Distributed Discrete Power Control in Cellular PCS

Distributed power control is an important issue in wireless networks. Recently, due to the bursty nature of data communication, packet switching is used in cellular systems. In addition, majority of previous power control algorithm assume that the transmitter power level can take values in a continuous domain. However, recent trends such as the GSM standard and QUALCOMM's proposal to the IS-95 standard use a finite number of discretized power levels. These motivate the need to investigate solutions for distributed discrete power control for bursty transmission. Therefore, we propose a probabilistic power adaptation algorithm and analyze its theoretical properties along with the numerical behavior for bursty transmission. We approximate the discrete power control iterations by an equivalent ordinary differential equation (ODE) to prove that the proposed stochastic learning power control algorithm converges to a stable Nash equilibrium. Conditions when more than one stable Nash equilibrium may exist are also studied. Experimental results are presented for several cases and the impact of data burstiness on the proposed algorithm is also concerned.

Transmitter power control is an essential tool for managing the interference in a system constituted by a set of communications nodes. In the absence of a central controller, the decentralized power control is of special interest and may be characterized as a competition among the communication nodes. This indicates the adequacy of the application of noncooperative game theory concepts to the problem. The continuous domain for transmit power is usually assumed, although, in practice, digitally power controlled systems work with discrete power levels. In this work we evaluate the performance of a distributed power control algorithm which is derived from the formulation of the power control problem as a static multi-stage nonzero- sum noncooperative game. Continuous and discrete domains for transmit power are considered.

Background. Power control is a process of adjusting transmitter power output in a communication system to achieve satisfactory performance within the system. In wireless data transfer networks, this process refers to uplink connection, when information about the transmitter power output is sent to the base station, whereupon the power is adjusted in accordance with one or more transmit power control commands received in the downlink. The uplink power control is intended to ensure quality of service declared by the system provider including maintaining a sufficient signal-to-noise ratio and link data rate, reducing interference, overloading, and preserving the battery life. Objective. Whereas the sum of power levels is constrained to the grand total of the powers transmitted in the uplink off all the transmitters, the quality-of-service equalization is a fundamental task. Henceforward, for a set of equidistant power levels, the goal is to achieve the quality-of-service equalization by non-decreasing powers when moving away from the base station with using the distances from the mobiles to the base station. Inasmuch as the uplink power grand total is “allowed”, the sum of all the powers should be as much as closer to the grand total. Methods. Principally, ratios of distances to the base station are calculated using an initial value of the path loss exponent. Then the case of the overloaded network is checked out. After that, the base station power responses are calculated using the ratios and the principle of the equal quality of service, wherein the received uplink power should be closely the same for all the users by every uplink transmission. If the farthest/closest transmitters’ powers are out of the power range, they are set down/up to the proper maximum/minimum. The path loss exponent is decreased if the proper maximum is re-violated. Finally, the base station power responses are rounded to values within a set of power levels. Results. The suggested algorithm deals with powers in watts fitting wireless data transfer networks working in shallow areas (like Wi-Fi, Bluetooth, etc.), for which the number of power levels is relatively great and the range of active uplink transmission powers is relatively narrow. The routine which implements the algorithm still can be optimized depending on the programming environment and paradigm. For instance, C++ and Python will fit for speeding up the performance. Nevertheless, the routine would not sustain the UMTS update frequency, unless a network works with a few tens of users. Conclusions. The uplink power control routine stated with the six algorithmic items effectively equalizes quality of service in shallow wireless data transfer networks, where user uplink powers are constrained to equidistant power levels in watts. It is not a one-step but a multi-step process during which four types of conditions are successively tried to get satisfied. Eventually, the factual sum of all the powers transmitted in the uplink may become “harmlessly” less than the grand total.

Transmitter adaptation in conjunction with power control enables individual users in multi-user wireless systems to meet specific quality of service (QoS) constraints imposed in terms of target values for the users' signal-to-interference+noise-ratio (SINR) at their intended receiver. Existing algorithms for joint transmitter adaptation and power control require knowledge of channel state information (CSI) which is in general difficult to acquire in wireless environments that are subject to fading. In this paper we study the performance of incremental algorithms for joint transmitter adaptation and power control with fading channels by formally defining a probability of outage measure and evaluating it through Monte-Carlo simulations in specific scenarios.

In a wireless, multi-hop sensor network, choosing transmission power levels has an important impact on energy efficiency and network lifetime. Two algorithms for dynamically adjusting transmission power level on a per-node basis are proposed here. Network lifetime, convergence speed as well as resulting network connectivity are used as figures of merit for these two algorithms. They have been evaluated in an indoor sensor environment. The network lifetime metrics of these two local algorithms are also benchmarked against power control algorithms using global information. We show that these local algorithms outperform fixed power level assignment and that the lifetime achieved by them is usually within a factor of two of globally computed solution while being scalable.

