In this context, the contribution in Ref. Consequently, the M best cognitive users, whose channels are mutually near orthogonal to each other are scheduled from the preselected cognitive users and a ZF beamforming is applied to cancel the interference among these selected candidates. In the similar context, the contribution in Ref. In the proposed scheme, a ZF Beamforming and antenna selection are integrated in such a way that the former can be used to support multiple concurrent streams transmission and the latter to reduce the feedback in the uplink of the secondary system.
In this context, a two phase scheduling scheme based on opportunistic beamforming has been proposed in Ref. In the proposed scheme, a secondary transmitter generates a set of beamforming matrices consisting of M orthogonal beams and then sends this set to the PU which selects the best beamforming matrix that minimizes interference to it. Subsequently, the index of the selected beamforming matrix is fed back to the cognitive transmitter. In the second step, the cognitive transmitter transmits the beams of the best matrix selected in the first step to all SUs and each SU calculates its SINR corresponding to each beam and feeds back its maximum SINR and the corresponding beam index to the cognitive transmitter.
This two phase scheme requires cooperation between SUs and the primary system. This cooperation has been exploited in the literature in different contexts [ 58, 59 ]. Furthermore, a codebook based joint user scheduling and beamforming has been studied in Ref. The contribution in Ref. It is assumed that all the mesh nodes are equipped with multiple antennas and are capable of beamforming. An extended duality theory has been applied to the original non-convex solution in order to find an efficient solution.
Moreover, in Ref. Wayne W. Zachary, in Handbook of Human-Computer Interaction , This chapter discusses the decision support systems DSSs. DSS is defined as any interactive system that is specifically designed to improve the decision making of its user by extending the user's cognitive decision-making abilities. The important points of this definition are that a DSS is necessarily an interactive system and that it augments the human decision-making ability to make decisions. A DSS does not make automatic decisions for its user, although it would make suggestions and recommendations.
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A DSS is intended to remove some obstacle or relax some constraint that is preventing the human decision maker from making the best possible decision. Therefore, a DSS is defined relative to some specific unaided or baseline decision making system. In most cases, the obstacles or constraints on decision making that are addressed by DSSs are situation specific, and, as a consequence, so are most decision support systems. This is another peculiar aspect of DSSs—there are few significantly generic systems.
Ashley French, Melissa R. A complete task analysis is fundamental to user interface optimization, use error prediction and prevention, and determination of the user's interactions with the device relative to task requirements. These user task requirements include user actions, user perception of information and user cognitive processes Sharit, A Perception, Cognition, and Manual Action PCA model is an FDA recommended strategy for task analysis that is used to identify user-device interactions and characterize user capabilities.
Applying PCA to a task analysis adds specific user requirements that lend support for identification of potential use errors and root cause analysis during human factors testing. This model identifies user actions related to the perceptual inputs, cognitive processing, and physical actions involved in the task CDRH guidance, Table 6.
Task type examples in the PCA model. In more complex systems, there may be a fourth category of tasks which is Communication people to people. You can go to cart and save for later there. Report incorrect product info or prohibited items. Preston Marshall. Walmart Only 1 left! Free delivery Arrives by Friday, Nov Pickup not available. Add to List. Add to Registry.
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Learn to integrate information and decision theory to extend network density and scaling to unprecedented levels. About This Item We aim to show you accurate product information. Manufacturers, suppliers and others provide what you see here, and we have not verified it.
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See our disclaimer. This cohesive treatment of cognitive radio and networking technology integrates information and decision theory to provide insight into relationships throughout all layers of networks and across all wireless applications. It encompasses conventional considerations of spectrum and waveform selection and covers topology determination, routing policies, content positioning and future hybrid architectures that fully integrate wireless and wired services.
