With the increasing availability of flexible wireless 802.11 devices, the potential exists for users to selfishly manipulate their channel access parameters and gain a performance advantage. Such practices can have a severe negative impact on compliant stations. To enable access points to counteract these selfish behaviours and preserve fairness in wireless networks, in this paper we propose a policing mechanism that drives misbehaving users into compliant operation without requiring any cooperation from clients. This approach is demonstrably effective against a broad class of misbehaviours, soundly-based, i.e. provably hard to circumvent and amenable to practical implementation on existing commodity hardware.
The solution we propose leverages our previous design @cite_20 , but differs in that here: we aim to control the transmission attempt rate instead of throughput, thus seeking to equalise stations' air time @cite_22 . By driving the channel access probabilities of all clients to the same value, regardless of the contention parameters they employ, we effectively preserve short-term fairness. We allow carrying forward penalties, thus also achieve long-term fairness. Finally, we guarantee that the mechanism cannot be gamed by greedy users that detect its operation.
@cite_22 @cite_20
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We investigate the optimal selection of minimum contention window values to achieve proportional fairness in a multirate IEEE 802.11e test-bed. Unlike other approaches, the proposed model accounts for the contention-based nature of 802.11's MAC layer operation and considers the case where stations can have different weights corresponding to different throughput classes. Our test-bed evaluation considers both the long-term throughput achieved by wireless stations and the short-term fairness. When all stations have the same transmission rate, optimality is achieved when a station's throughput is proportional to its weight factor, and the optimal minimum contention windows also maximize the aggregate throughput. When stations have different transmission rates, the optimal minimum contention window for high rate stations is smaller than for low rate stations. Furthermore, we compare proportional fairness with time-based fairness, which can be achieved by adjusting packet sizes so that low and high rate stations have equal successful transmission times, or by adjusting the transmission opportunity (TXOP)limit so that high rate stations transmit multiple back-to-back packets and thus occupy the channel for the same time as low rate stations that transmit a single packet. The test-bed experiments show that when stations have different transmission rates and the same weight, proportional fairness achieves higher performance than the time-based fairness approaches, in terms of both aggregate utility and throughput. In this paper we propose the following approach to the dimensioning of the radio part of the downlink in OFDMA networks. First, we use information theory to characterize the bit-rate in the channel from a base station to its mobile. It depends on the power and bandwidth allocated to this mobile. Then, we describe the resource (power and bandwidth) allocation problem and characterise feasible configurations of bit-rates of all users. As the key element, we propose some particular sufficient condition (in a multi-Erlang form) for a given configuration of bit-rates to be feasible. Finally, we consider an Erlang's loss model, in which streaming arrivals whose admission would lead to the violation of this sufficient condition are blocked and lost. In this model, the blocking probabilities can be calculated using Kaufman-Roberts algorithm. We propose it to evaluate the minimal density of base stations assuring acceptable blocking probabilities for a streaming traffic of a given load per surface unit. We validate this approach by comparison of the blocking probabilities to these simulated in the similar model in which the admission control is based on the original feasibility property (instead of its sufficient condition). Our sufficient bit-rate feasibility condition can also be used to dimension the network with respect to the elastic traffic. This paper studies the use of the proportional fairness (PF) utility function as the basis for capacity allocation and scheduling in multi-channel multi-rate wireless networks. Examples of applications include (i) access point (AP) association and transmission scheduling in large-scale IEEE 802.11 networks; and (ii) subcarrier assignment and transmission scheduling in orthogonal frequency division multiplexing (OFDM) cellular networks. The contributions of this paper are twofold. First, we study the fundamental properties and physical economic interpretation of PF. We show by general mathematical arguments that PF leads to equal airtime allocation to individual users for the single-channel case; and equal equivalent airtime allocation to individual users for the multi-channel case, where the equivalent airtime enjoyed by a user is a weighted sum of the airtimes enjoyed by the user on all channels, with the weight of a channel being the price or value of that channel. In addition, we establish the Pareto efficiency of PF solutions and derive characteristics of PF solutions that are useful for the construction of PF algorithms. Second, we generate numerical results for application (i) above. We find that the PF solution simultaneously achieves higher system throughput, better fairness, and lower outage probability with respect to the default AP-association and medium access control (MAC) protocol adopted in today's 802.11 commercial products. We consider the problem of resource allocation in downlink OFDMA systems for multi service and unknown environment. Due to users' mobility and intercell interference, the base station cannot predict neither the Signal to Noise Ratio (SNR) of each user in future time slots nor their probability distribution functions. In addition, the traffic is bursty in general with unknown arrival. The probability distribution functions of the SNR, channel state and traffic arrival density are then unknown. Achieving a multi service Quality of Service (QoS) while optimizing the performance of the system (e.g. total throughput) is a hard and interesting task since it depends on the unknown future traffic and SNR values. In this paper we solve this problem by modeling the multiuser queuing system as a discrete time linear dynamic system. We develop a robust H∞ controller to regulate the queues of different users. The queues and Packet Drop Rates (PDR) are controlled by proposing a minimum data rate according to the demanded service type of each user. The data rate vector proposed by the controller is then fed as a constraint to an instantaneous resource allocation framework. This instantaneous problem is formulated as a convex optimization problem for instantaneous subcarrier and power allocation decisions. Simulation results show small delays and better fairness among users.