Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

It is shown that, particularly for terrestrial cellular telephony, the interference-suppression feature of CDMA (code division multiple access) can result in a many-fold increase in capacity over analog and even over competing digital techniques. A single-cell system, such as a hubbed satellite network, is addressed, and the basic expression for capacity is developed. The corresponding expressions for a multiple-cell system are derived. and the distribution on the number of users supportable per cell is determined. It is concluded that properly augmented and power-controlled multiple-cell CDMA promises a quantum increase in current cellular capacity. >

On the capacity of a cellular CDMA system

Newness and distinctiveness is claimed in the features of ornamentation as shown inside the broken line circle in the accompanying representation.

Display screen or portion thereof with graphical user interface

We developed a cross-layer design which combines adaptive modulation and coding at the physical layer with a truncated automatic repeat request protocol at the data link layer, in order to maximize spectral efficiency under prescribed delay and error performance constraints. We derive the achieved spectral efficiency in closed-form for transmissions over Nakagami-m block fading channels. Numerical results reveal that retransmissions at the data link layer relieve stringent error control requirements at the physical layer, and thereby enable considerable spectral efficiency gain. This gain is comparable with that offered by diversity, provided that the maximum number of transmissions per packet equals the diversity order. Diminishing returns on spectral efficiency, that result when increasing the maximum number of retransmissions, suggest that a small number of retransmissions offers a desirable delay-throughput tradeoff, in practice.

Cross-Layer combining of adaptive Modulation and coding with truncated ARQ over wireless links

In a data communication system capable of variable rate transmission, high rate packet data transmission improves utilization of the forward link and decreases the transmission delay. Data transmission on the forward link is time multiplexed and the base station transmits at the highest data rate supported by the forward link at each time slot to one mobile station. The data rate is determined by the largest C/I measurement of the forward link signals as measured at the mobile station. Upon determination of a data packet received in error, the mobile station transmits a NACK message back to the base station. The NACK message results in retransmission of the data packet received in error. The data packets can be transmitted out of sequence by the use of sequence number to identify each data unit within the data packets.

Method and apparatus for high-rate packet data transmission

A mobile terminal has a controller, a display and an input device connected to the controller, and a digital object browser for browsing among a plurality of available digital objects. A first representation and a second representation of each of the digital objects are presentable on a display screen. The first representations of the digital objects are presented along a predefined path on the display screen, the first representation of a focused one of the digital objects being shown in a focus area on the display screen. For the focused digital object, the second representation thereof is presented in a preview area on the display screen. In response to a user input given on the input device, a desired scrolling operation is performed among the first representations presented, thereby shifting the positions of the first representations on the display screen along the path accordingly, updating the focus area to reflect a change in focus from the focused digital object to another digital object by presenting in the focus area the first representation of the other digital object, as well as updating the preview area correspondingly by presenting therein the second representation of the other digital object.

Mobile communications terminal and method therefore

A two-dimensional optimization between adaptive modulation and coding (AMC) and multi-code transmission targets the best bit rate at which highest useable modulation and coding scheme (MCS, 12 fig. 1)) achieves a maximum allowed number of channelization codes or less. The optimized MCS and number of channelization codes at a best bit rate is communicated from a transmitter to a receiver possibly also with power information for instance using a transport format combination indicator (TFCI fig. 5).User equipment may play the role of carrying out the optimization and signaling the result to the Node B or may instead receive the result as carried out by the Node B or RNC in response to a radio link quality measurement sent from the UE to the Node B.

Optimization of mcs and multicode with tfci signaling

If only good coverage is required in GSM networks, high frequency reuse factors can be used. In that case the network capacity is directly determined by the number of available time slots, and no advanced radio control algorithms are needed. If high capacity is required and fractional loading is used in GSM networks, advanced radio resource management algorithms are required. Sophisticated network simulators are needed for algorithm development in GSM base station systems. Also, finding out the capacity for accurate network planning is difficult without radio network simulators. In UMTS the need for an advanced radio network simulator will further increase because of new services, higher bit rates, multiplexing of services, and because of the possible asymmetric capacity requirement between uplink and downlink. Additionally, the UTRA WCDMA air interface is more dynamic than the GSM air interface, and therefore, optimised radio resource management algorithms need to be developed to fully exploit the WCDMA capabilities. The algorithm development for highly loaded dynamic networks is not possible without accurate modelling in computer simulations. In this paper the modelling principles of a WCDMA radio network simulator are presented.

/pdf/advanced-wcdma-radio-network-simulator-3m07lo3w6p.pdf

Advanced wcdma radio network simulator

An apparatus and method for deciding whether to perform link adaptation for communication transmitted from a first communication device (12) to a second communication device (11), where the second communication device (11) examines a signal received from the first communication device (12) and provides a first indication of the quality of the signal. The method includes steps of: recording at least a first indication of the quality of the signal as received by the second communication device (11); providing a second indication of the quality of the signal based on the at least one first indication of the quality of the signal; and deciding whether to perform link adaptation based on the second indication of the quality of the signal. The first indication of the signal quality is for example a signal to interference ratio (SIR) estimate. Often, the second indication of the signal quality is a changed SIR target value.

Method for determining whether to perform link adaptation in wcdma communications

The invention relates to a radio system and a method of timing an interfrequency handover in a WCDMA cellular radio system where data transmission between a terminal equipment and a base station employs several adjacent alternative frequency bands (F1 - F3, F4 - F6), and which comprises several different base stations (BTS1, BTS2) the coverage areas of which overlap at least partly, the base stations utilizing frequency band groups (400, 402) that differ from one another and that are situated next to one another in the frequency range, in which system a terminal equipment (MS11) communicates with a first base station (BTS1) on a first frequency band (F3) and measures the strength of a first signal it has received from the base station, and wherein the terminal equipment measures the strength of a second signal transmitted by a second base station (BTS2) on a second, adjacent frequency band (F4). In order to perform a handover at a correct moment, the terminal equipment (MS11) compares the measurement results of the first and the second signal, and if the second signal exceeds the first signal by a given threshold value, the terminal equipment signals a handover request to the first base station (BTS1).

Timing of handover

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