This paper addresses the problem of dynamic resource allocation in a multiservice direct-sequence code-division multiple-access (DS-CDMA) wireless network supporting real-time (RT) and nonreal-time (NRT) communication services. For RT users, a simple transmission power allocation strategy is assumed that maximizes the amount of capacity available to NRT users without violating quality of service requirements of RT users. For NRT users, a joint transmission power and spreading gain (transmission rate) allocation strategy, obtained via the solution of a constrained optimization problem, is provided. The solution maximizes the aggregate NRT throughput, subject to peak transmission power constraints and the capacity constraint imposed by RT users. The optimization problem is solved in a closed form, and the resulting resource allocation strategy is simple to implement as a hybrid CDMA/time-division multiple-access strategy. Numerical results are presented showing that the optimal resource allocation strategy can offer substantial performance gains over other conventional resource allocation strategies for DS-CDMA networks.

In this paper, we are interested in developing a control algorithm or transmission scheme for wireless data communication over time-varying channels with memory that determines when to attempt a transmission and at what power level, so as to achieve a suitable balance between throughput and energy consumption; and studying the effect of channel memory on the performance, design, and structure of this scheme. The channel state is not directly observable, and thus the transmission decisions must be based on partial or incomplete channel state information, provided to the user by the base station over a feedback channel. More specifically, we assume that the delayed channel state information is provided to the sender at the end of those time slots during which packet transmissions are made. We cast the problem as a partially observable Markov decision process. Despite the difficulty associated with the incomplete channel information, we obtain the optimal transmission scheme, which can be interpreted as a back-off rule: at the end of the transmission, depending on the channel quality during the past time slot, the transmission may be suspended for some time slots, which can be computed in advance. Numerical results are also provided.

We set up a general framework for evaluating the accuracy of the Markov model for the packet transmission over a wireless channel. Unlike most previous studies, a broad range of fading/shadowing models and coding/modulation schemes are included in our framework. We study the effect of both the packet length and mobile speed. Our analysis is based on the approximate normal distribution and the second order statistics in both the time and frequency domain. We make the generalized conclusion that the Markov model is most accurate when the mobile movement is either very fast (packets i.i.d.) or very slow (packets highly correlated). For the first time, we explicitly show that at intermediate speed when the correlation length in the fading is comparable with the packet length, the accuracy of the Markov model is highly dependent on the specific bit level model. This intermediate range changes with the packet length. A long packet will make this range narrow and occur at low speed.

Of increasing importance is estimation of internal link parameters in communications networks. Multicast probes are a way to gather statistics about internal links from edge node measurements. The problem of estimating link loss probabilities for a multicast distribution tree is examined. Our model assumes loss statistics are distributed to session participants by a network protocol such as RTCP. We propose a decentralized algorithm for ML estimation of the link loss probabilities in a chain of nodes rooted at the source node of the multicast distribution tree and terminating at a given leaf. An expression for the Cramer-Rao bound and an approximate form for the probability distribution function of the estimator are given. The performance of the algorithm is evaluated using computer simulations for a bottleneck detection application.

We study the radio resource allocation problem of distributed joint transmission power control and spreading gain allocation in a DS-CDMA mobile data network. The network consists of K base stations and M wireless data users. The data streams generated by the users are treated as best-effort traffic, in the sense that there are no pre-specified constraints on the quality of the radio channels. We are interested in designing a distributed algorithm that achieves maximal (or near-maximal in some reasonable sense) aggregate throughput, subject to peak power constraints. We provide an algorithm where base stations coordinate in a distributed fashion to control the powers and spreading gains of the users, and show that it converges to a Nash equilibrium point. In general, there may be multiple equilibrium points; however, certain structural properties of the throughput expression can be exploited to significantly trim the search space and induce an ordering on the users in each cell. The numerical results indicate that with these modifications, the algorithm frequently converges in just a few iterations to the throughput maximizing (globally optimal) power and spreading gain allocation.

We develop a control algorithm or transmission scheme for wireless data communication over time-varying channels with memory which determines when to attempt a transmission so as to achieve a suitable balance between throughput and energy consumption; and we study the effect of channel memory on the performance, design, and structure of this scheme. The channel state is not directly observable, and thus the transmission decisions must be based on ACK/NAK information provided over a feedback channel. We cast the problem as a partially observable Markov decision process, and derive the optimal transmission scheme. Numerical results are also provided comparing performance of the optimal scheme with some other existing mechanisms.

The decision to attempt or suspend transmission is cast as a stochastic control problem with imperfect state information. By employing a dynamic programming formulation, the tradeoff between high throughput and energy efficiency is resolved in a flexible cost structure over which the optimization is performed. The optimal policy is derived and shown to be a threshold rule that varies with the memory present in the error process. A suboptimal implementation of the policy indicates that the protocol always favors attempting transmissions when the memory is low. As the channel memory increases, the protocol suspends transmission for longer durations after a packet failure.