Emerging flexibility in spectrum regulation and the imminent adoption of spectrum-sharing policies make this topic of immediate relevance both to the research community and to the commercial wireless community. There are some drawbacks for current inter-cell interference coordination techniques. Firstly, current inter-cell interference management is performed in a distributed manner [ 11 ]. Generally, the processing complexity and overhead of distributed interference management impose high computing and resource burden on the RAN side.
Secondly, the lack of global view and information exchanged lead to poor performance. SDN-enabled access network offers the possibility to overcome the limitations described for inter-cell interference management. By centralizing network intelligence and computation resources, resource allocation decisions can be adjusted based on the dynamic power and subcarrier allocation profile of each base station.
In addition, scalability is improved because as new users are added, the required computing capacity at each base station remains low because these processing is centralized in the SDRAN controller. Illustration and system evaluation of inter-cell interference management scheme.
The two picocells coexisted with one macrocell denoted by MC. The macrocell and picocells report their locations, transmission power RNTP , support protocol version, load statue and interference statue to SDRAN controller in the first time. SDRAN controller records the information into database.
In addition, the SDRAN controller can ask the macrocell to communicate the utilization of the allocated ABS resources by starting a resource status RS reporting initialization mechanism. If the macrocell makes the decision of changing the ABS muting pattern, it informs the picocells within the cluster by means of an ABS information message. Otherwise, the UEs are labeled as center UEs of victim cell.
Calculate the traffic belong to the edge UEs in victim cell, denoted as u edge , while the total traffic of macrocell interference cell is u macro. Some system-level simulations are implemented to prove the performance of proposed scheme. The simulation scenario is configured according to case 1 configuration 4a corresponds to the macrocell and picocells deployment with 6 dB range expansion defined in the 3GPP [Ref TBA]. The scenario is composed of 21 macrocells i.
Thirtty UEs are dropped per macrocell area, where four UEs are located in each picocell. The remaining UEs are dropped randomly in the macrocell area without overlapping with picocell. Figure 5 shows the simulation scenario and the cumulative distribution function CDF of signal-to-interference-plus-noise ratio SINR obtained in this scenario.
The distributed interference management scheme denotes the traditional approaches with the decision in each macrocell. We observe that the improvement on the SINR distribution for logically centralized interference management facilitated by the SDRAN architecture is about 2 dB compared with the distributed scheme. In this article, we have identified the key benefits and challenges that software-defined networking can bring to radio access networks. We have presented a high-level general architecture of SDRAN along with some of its most relevant design and implementation details.
Finally, a simple use case interference management in heterogeneous network is discussed to show the practical implementation and benefits of logically centralized control. This paper is only the first step towards the completion of a complete solution. In fact, several issues remain open, such as the compatibility with the current standards and the feasibility of virtualized control functions. Skip to main content Skip to sections. Advertisement Hide. Download PDF. Towards next generation software-defined radio access network—architecture, deployment, and use case.
Open Access. First Online: 16 November Part of the following topical collections: Radar and Sonar Networks. As illustrated in Fig. The P-GW also connects to the Internet and other cellular networks. Open image in new window. The contradiction between centralized data-plane functions and decentralize control plane is the main reason. For instance, centralized data-plane functions force all traffic through the P-GW, while the QoS functionality at the P-GW introduces scalability challenges. In respect of the mobile operator, the existing cellular network architecture has the following problems.
Figure 2 shows the proposed SDN-based cellular network architecture. Compared with traditional cellular network architecture in Fig. Separate data and control path As shown in Fig. Besides, eNode C reports local view i. Note that eNode C may also implement some control functions that are not suitable for virtualization or centralization.
The programmable configuration of the RAN functions allows the best dynamic use of available resource. However, not all control functions are suitable for virtualized or centralized management.
Need of coordination in brief, NC All decisions that influence the decision-making at neighbouring cells should be made at the controller, since such decisions need to be coordinated across eNodeBs. Not bandwidth constrained briefly, BC Similar with delay constraint, the limited link bandwidth between controller and eNodeB is another bottleneck, especially considering the high network density and scalability.