Next Generation Wireless Network MCQs

Welcome to our comprehensive collection of Multiple Choice Questions (MCQs) on Next Generation Wireless Network, a fundamental topic in the field of Cognitive Radio. Whether you're preparing for competitive exams, honing your problem-solving skills, or simply looking to enhance your abilities in this field, our Next Generation Wireless Network MCQs are designed to help you grasp the core concepts and excel in solving problems.

In this section, you'll find a wide range of Next Generation Wireless Network mcq questions that explore various aspects of Next Generation Wireless Network problems. Each MCQ is crafted to challenge your understanding of Next Generation Wireless Network principles, enabling you to refine your problem-solving techniques. Whether you're a student aiming to ace Cognitive Radio tests, a job seeker preparing for interviews, or someone simply interested in sharpening their skills, our Next Generation Wireless Network MCQs are your pathway to success in mastering this essential Cognitive Radio topic.

Note: Each of the following question comes with multiple answer choices. Select the most appropriate option and test your understanding of Next Generation Wireless Network. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

So, are you ready to put your Next Generation Wireless Network knowledge to the test? Let's get started with our carefully curated MCQs!

Next Generation Wireless Network MCQs | Page 31 of 33

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Q301.
Which among the following is concerned with the effect on a knowledge system?

a.

Behaviour knowledge

b.

Declarative knowledge

c.

Narrative knowledge

d.

Status and system knowledge

Discuss
Answer: (a).Behaviour knowledge Explanation:Behavioural knowledge involves actions and their effect on the knowledge system. Both internal and external effectors altering the system and the environment are included. An event begins with a set of assertions and terminates with a varying set of assertions.
Q302.
Which among the following is a benefit of symbolic knowledge representation?

a.

No encryption is required

b.

Fast translation

c.

Less memory storage

d.

Easy human comprehension

Discuss
Answer: (d).Easy human comprehension Explanation:Symbolic knowledge representation typically involves graphical or natural language notations. It is easy for humans to understand. It offers a simplistic means for conveying underlying information for human understanding and interpretation.
Q303.
Which among the following is not used for symbolic knowledge representation?

a.

Interface

b.

Objects

c.

Semantic nets

d.

Frames

Discuss
Answer: (a).Interface Explanation:In symbolic representation and reasoning systems, storage is performed using extensible data structures to capture facts, descriptions, and properties related to a concept. Semantic nets, rules, frames, and objects are some of the structures employed to represent knowledge.
Q304.
Which among the following is employed for associating new concepts in the learning process for ontology based systems?

a.

Dissimilarities between two consequent entries

b.

Similarities between two consequent entries

c.

Similarities to existing entries in memory

d.

Dissimilarities to existing entries in memory

Discuss
Answer: (c).Similarities to existing entries in memory Explanation:Learning in ontology based system involves integrating new knowledge with existing collection of entries in memory. A classifier is used to assess the degree of similarity between a new entry and the existing entries. The classifier accomplishes this by analyzing the properties and values of a new entry.
Q305.
What does an arc in a decision tree represent?

a.

An action

b.

A decision

c.

Set of all possible choices

d.

Set of all possible plans

Discuss
Answer: (c).Set of all possible choices Explanation:A decision tree is a directed graph with a hierarchical set of nodes and arcs. A node represents a choice or a decision. An arc from one decision node to another decision node represents all possible choices associated with that node.
Q306.
What is the parameter of analysis in reinforcement learning?

a.

Number of requests during wake cycle

b.

Number of processes to achieve final outcome

c.

Degree of failure

d.

Degree of success

Discuss
Answer: (d).Degree of success Explanation:Reinforcement based learning assigns a weightage to an action on the basis of the degree of success of its outcome. When situations requiring similar action arise, the weightage associated with an action is analyzed for compatibility. The degree of success is computed by measuring the closeness of the obtained outcome with the expected outcome.
Q307.
Which among the following statements provides the difference between reinforcement-based learning and temporal difference technique?

a.

State represented by a directed graph

b.

Assignment of weightage to an action on the basis of the degree of success

c.

Computation of degree of success

d.

Priori model of the sequence of possible states

Discuss
Answer: (d).Priori model of the sequence of possible states Explanation:The temporal difference algorithm does not a priori model of the sequence of possible states as the temporal difference algorithm constructs the state representation during execution. The states are composed as a value function and are stored on a neural network.
Q308.
Which among the following is not a challenge of employing reasoning and learning stage in the cognitive radio?

a.

Computational requirement

b.

Quality of Service

c.

Edge conditions

d.

Predictable behaviour

Discuss
Answer: (b).Quality of Service Explanation:Cognitive radio should provide a large amount of computational resources to achieve the results of each operation. Edge conditions refer to the devices which are positioned in a location without regular service and hence cannot benefit from learning techniques. Predictable behaviour refers to the ability to estimate the outcome of each step of the operation but not the final outcome.
Q309.
Which among the following is not a challenge for case based reasoning implementation?

a.

Memory

b.

Fixed ontology and knowledge representation

c.

Pattern matching

d.

Accuracy of reasoning

Discuss
Answer: (b).Fixed ontology and knowledge representation Explanation:Case based reasoning requires a large case database. It requires a large amount of computational resources for pattern matching and to modify the solution of a close match to satisfy the current problem. Case based reasoning can be implemented in any system provided it has large memory and processing power regardless of ontology and knowledge representation.
Q310.
Temporal reasoning allows a system to reason about its operational characteristics at discrete points in time.

a.

True

b.

False

c.

May be True or False

d.

Can't say

Discuss
Answer: (b).False Explanation:Temporal reasoning allows a system to reason about its operational characteristics during an interval of time. For example, spectrum sensing over a period can help identify intervals of spectrum underutilization within that period of time.
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