Channel spatial reuse enables transmission links to share wireless radio channels and thus improves the spectrum utilization. However, to determine the feasibility of channel spatial reuse is very challenging since aggregated co-channel interference would be harmful to link quality. In this paper we investigate the feasibility problem of channel spatial reuse in power-controlled wireless communication networks, and we propose an effective and efficient method to solve it. The feasibility problem of channel spatial reuse is to determine whether a strictly feasible power allocation solution for all transmission links exists such that all link quality requirements are met. The feasibility problem is transformed into a new optimization problem. We prove that the feasibility of channel spatial reuse could be determined by the solution of this new optimization problem. This new optimization problem is found to be a linear programming problem which could be solved by the interior point methods in polynomial time.

Received signal strength (RSS) in Wi-Fi networks is commonly employed in indoor positioning systems; however, device diversity is a fundamental problem in such systems. This problem becomes more important in recent years due to the tremendous growth of new Wi-Fi devices, which perform differently in respect to the RSS values and degrade localization performance significantly. Several studies have proposed methods to improve the robustness of positioning systems against device diversity. This paper is primarily concerned with the performance of calibration-free approaches, including signal strength difference (SSD), hyperbolic location fingerprinting (HLF), and DIFF. The performance comparison is based on two Wi-Fi positioning systems in a 3-D indoor building, including a zero-configuration and a fingerprinting-based system. The results show that these calibration-free techniques perform much better than the original RSS with heterogeneous devices. However, the improvement in robustness is gained at the expense of losing some discriminative information. When the testing and training data are both measured from the same device, the performance of HLF and SSD is clearly below that of RSS in both systems. Although DIFF performs the best, it has to suffer from dealing with a space of large dimensions.

Wireless spectrum is a limited and valuable resource for communications. However, wireless spectrum is known to be underutilized in spacial, temporal, and spectral domains. The dynamic spectrum access (DSA) of cognitive radio networks provides the capability to improve the spectrum efficiency by allowing secondary users to access the spectrum opportunistically without interfering primary users. The dynamic spectrum access is a joint channel allocation and power control problem with the objective to maximize the aggregated throughput of all secondary users. This problem is especially difficult in multi-channel multi-user cognitive radio networks. In the literature it is often formulated as a mixed integer nonlinear programming (MINLP) problem which is NP-hard. Therefore, some approximation methods are proposed to solve this problem, which lead to suboptimal solutions. In this paper we carefully reexamine the DSA problem, and prove that the original MINLP problem formulation is over-parameterized. We show that the DSA problem could be formulated as a nonlinear programming (NLP) problem without losing globally optimal objective function values. Moreover, the optimal solution to this NLP problem could be obtained by an interior point DSA optimization algorithm in polynomial time. Simulation results show that the proposed method performs better than other approximation methods do.

Designing efficient search algorithms is a key challenge in unstructured peer-to-peer networks. Flooding and random walk (RW) are two typical search algorithms. Flooding searches aggressively and covers the most nodes. However, it generates a large amount of query messages and, thus, does not scale. On the contrary, RW searches conservatively. It only generates a fixed amount of query messages at each hop but would take longer search time. We propose the dynamic search (DS) algorithm, which is a generalization of flooding and RW. DS takes advantage of various contexts under which each previous search algorithm performs well. It resembles flooding for short-term search and RW for long-term search. Moreover, DS could be further combined with knowledge-based search mechanisms to improve the search performance. We analyze the performance of DS based on some performance metrics including the success rate, search time, query hits, query messages, query efficiency, and search efficiency. Numerical results show that DS provides a good tradeoff between search performance and cost. On average, DS performs about 25 times better than flooding and 58 times better than RW in power-law graphs, and about 186 times better than flooding and 120 times better than RW in bimodal topologies.

We present a novel approach to the problem of the indoor localization in wireless environments. The main contribution of this paper is fourfold: 1) we show that by projecting the measured signal into a decorrelated signal space, the positioning accuracy is improved, since the cross correlation between each AP is reduced, 2) we demonstrate that this novel approach achieves a more efficient information compaction and provides a better scheme to reduce online computation (the drawback of AP selection techniques is overcome, since we reduce the dimensionality by combing features, and each component in the decorrelated space is the linear combination of all APs; therefore, a more efficient mechanism is provided to utilize information of all APs while reducing the computational complexity), 3) experimental results show that the size of training samples can be greatly reduced in the decorrelated space; that is, fewer human efforts are required for developing the system, and 4) we carry out comparisons between RSS and three classical decorrelated spaces, including Discrete Cosine Transform (DCT), Principal Component Analysis (PCA), and Independent Component Analysis (ICA) in this paper. Two AP selection criteria proposed in the literature, MaxMean and InfoGain are also compared. Testing on a realistic WLAN environment, we find that PCA achieves the best performance on the location fingerprinting task.

