Analytical evaluation of QoS in the downlink of OFDMA wireless cellular networks serving streaming and elastic traffic

Abstract

The objective of the present paper is to build analytical methods for the evaluation of the quality of service (QoS) perceived by the users in the downlink of OFDMA wireless cellular networks serving streaming and elastic traffic.To do so, we first describe the resource (power and bandwidth) allocation problem and characterize its feasibility by some reference feasibility condition (FC). The QoS for FC may only by evaluated by simulations. To cope with this difficulty, we propose some sufficient feasibility condition (SFC) having the multi-Erlang form which permits analytical evaluation of the QoS. In particular, the blocking probability for streaming users can be calculated using Kaufman-Roberts algorithm. For elastic users, explicit expressions of the throughputs are obtained by using a multi-class processor sharing model. Moreover, we study the QoS in a network serving simultaneously streaming and elastic traffic.We validate this approach by comparing SFC's blocking probabilities to these simulated for FC. Moreover, we illustrate the proposed approach by solving the dimensioning problem; i.e., evaluating the minimal density of base stations assuring acceptable QoS of a given traffic demand per surface unit.

Effect of opportunistic scheduling on the quality of service perceived by the users in OFDMA cellular networks

“…It is not in the scope of this paper to make a thorough review of this literature. For some references on this field see for example those cited in [17]. Here we remark only those results that are directly related to our approach.…”

“…Our starting point is our work in [17] assuming AWGN channels between the users and the base stations. Coluccia et al [11] study optimality and fairness in resource allocation.…”

“…Such power adaptation may be either a power control to transmit just what is necessary to compensate the interference as described in [17] or a water filling attempting to maximize the cell capacity by allocating the powers among the users according to their fadings as described in [20, Theorem 2 p.119 ].…”

“…Proposition 5 Consider the opportunistic bit-rate allocation (17) and denote byγ ′ the expectation of γ ′ m with respect to the user's location m uniformly distributed in the cell. If the traffic demand per cell…”

Automatic UMTS system resource dimensioning based on service traffic analysis

“…Blaszczyszyn and Karray [23] use an Erlang's loss model to dimension downlink in OFDMA networks. Karray [24] particularizes the downlink QoS study to streaming and elastic traffics.…”