Recent trends suggest that cognitive radio based wireless networks will be frequency agile and the nodes will be equipped with multiple radios capable of tuning across large swaths of spectrum. The MAC scheduling problem in such networks refers to making intelligent decisions on which communication links to activate at which time instant and over which frequency band. The challenge in designing a low-complexity distributed MAC, that achieves low delay, is posed by two additional dimensions of cognitive radio networks: interference graphs and data rates that are frequency-band dependent, and explosion in number of feasible schedules due to large number of available frequency-bands. In this paper, we propose MAXIMAL-GAIN MAC, a distributed MAC scheduler for frequency agile multi-band networks that simultaneously achieves the following: (i) optimal network-delay scaling with respect to the number of communicating pairs, (ii) low computational complexity of O(log2(maximum degree of the interference graphs)) which is independent of the number of frequency bands, number of radios per node, and overall size of the network, and (iii) robustness, i.e., it can be adapted to a scenario where nodes are not synchronized and control packets could be lost. Our proposed MAC also achieves a throughput provably within a constant fraction (under isotropic propagation) of the maximum throughput. Due to a recent impossibility result, optimal delay-scaling could only be achieved with some amount of throughput loss . Extensive simulations using OMNeT++ network simulator shows that, compared to a multi-band extension of a state-of-art CSMA algorithm (namely, Q-CSMA), our asynchronous algorithm achieves a 2.5x reduction in delay while achieving at least 85 of the maximum achievable throughput. Our MAC algorithms are derived from a novel local search based technique.
The extensive research on MAC scheduling in single band networks (see @cite_29 for an excellent survey) can be broadly divided into two classes: max-weight computation based and Glauber dynamics based. The first class of approach is inspired by the seminal work @cite_21 which proves that maximum-weight (MW) scheduling achieves 100 at every instant a non-interfering set of links such that the total of weighted data-rates of the activated links is maximized, where, weight of a link is roughly defined by the number of backlogged packets. Furthermore, a recent work @cite_22 (also see @cite_2 @cite_8 ) has shown that an MW schedule achieves order optimal delay scaling with the number of links. However, computing MW schedule is NP-hard, leading to considerable research on approximate MW schedules @cite_16 @cite_30 @cite_31 @cite_7 . However, extending these works to multi-band wireless networks, with distributed implementation and low complexity, is not easy. As discussed in , the second class of approach @cite_14 @cite_12 @cite_17 is motivated by so called Glauber dynamics based local search. These scheme achieve 100 guarantees in general networks.
@cite_30 @cite_14 @cite_22 @cite_7 @cite_8 @cite_29 @cite_21 @cite_2 @cite_31 @cite_16 @cite_12 @cite_17
2154125468 2038187727 2139644003 2097058227
In the interference scheduling problem, one is given a set of n communication requests described by source-destination pairs of nodes from a metric space. The nodes correspond to devices in a wireless network. Each pair must be assigned a power level and a color such that the pairs in each color class can communicate simultaneously at the specified power levels. The feasibility of simultaneous communication within a color class is defined in terms of the Signal to Interference plus Noise Ratio (SINR) that compares the strength of a signal at a receiver to the sum of the strengths of other signals. The objective is to minimize the number of colors as this corresponds to the time needed to schedule all requests. We introduce an instance-based measure of interference, denoted by I, that enables us to improve on previous results for the interference scheduling problem. We prove the upper and lower bounds in terms of I on the number of steps needed for scheduling a set of requests. For general power assignments, we prove a lower bound of @W(I ([email protected])) steps, where @D denotes the aspect ratio of the metric. When restricting to the two-dimensional Euclidean space (as in the previous work) the bound improves to @W(I [email protected]). Alternatively, when restricting to linear power assignments, the lower bound improves even to @W(I). The lower bounds are complemented by an efficient algorithm computing a schedule for linear power assignments using only O(Ilogn) steps. A more sophisticated algorithm computes a schedule using even only O(I+log^2n) steps. For dense instances in the two-dimensional Euclidean space, this gives a constant factor approximation for scheduling under linear power assignments, which shows that the price for using linear (and, hence, energy-efficient) power assignments is bounded by a factor of O([email protected]). In addition, we extend these results for single-hop scheduling to multi-hop scheduling and combined scheduling and routing problems, where our analysis generalizes the previous results towards general metrics and improves on the previous approximation factors. Developing scheduling mechanisms that can simultaneously achieve throughput optimality and good delay performance often require solving the Maximum Independent Weighted Set (MWIS) problem. However, under most realistic network settings, the MWIS problem can be shown to be NP-hard. In non-fading environments, low-complexity scheduling algorithms have been provided that converge either to the MWIS solution in time or to a solution that achieves at least a provable fraction of the achievable throughput. However, in more practical systems the channel conditions can vary at faster time-scales than convergence occurs in these lower-complexity algorithms. Hence, these algorithms cannot take advantage of the opportunistic gain, and may no longer guarantee good performance. In this paper, we propose a low-complexity scheduling scheme that performs provably well under fading channels and is amenable to implement in a distributed manner. To the best of our knowledge, this is the first scheduling scheme under fading environments that requires only local information, has a low complexity that grows logarithmically with the network size, and achieves provable performance guarantees (which is arbitrarily close to that of the well-known centralized Greedy Maximal Scheduler). Through simulations we verify that both the throughput and the delay under our proposed distributed scheduling scheme are close to that of the optimal solution to MWIS. Further, we implement a preliminary version of our algorithm in a testbed by modifying the existing IEEE 802.11 DCF. The preliminary experiment results show that our implementation successfully accounts for wireless fading, and attains the opportunistic gains in practice, and hence substantially outperforms IEEE 802.11 DCF. Accurate analytical expressions of delay and packet reception probabilities, and energy consumption of duty-cycled wireless sensor networks with random medium access control (MAC) are instrumental for the efficient design and optimization of these resource-constrained networks. Given a clustered network topology with unslotted IEEE 802.15.4 and preamble sampling MAC, a novel approach to the modeling of the delay, reliability, and energy consumption is proposed. The challenging part in such a modeling is the random MAC and sleep policy of the receivers, which prevents to establish the exact time of data packet transmission. The analysis gives expressions as function of sleep time, listening time, traffic rate and MAC parameters. The analytical results are then used to optimize the duty cycle of the nodes and MAC protocol parameters. The approach provides a significant reduction of the energy consumption compared to existing solutions in the literature. Monte Carlo simulations by ns2 assess the validity of the analysis. In multihop wireless networks, designing distributed scheduling algorithms to achieve the maximal throughput is a challenging problem because of the complex interference constraints among different links. Traditional maximal-weight scheduling (MWS), although throughput-optimal, is difficult to implement in distributed networks. On the other hand, a distributed greedy protocol similar to IEEE 802.11 does not guarantee the maximal throughput. In this paper, we introduce an adaptive carrier sense multiple access (CSMA) scheduling algorithm that can achieve the maximal throughput distributively. Some of the major advantages of the algorithm are that it applies to a very general interference model and that it is simple, distributed, and asynchronous. Furthermore, the algorithm is combined with congestion control to achieve the optimal utility and fairness of competing flows. Simulations verify the effectiveness of the algorithm. Also, the adaptive CSMA scheduling is a modular MAC-layer algorithm that can be combined with various protocols in the transport layer and network layer. Finally, the paper explores some implementation issues in the setting of 802.11 networks.