In a wireless network, mobile nodes (MNs) repeatedly perform tasks such as layer 2 (L2) handoff, layer 3 (L3) handoff and authentication. These tasks are critical, particularly for real-time applications such as VoIP. We propose a novel approach, namely Cooperative Roaming (CR), in which MNs can collaborate with each other and share useful information about the network in which they move. We show how we can achieve seamless L2 and L3 handoffs regardless of the authentication mechanism used and without any changes to either the infrastructure or the protocol. In particular, we provide a working implementation of CR and show how, with CR, MNs can achieve a total L2+L3 handoff time of less than 16 ms in an open network and of about 21 ms in an IEEE 802.11i network. We consider behaviors typical of IEEE 802.11 networks, although many of the concepts and problems addressed here apply to any kind of mobile network.
More recently, cooperative approaches have been proposed in the network community. , @cite_2 show how cooperation amongst MNs can be beneficial for all the MNs in the network in terms of bit-rate, coverage and throughput. Each MN builds a table in which possible helpers for that MN are listed. If an MN has a poor link with the AP and its bit-rate is low, it sends packets to the helper who relays them to the AP. The advantage in doing this is that the link from the MN to the helper and from the helper to the AP is a high bit-rate link. In this way the MN can use two high bit-rate links via the helper instead of the low bit-rate one directly to the AP.
@cite_2
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In multiuser MIMO (MU-MIMO) networks, the optimal bit rate of a user is highly dynamic and changes from one packet to the next. This breaks traditional bit rate adaptation algorithms, which rely on recent history to predict the best bit rate for the next packet. To address this problem, we introduce TurboRate, a rate adaptation scheme for MU-MIMO LANs. TurboRate shows that clients in a MU-MIMO LAN can adapt their bit rate on a per-packet basis if each client learns two variables: its SNR when it transmits alone to the access point, and the direction along which its signal is received at the AP. TurboRate also shows that each client can compute these two variables passively without exchanging control frames with the access point. A TurboRate client then annotates its packets with these variables to enable other clients to pick the optimal bit rate and transmit concurrently to the AP. A prototype implementation in USRP-N200 shows that traditional rate adaptation does not deliver the gains of MU-MIMO WLANs, and can interact negatively with MU-MIMO, leading to low throughput. In contrast, enabling MU-MIMO with TurboRate provides a mean throughput gain of 1.7x and 2.3x, for 2-antenna and 3-antenna APs respectively. In wireless local area networks, often a station can potentially associate with more than one access point (AP). Therefore, a relevant question is which AP to select 'best' from a list of candidate ones. In IEEE 802.11, the user simply associates to the AP with the strongest received signal strength. However, this may result in a significant load imbalance between several APs. Moreover, the multi-rate flexibility provided by several IEEE 802.11 variants can cause low bit rate stations to negatively affect high bit rate ones and consequently degrade the overall network throughput. This paper investigates the various aspects of 'best' AP selection for IEEE 802.11 systems. In detail, we first derive a new decision metric which can be used for AP selection. Using this metric, we propose two new selection mechanisms which are decentralised in the sense that the decision is performed by each station, given appropriate status information of each AP. In fact, only few bytes of status information have to be added to the Beacon and Probe Response frames which does not impose significant overhead. We show that our mechanism improves mean quality of service of all stations and better utilises network resources compared to the conventional one implemented today in IEEE 802.11 devices. Also, the schemes are appealing in terms of stability and provide their performance improvement even for denser or lighter network configurations. A generalization of the Gaussian dirty-paper problem to a multiple access setup is considered. There are two additive interference signals, one known to each transmitter but none to the receiver. The rates achievable using Costa's strategies (i.e. by a random binning scheme induced by Costa's auxiliary random variables) vanish in the limit when the interference signals are strong. In contrast, it is shown that lattice strategies ("lattice precoding") can achieve positive rates independent of the interferences, and in fact in some cases - which depend on the noise variance and power constraints - they are optimal. In particular, lattice strategies are optimal in the limit of high SNR. It is also shown that the gap between the achievable rate region and the capacity region is at most 0.167 bit. Thus, the dirty MAC is another instance of a network setup, like the Korner-Marton modulo-two sum problem, where linear coding is potentially better than random binning. Lattice transmission schemes and conditions for optimality for the asymmetric case, where there is only one interference which is known to one of the users (who serves as a "helper" to the other user), and for the "common interference" case are also derived. In the former case the gap between the helper achievable rate and its capacity is at most 0.085 bit. We consider the design of a scheduling policy for video streaming in a wireless network formed by several users and helpers (e.g., base stations). In such networks, any user is typically in the range of multiple helpers. Hence, an efficient policy should allow the users to dynamically select the helper nodes to download from and determine adaptively the quality level of the requested video segment. In order to obtain a tractable formulation, we follow a “divide and conquer” approach. First, we formulate a network utility maximization (NUM) problem where the network utility function is a concave and component-wise nondecreasing function of the time-averaged users' requested video quality index, and maximization is subject to the stability of all queues in the system. Second, we solve the NUM problem by using a Lyapunov drift plus penalty approach, obtaining a dynamic adaptive scheme that decomposes into two building blocks: 1) adaptive video quality and helper selection (run at the user nodes); and 2) dynamic allocation of the helper-to-user transmission rates (run at the help nodes). Our solution provably achieves NUM optimality in a strong per-sample path sense (i.e., without assumptions of stationarity and ergodicity). Third, we observe that, since all queues in the system are stable, all requested video chunks shall be eventually delivered. Fourth, in order to translate the requested video quality into the effective video quality at the user playback, it is necessary that the chunks are delivered within their playback deadline. This requires that the largest delay among all queues at the helpers serving any given user is less than the pre-buffering time of that user at its streaming session startup phase. In order to achieve this condition with high probability, we propose an effective and decentralized (albeit heuristic) scheme to adaptively calculate the pre-buffering and re-buffering time at each user. In this way, the system is forced to work in the “smooth streaming regime,” i.e., in the regime of very small playback buffer underrun rate. Through simulations, we evaluate the performance of the proposed algorithm under realistic assumptions of a network with densely deployed helper and user nodes, including user mobility, variable bit-rate video coding, and users joining or leaving the system at arbitrary times.