In infrastructure-less sensor networks, efficient usage of energy is very critical because of the limited energy available to the sensor nodes. Among various phenomena that consume energy, radio communication is by far the most demanding one. One of the effective ways to limit unnecessary energy loss is to control the power at which the nodes transmit signals. In this paper, we apply game theory to solve the power control problem in a CDMA-based distributed sensor network. We formulate a noncooperative game under incomplete information and study the existence of Nash equilibrium. With the help of this equilibrium, we devise a distributed algorithm for optimal power control and prove that the system is power stable only if the nodes comply with certain transmit power thresholds. We show that even in a noncooperative scenario, it is in the best interest of the nodes to comply with these thresholds. The power level at which a node should transmit, to maximize its utility, is evaluated. Moreover, we compare the utilities when the nodes are allowed to transmit with discrete and continuous power levels; the performance with discrete levels is upper bounded by the continuous case. We define a distortion metric that gives a quantitative measure of the goodness of having finite power levels and also find those levels that minimize the distortion. Numerical results demonstrate that the proposed algorithm achieves the best possible payoff/utility for the sensor nodes even by consuming less power.

A distributed priority-based power and admission control algorithm is presented to address the priority-based gradual removal problem in cellular wireless networks. We assume that there exist two classes of priority for users (high-priority users versus low-priority users) and minimal number of low-priority users should be gradually removed, subject to the constraint that all high-priority users are supported with their target signal-to-interference-plus-noise ratios (SINRs) which is assumed feasible. In our proposed algorithm, each high-priority user rigidly tracks its target-SINR by employing the conventional target-SINR tracking power control algorithm, and each transmitting low-priority user tracks its target-SINR as long as its required transmit power is below a threshold, otherwise it temporarily removes itself. Each removed low-priority user resumes its transmission if the required transmit power to reach its target-SINR goes below a given threshold which is different from the former. Of these two thresholds, whose values are analytically obtained, the former is provided by the base station and the latter is obtained in a distributed manner as a function of the former. We show that the distributed power-update function corresponding to our proposed algorithm has at least one fixed-point which is not unique in general. The convergence point, where our proposed algorithm potentially converges to, depends on initial transmit power levels of users. We also show that our proposed algorithm, at each of its fixed-points, not only provides all high-priority users with their (feasible) target-SINRs but also guarantees that no low-priority user is erroneously removed (i.e., no additional low priority user can be supported along with currently supported users). Furthermore, for the special case of tracking a common target-SINR by all low-priority users, we show that our proposed algorithm minimizes the outage-ratio of low-priority users subject to zero-outage-ratio of high-priority users. Simulation results confirm our analytical developments and show that our proposed priority-based power and admission control algorithm solves the priority-based gradual removal problem efficiently.

Most cellular radio systems provide for the use of transmitter power control to reduce cochannel interference for a given channel allocation. Efficient interference management aims at achieving acceptable carrier-to-interference ratios in all active communication links in the system. Such schemes for the control of cochannel interference are investigated. The effect of adjacent channel interference is neglected. As a performance measure, the interference (outage) probability is used, i.e., the probability that a randomly chosen link is subject to excessive interference. In order to derive upper performance bounds for transmitter power control schemes, algorithms that are optimum in the sense that the interference probability is minimized are suggested. Numerical results indicate that these upper bounds exceed the performance of conventional systems by an order of magnitude regarding interference suppression and by a factor of 3 to 4 regarding the system capacity. The structure of the optimum algorithm shows that efficient power control and dynamic channel assignment algorithms are closely related.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Machine-type communications (MTC) in 5G will be mostly realized using low-cost transceivers with a small, discrete set of possible configurations. This is in contrast to more capable devices with software defined radio capabilities that support configurations over a practically continuous domain. We find that existing theoretical work assuming continuous domains cannot be immediately applied to such constrained MTC devices. Therefore, we propose a methodology that guides researchers in the process of developing effective MTC wireless systems with devices that have restricted capabilities. By following the proposed methodology, existing theories can be experimentally evaluated and replicated. We show how this methodology can be applied for developing and evaluating efficient interference mitigation systems on a case study using devices that support only discrete transmit power levels. In this case study, we formulate an interference mitigation problem and a corresponding game-theoretic formalism that can support discrete output power levels. We validated some of the existing results obtained analytically and in simulation and we also found that: (1) in practice, devices have to be penalized stronger than in theory to determine them to select all the available transmit power levels; (2) in practice, for the discrete case of the power allocation algorithm, the convergence speed does not increase exponentially with the number of devices; and (3) the proposed power selection algorithm converges in two to four iterations. Similarly, as in the presented case study, the methodology can be used to adapt and test other resource management solutions in an operating environment with real-world restrictions.