The access of multimedia computing in wireless networks is concerned with the performance of handoff because of the irretrievable property of real-time data delivery. To lessen throughput degradation incurred by unnecessary handoffs or handoff latencies leading to media disruption perceived by users, this paper presents a link quality based handoff algorithm. Neural networks are used to learn the cross-layer correlation between the link quality estimator such as packet success rate and the corresponding context metric indictors, for example, the transmitting packet length, received signal strength, and signal to noise ratio. Based on a pre-processed learning of link quality profile, neural networks make essential handoff decisions efficiently with the evaluations of link quality instead of the comparisons between relative signal strength. The experiment and simulation results show that the proposed algorithm improves the user perceived qualities in a transmission scenario of VoIP applications by minimizing both the number of lost packets and unnecessary handoffs.

In this paper we propose a cross-layer handoff ordering scheme. The frame success rate (FSR) is adopted as the basis of prioritization. Different quality of service (QoS) requirements of various applications would result in different FSR requirements. In order to indicate how critical a handoff request is, both the FSR requirement from the application layer and the FSR measurement from the medium access control layer are taken into consideration in the proposed scheme. The prioritization of handoff requests follows the most-critical-first policy. Performance analysis shows that the proposed scheme effectively reduces the forced termination probabilities. Under the same forced termination probability requirements, it could provide 1.95% to 11.13% more arrival calls compared to previous works.

Flooding and random walk (RW) are the two typical search algorithms in unstructured peer-to-peer networks. The flooding algorithm searches the network aggressively. It covers the most nodes but generates a large number of query messages. Hence it is considered to be not scalable. This cost issue is especially serious when the queried resource locates far from the query source. On the contrary, RW searches the network conservatively. It only generates a fixed amount of query messages at each hop, but it may take particularly longer search time to find the queries resource. We propose the dynamic search algorithm (DS) which is a generalization of flooding, modified breadth first search (MBFS), and RW. This search algorithm takes advantage of different contexts under which each previous search algorithm performs well. The operation of DS resembles flooding or MBFS for the short-term search, and RW for the long-term search. We analyze the performance of DS based on the power-law random graph model and adopt some performance metrics including the guaranteed search time, query hits, query messages, success rate, and a unified metric, search efficiency. The main objective is to obtain the effects of the parameters of DS. Numerical results show that proper setting of the parameters of DS can obtain the short guaranteed search time and provide a good tradeoff between the search performance and the cost.

Packet size is one of the most important factors that would affect the user-perceived multimedia QoS in the wireless LAN environments. The time-varying channel characteristics make it difficult to find the exact relationship between the packet size and the throughput and decide an optimal packet size in advance. Furthermore, every node would suffer different channel conditions. In this paper, we tackle this problem by an optimization approach. A context-aware framework is designed to optimize the packet size adaptively in order to maximize the throughput. In this approach each node abstracts its specific context via the throughput from the time-varying wireless environments. The obtained throughput information is the instantaneous integrated effect of all contexts in wireless LAN environments. This approach adopts neural networks to learn the complex nonlinear function between the packet size and the throughput and adaptively adjusts the packet size. Simulation results show that out method can cope with the time-varying wireless channel conditions and improve the perceived QoS of wireless multimedia services

In this letter, we propose a cross-layer adaptation scheme which improves IEEE 802.11e quality of service (QoS) by online adapting multidimensional medium access control (MAC)-layer parameters depending on the application-layer QoS requirements and physical layer (PHY) channel conditions. Our solution is based on an optimization approach which utilizes neural networks (NNs) to learn the cross-layer function. Simulations results demonstrate the effectiveness of our adaptation scheme.

Traditional handoff ordering methods adopt the received signal strength (RSS) as the basis of prioritization. However, the RSS is not the only one metric to represent the user perceived quality of service, since many other factors, like the packet length, interference, and the modulation/codec schemes, would also affect it. In this paper, we propose a handoff ordering method based on packet success rate (PSR) for multimedia communications in wireless networks. The priority of a handoff request is based on its current PSR, the PSR degradation rate, and the minimum PSR requirement of its service class. The major contribution of our method is that we improve the user perceived QoS during the handoff process. Simulation results indicate that our method can effectively improve the handoff call dropping probability with little or no increase of the new call blocking probability.

The access of multimedia computing in wireless networks is concerned with the efficiency of handoff because of the irretrievable property of real-time data delivery. To lessen throughput degradation leading to media computing disruption perceived by users, this paper presents a link quality based handoff algorithm. Neural networks are used to learn the correlation between link quality estimator and the corresponding context metric indictors. Based on a pre-processed learning of link quality profile, neural networks make efficient handoff decisions with an evaluation of link quality instead of a comparison between relative signal strength. The experimental and simulation results show that the number of lost packets is minimized using the proposed algorithm without incurring unnecessary handoffs.