SET. Finally, our numerical results show that MUAA-SIS offers a higher EASRPG.

The application of Technology CAD simulations for development of IC processes in foundry is presented. Examples include device design, Flash cell design and optical proximity correction for SRAM cell. The challenges of using TCAD tools in the IC foundry is also discussed.

Recent work on joint word segmentation, POS (Part Of Speech) tagging, and dependency parsing in Chinese has two key problems: the first is that word segmentation based on character and dependency parsing based on word were not combined well in the transition-based framework, and the second is that the joint model suffers from the insufficiency of annotated corpus. In order to resolve the first problem, we propose to transform the traditional word-based dependency tree into character-based dependency tree by using the internal structure of words and then propose a novel character-level joint model for the three tasks. In order to resolve the second problem, we propose a novel semi-supervised joint model for exploiting n-gram feature and dependency subtree feature from partially-annotated corpus. Experimental results on the Chinese Treebank show that our joint model achieved 98.31%, 94.84% and 81.71% for Chinese word segmentation, POS tagging, and dependency parsing, respectively. Our model outperforms the pipeline model of the three tasks by 0.92%, 1.77% and 3.95%, respectively. Particularly, the F1 value of word segmentation and POS tagging achieved the best result compared with those reported until now.