Wireless communications systems have transmitter power control as an essential tool for managing radio resources. Decentralized power control is of special interest and may be characterized as a competition among the communication nodes. This indicates the adequacy of the application of noncooperative game theory concepts to the problem. In this work, we formulate a general framework to the distributed power control problem, derive a power control algorithm and use a simple prediction method to improve its performance.

Wireless local area networks (WLANs) based on IEEE 802.11 standard have been rapidly growing. With limitations of the standard and rapid increase in wireless application demand, the air interface acts as bottleneck even in high-speed WLAN standards such as IEEE 802.11a and 802.11g with expected data transmission rates of up to 54 Mbps. To improve the overall performance of IEEE 802.11 WLAN under large deployment and heavy application demand environment, radio resource management (RRM) algorithms based on power control have been investigated and tested through simulation. The results show that controlling the wireless terminal's (WT) transmitter power to an optimum power level is important. This results into an increased data throughput in a WLAN network and when the transmitter power level of a WT is increased beyond the optimum power level, the throughputs drop drastically no matter how high the WT's transmitter power may be increased.

With cellular phones mass-market consumer items, the next frontier is mobile multimedia communications. This situation raises the question of power control for information sources other than voice. To explore this issue, we use the concepts and mathematics of microeconomics and game theory. In this context, the quality of service of a telephone call is referred to as the and the distributed power control problem for a CDMA telephone is a noncooperative game. The power control algorithm corresponds to a strategy that has a locally optimum operating point referred to as a Nash equilibrium. The telephone power control algorithm is also efficient in the terminology of game theory. When we apply the same approach to power control in wireless data transmissions, we find that the corresponding strategy, while locally optimum, is not Pareto efficient. Relative to the telephone algorithm, there are other algorithms that produce higher utility for at least one terminal, without decreasing the utility for any other terminal. This paper presents one such algorithm. The algorithm includes a price function, proportional to the transmitter power. The price acts as a tax on the utility of a transmission. When terminals adjust their power levels to maximize the net utility (utility price), they arrive at lower power levels and higher utility than they achieve when they individually strive to maximize utility.

In the wireless body sensor network system (WB-SNS), saving energy in sensor nodes is a high priority. As an energy-saving method for sensor nodes, the transmission power control (TPC) algorithm has been developed. The TPC technique is a method of saving energy by adjusting the transmission power of the radio chip inside a sensor node. Numerous TPC algorithms exist, but in this paper, the dynamic TPC algorithm was analyzed intensively. The existing dynamic TPC algorithm utilizes a linear equation to find the optimal transmission power level (TPL). However, the existing dynamic TPC algorithm may operate well when the channel condition is good but it could estimate the TPL inaccurately due to the generation of an incorrect linear equation when the channel conditions are bad. Therefore, to solve the problem described above, this paper proposes an extended dynamic TPC algorithm that can estimate errors earlier based on slope values, which can be extracted at the time of generation of the linear equation, and estimate transmission power better by reutilizing the existing transmission power to create a more accurate equation. Through an experiment, this paper also proves that the extended dynamic TPC algorithm showed better performance compared to the existing dynamic TPC algorithm.

Traditional beamforming and power control algo-rithms in cognitive radio (CR) are based on the assumption of perfect channel state information (CSI) however; this may lead to performance degradation in realistic systems.In this paper, the problem of joint beamforming and power control is investigated in underlay CR networks with imperfect CSI.Our objective is to maximize the sum utility of secondary users (SUs) under the primary users (PUs) interference power constraints and the transmission power constraint of SUs.First, the joint beamforming and power control problem is formulated under game theory framework, where the SUs compete with each other over the beamforming vectors and transmission power level made available by the PUs.Moreover, the channel uncertainty is described using ellipsoid sets and the interference power constraints can be converted into robust interference power constraints.Besides, Nash equilibrium (NE) is considered as the solution of this game.Finally, simulation results show that the proposed scheme can converge to a locally optimal pair of beamforming vector and transmission power level in the presence of channel uncertainty.

Transmitter power control is a necessity to combat “near-far” problems in radio systems using receivers with limited “dynamic range” (such as conventional DSCDMA systems). Transmitter power control, however, can also be used to control cochannel interference, i.e. interference from other users using the same channel (code or time slot). For this purpose, it has been shown that the optimum transmitter power configuration is found by solving an eigenvalue problem. The paper reviews some recent results in this area. The basic models as well as the concepts of achievable C/I, up/down-link equivalence and C/Ibalancing are introduced. Both the interference limited (noise-less) case as well as models including thermal noise are treated. Results show that substantial improvements in system capacity can be achieved, particulary in conjunction with Dynamic Channel Allocation. The optimum power control schemes are shown to be robust against implementational shortcomings.