Article Optimality of greedy power control and variable spreading gain in multi-class CDMA mobile networks Share on Authors: Seong-Jun Oh Electrical Engineering and Computer Science Department, University of Michigan, 1301 Beal Ave., Ann Arbor, MI Electrical Engineering and Computer Science Department, University of Michigan, 1301 Beal Ave., Ann Arbor, MIView Profile , Kimberly M. Wasserman Electrical Engineering and Computer Science Department, University of Michigan, 1301 Beal Ave., Ann Arbor, MI Electrical Engineering and Computer Science Department, University of Michigan, 1301 Beal Ave., Ann Arbor, MIView Profile Authors Info & Claims MobiCom '99: Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networkingAugust 1999 Pages 102–112https://doi.org/10.1145/313451.313498Online:01 August 1999Publication History 38citation813DownloadsMetricsTotal Citations38Total Downloads813Last 12 Months2Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

In this paper, we consider a DS-CDMA mobile network supporting real-time and non-real-time services. We study how the delay tolerance of non-real-time traffic can be exploited by allowing both transmission power control and variable spreading gain (transmission rate) control. Two control mechanisms adapt the received energy per bit to the current channel conditions and efficiently manage the multiple access interference so as to optimize performance. We provide the jointly optimal power and spreading gain allocation strategy of non-real-time sources. Our strategy maximizes non-real-time throughput subject to constraints on peak transmission power and protects QoS of real-time-services. We show that under the optimal strategy, the optimal spreading gains are inverse linear in the signal to interference plus noise ratio (SINR), and transmission power is allocated to the non-real-time sources in decreasing order of channel gain according to a greedy control strategy. We also present numerical results comparing the throughput and delay performance of the optimal strategy with other common strategies; the optimal strategy can offer substantial performance gains.

In this paper, we consider a direct-sequence code division multiple access (DS-CDMA) network consisting of a single radio access point and a collection of wireless terminals. The network offers two classes of service: class-1 (real-time) and class-2 (reliable). We are interested in studying the effect of dynamic spreading gain control (SGC) on the dynamics of multiple access interference (MAI), spectral efficiency, and the quality of service (QoS) experienced by each service class. We first consider a time-slotted system in which class-2 terminals operate in a random access fashion. We show that under optimal (throughput maximizing) dynamic SGC: (1) the optimal retransmission probability is equal to one, and (2) the optimal spreading gain increases linearly, or equivalently, the transmission rate decreases inverse linearly, as the MAI level increases. We then model the system as a continuous-time finite-source queueing system with processor sharing, and obtain an explicit (closed-form) expression for the stationary distribution of the number of active class-1 and class-2 terminals, that is, the MAI level. This distribution is used to derive expressions for various QoS measures and define a capacity or admissible region. The results obtained by simulation and analysis are in extremely close agreement. This work contributes to a better understanding of the relationships between QoS, multiple access interference, and allocation of radio resources in DS-CDMA networks.

We consider a direct-sequence code division multiple access (DS-CDMA) network consisting of a single base station and a collection of mobile wireless terminals. There are two classes of traffic: voice and data. We provide a spectrally efficient multiple access protocol based on the dynamic control of the spreading gain of the data terminals. We also obtain expressions for key quality of service (QOS) metrics in terms of the number of active voice and data terminals. This work contributes to a better understanding of the relationships between the QOS, number of active terminals, and the allocation of radio resources in DS-CDMA networks.

We consider a time-slotted DS-CDMA network consisting of a single radio access point and a finite number of mobile wireless terminals. The terminals generate non-real-time packet data and transmit, subject to constraints on the average energy consumed per packet transmission, in a random access fashion over a common broadband channel to the access point using different spreading codes. As in narrowband ALOHA systems, the performance of random access spread spectrum networks can be severely hampered due to saturation effects caused by inherent bistable behavior. We study the effect of dynamic spreading gain control on the stability and throughput properties of the network. We first impose no energy constraints and provide an optimal (throughput maximizing) algorithm under which (i) the asymptotically optimal retransmission probability is equal to one, and (ii) the optimal spreading gain increases linearly as the multiaccess interference (MAI) level increases, or equivalently, the transmission rate decreases inverse linearly as the MAI level increases. We also obtain a simple closed-form expression for the asymptotic throughput as the number of backlogged terminals becomes large. We then impose energy constraints and modify the optimal algorithm by controlling both the spreading gain and retransmission probability of the terminals. The resulting algorithm may or may not eliminate bistability; however, in either case, it achieves high throughput.