Flow-level performance and capacity of wireless networks with user mobility

Abstract

The performance evaluation of wireless networks is severely complicated by the specific features of radio communication, such as highly variable channel conditions, interference issues, and possible hand-offs among base stations. The latter elements have no natural counterparts in wireline scenarios, and create a need for novel performance models that account for the impact of these characteristics on the service rates of users.Motivated by the above issues, we review several models for characterizing the capacity and evaluating the flow-level performance of wireless networks carrying elastic data transfers. We first examine the flow-level performance and stability of a wide family of so-called α-fair channel-aware scheduling strategies. We establish that these disciplines provide maximum stability, and describe how the special case of the Proportional Fair policy gives rise to a Processor-Sharing model with a statedependent service rate. Next we turn attention to a network of several base stations with inter-cell interference. We derive both necessary and sufficient stability conditions and construct lower and upper bounds for the flow-level performance measures. Lastly we investigate the impact of user mobility that occurs on a slow timescale and causes possible hand-offs of active sessions. We show that the mobility tends to increase the capacity region, both in the case of globally optimal scheduling and local

User’s mobility effect on the performance of wireless cellular networks serving elastic traffic

“…On the other hand, Fig. 1 (down) shows that the stability condition for the three values t/l = 1, 10, 100 is the same as that for the analytical model with infinite mobility in accordance with the theoretical result of [11,Sect. 4…”

“…In particular, when the network approaches instability, user mobility is being frozen. A more suitable model, where mobility is independent from users services, is studied in [16,17], whose results are summarized in [11,Sect. 4].…”

A Queueing Theoretic Approach to the Dimensioning of Wireless Cellular Networks Serving Variable-Bit-Rate Calls

“…. , ρ J ) is proportional to the mobility distribution; then, since the arithmetic mean is larger than the harmonic mean, we deduce that the critical traffic with mobility is larger than the critical traffic in the no mobility case, which is coherent with [16,Sec. 4…”

“…The case when the average user's speed is finite and nonnull is analytically intractable. However, it may be bounded by the two extreme cases of no mobility and infinite mobility since mobility improves performance, as proven in [16,Sec. 4.2.2].…”

“…Once the given fix-point problem is solved, the ratio θ := ρ ρ c (16) is called the load of the system. Remark 4: Note that the load depends on the traffic demand; therefore, we cannot consider these two parameters as independent inputs when evaluating the QoS of the users.…”