In this paper, we propose a novel multichannel network with infrastructure support, which is called an MC-IS network, that has not been studied in the literature. To the best of our knowledge, we are the first to study such an MC-IS network. Our proposed MC-IS network has a number of advantages over three existing conventional networks: a single-channel wireless ad hoc network (called an SC-AH network), a multichannel wireless ad hoc network (called an MC-AH network), and a single-channel network with infrastructure support (called an SC-IS network). In particular, the network capacity of our proposed MC-IS network is @math times higher than that of an SC-AH network and an MC-AH network and the same as that of an SC-IS network, where @math is the number of nodes in the network. The average delay of our MC-IS network is @math times lower than that of an SC-AH network and an MC-AH network and @math times lower than the average delay of an SC-IS network, where @math and @math denote the number of channels dedicated for infrastructure communications and the number of interfaces mounted at each infrastructure node, respectively. Our analysis on an MC-IS network equipped with omnidirectional antennas has been extended to an MC-IS network equipped with directional antennas only, which are named as an MC-IS-DA network. We show that an MC-IS-DA network has an even lower delay of @math compared with an SC-IS network and our MC-IS network. For example, when @math and @math , an MC-IS-DA can further reduce the delay by 24 times lower than that of an MC-IS network and by 288 times lower than that of an SC-IS network.
The second network related to our network is an network, in which multiple channels instead of a single channel are used. Besides, each node in such a network is equipped with multiple network interfaces instead of single network interface. This network has a higher throughput than an network because each node can support multiple concurrent transmissions over multiple channels. However, this network suffers from the high delay and the increased deployment complexity. The average delay of an network is the same as that of an network, which increases significantly with the number of nodes. The deployment complexity is mainly due to the condition @cite_17 that each channel (up to @math channels) must be utilized by a dedicated interface at a node so that all the channels are fully utilized simultaneously and thus the network capacity can be maximized. When the condition is not fulfilled, the capacity degrades significantly.
@cite_17
2163418239 1976570693 1608479615 1970554468
This paper studies how the capacity of a static multi-channel network scales as the number of nodes, n, increases. Gupta and Kumar have determined the capacity of single-channel networks, and those bounds are applicable to multi-channel networks as well, provided each node in the network has a dedicated interface per channel.In this work, we establish the capacity of general multi-channel networks wherein the number of interfaces, m, may be smaller than the number of channels, c. We show that the capacity of multi-channel networks exhibits different bounds that are dependent on the ratio between c and m. When the number of interfaces per node is smaller than the number of channels, there is a degradation in the network capacity in many scenarios. However, one important exception is a random network with up to O(log n) channels, wherein the network capacity remains at the Gupta and Kumar bound of Θ(W√noverlog n) bits sec, independent of the number of interfaces available at each node. Since in many practical networks, number of channels available is small (e.g., IEEE 802.11 networks), this bound is of practical interest. This implies that it may be possible to build capacity-optimal multi-channel networks with as few as one interface per node. We also extend our model to consider the impact of interface switching delay, and show that in a random network with up to O(log n) channels, switching delay may not affect capacity if multiple interfaces are used. In this correspondence, we consider a cellular network consisting of a base station and N receivers. The channel states of the receivers are assumed to be identical and independent of each other. The goal is to compare the throughput of two different scheduling policies (a queue-length-based (QLB) policy and a greedy policy) given an upper bound on the queue overflow probability or the delay violation probability. We consider a multistate channel model, where each channel is assumed to be in one of L states. Given an upper bound on the queue overflow probability or an upper bound on the delay violation probability, we show that the total network throughput of the (QLB) policy is no less than the throughput of the greedy policy for all N. We also obtain a lower bound on the throughput of the (QLB) policy. For sufficiently large N, the lower bound is shown to be tight, strictly increasing with N, and strictly larger than the throughput of the greedy policy. Further, for a simple multistate channel model-ON-OFF channel, we prove that the lower bound is tight for all N In a cognitive radio scenario, we consider a single secondary user (SU) accessing a multichannel system. The SU senses the channels sequentially to detect if a primary user (PU) is occupying the channels and stops its search to access a channel if it offers a significantly high throughput. The optimal stopping rule and power control problem is considered. The problem is formulated as an SU's throughput-maximization problem under power, interference, and packet delay constraints. We first show the effect of the optimal stopping rule on packet delay and then solve this optimization problem for both the overlay system, where the SU transmits only at the spectrum holes, and the underlay system, where tolerable interference (or tolerable collision probability) is allowed. We provide closed-form expressions for the optimal stopping rule and show that the optimal power control strategy for this multichannel problem is a modified waterfilling approach. We extend the work to a multi-SU scenario and show that when the number of SUs is large, the complexity of the solution becomes smaller than that of the single-SU case. We discuss the application of this problem in typical networks where packets simultaneously arrive and have the same departure deadline. We further propose an online adaptation policy to the optimal stopping rule that meets the packets' hard-deadline constraint and, at the same time, gives higher throughput than the offline policy. The throughput of wireless networks can be significantly improved by multi-channel communications compared with single-channel communications since the use of multiple channels can reduce interference influence. In this paper, we study interference-aware topology control and QoS routing in IEEE 802.11-based multi-channel wireless mesh networks with dynamic traffic. Channel assignment and routing are two basic issues in such networks. Different channel assignments can lead to different network topologies. We present a novel definition of co-channel interference. Based on this concept, we formally define and present an effective heuristic for the minimum INterference Survivable Topology Control (INSTC) problem which seeks a channel assignment for the given network such that the induced network topology is interference-minimum among all K-connected topologies. We then formulate the Bandwidth-Aware Routing (BAR) problem for a given network topology, which seeks routes for QoS connection requests with bandwidth requirements. We present a polynomial time optimal algorithm to solve the BAR problem under the assumption that traffic demands are splittable. For the non-splittable case, we present a maximum bottleneck capacity path routing heuristic. Simulation results show that compared with the simple common channel assignment and shortest path routing approach, our scheme improves the system performance by 57 on average in terms of connection blocking ratio.