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.
, @cite_6 introduce a location sensing mechanism based on cooperative behavior among stations. Stations share location information about other stations and about landmarks so to improve position prediction and reduce training.
@cite_6
2158442058
We introduce a framework to study slotted Aloha with cooperative base stations. Assuming a geographic-proximity communication model, we propose several decoding algorithms with different degrees of base stations' cooperation (non-cooperative, spatial, temporal, and spatio-temporal). With spatial cooperation, neighboring base stations inform each other whenever they collect a user within their coverage overlap; temporal cooperation corresponds to (temporal) successive interference cancellation done locally at each station. We analyze the four decoding algorithms and establish several fundamental results. With all algorithms, the peak throughput (average number of decoded users per slot, across all base stations) increases linearly with the number of base stations. Further, temporal and spatio-temporal cooperations exhibit a threshold behavior with respect to the normalized load (number of users per station, per slot). There exists a positive load @math , such that, below @math , the decoding probability is asymptotically maximal possible, equal the probability that a user is heard by at least one base station; with non-cooperative decoding and spatial cooperation, we show that @math is zero. Finally, with spatio-temporal cooperation, we optimize the degree distribution according to which users transmit their packet replicas; the optimum is in general very different from the corresponding optimal distribution of the single-base station system. We present the cooperative location-sensing system (CLS), an adaptive location-sensing system that enables devices to estimate their position in a self-organizing manner without the need for an extensive infrastructure or training. Hosts cooperate and share positioning information. CLS uses a grid representation that allows an easy incorporation of external information to improve the accuracy of the position estimation. We evaluated the performance of CLS via simulation and investigated the impact of the density of landmarks, degree of connectivity, range error, and grid resolution on the accuracy. We found that the average error is less than 2 of the transmission range, when used in a terrain with 20 of the hosts to be landmarks, average network connectivity above 7, and distance estimation error equal to 5 of the transmission range. Motivated by the ongoing discussion on coordinated multipoint in wireless cellular standard bodies, this paper considers the problem of base station cooperation in the downlink of heterogeneous cellular networks. The focus of this paper is the joint transmission scenario, where an ideal backhaul network allows a set of randomly located base stations, possibly belonging to different network tiers, to jointly transmit data, to mitigate intercell interference and hence improve coverage and spectral efficiency. Using tools from stochastic geometry, an integral expression for the network coverage probability is derived in the scenario where the typical user located at an arbitrary location, i.e., the general user, receives data from a pool of base stations that are selected based on their average received power levels. An expression for the coverage probability is also derived for the typical user located at the point equidistant from three base stations, which we refer to as the worst case user. In the special case where cooperation is limited to two base stations, numerical evaluations illustrate absolute gains in coverage probability of up to 17 for the general user and 24 for the worst case user compared with the noncooperative case. It is also shown that no diversity gain is achieved using noncoherent joint transmission, whereas full diversity gain can be achieved at the receiver if the transmitting base stations have channel state information. In this paper, we develop an energy-efficient base station deployment framework for heterogeneous networks. Micro base station deployment increases the total capacity of the network. However, increasing the number of micro base stations excessively may reduce the energy efficiency of the network. Therefore, in this paper, we examine the energy efficiency aspect of the micro base station deployment problem. This problem can be divided into two subproblems: choosing feasible candidate micro base station locations and selecting the optimum set of micro base stations among the candidate locations. The proposed algorithm first chooses the subset of the feasible locations as candidate locations, and then selects the micro base stations which maximize the energy efficiency of the network iteratively. It is shown that the proposed algorithm is a constant-factor approximation of the optimal solution. Our simulations demonstrate that proposed algorithm improves the energy efficiency of the network up to 12 for low-loaded scenarios and 98 for the high-loaded scenarios.