Growth in the applications of wireless devices and the need for seamless solutions to location-based services has motivated extensive research efforts to address wireless indoor localization networks. Existing works provide range-based localization using ultra-wideband technology, focusing on reducing the inaccuracy in range estimation due to clock offsets between different devices. This is generally achieved via signal message exchange between devices, which can lead to network congestion when the number of users is large. To address the problem of range estimation with limited signal messages, this paper proposes multiple simultaneous ranging methods based on a property of time difference of reception of two packets transmitted from different sources in impulse-radio ultra-wideband (IR-UWB) networks. The proposed method maintains similar robustness to the clock offsets while significantly reducing the air time occupancy when compared with the best existing ranging methods. Experimental evaluation of ranging in a line-of-sight environment shows that the proposed method enables accurate ranging with minimal air time occupancy.

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The ubiquitous coverage/connectivity requirement of wireless cellular networks has shifted mobile network operators’ (MNOs) interest toward dense deployment of small cells with coverage areas that are much smaller as compared to macrocell base stations (MBSs). Multi-operator small cells could provide virtualization of network resources (infrastructure and spectrum) and enable its efficient utilization, i.e., uninterrupted coverage and connectivity to subscribers, and an opportunity to avoid under-utilization of the network resources. However, a MNO with exclusive ownership to network resources would have little incentive to utilize its precious resources to serve users of other MNOs, since MNOs differentiate among others based on their ownership of the licensed spectrum. Thus, considering network resources scarcity and under-utilization, this paper proposes a mechanism for multi-operator small cells collaboration through negotiation that establishes a mutual agreement acceptable to all involved parties, i.e., a win–win situation for the collaborating MNOs. It enables subscribers of a MNO to utilize other MNOs’ network resources, and allows MNOs to offer small cells “as a service” to users with ubiquitous access to wireless coverage/connectivity, maximize the use of an existing network resources by serving additional users from a market share, and enhance per-user data rate. We validated and evaluated the proposed mechanism through simulations considering various performance metrics.

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The paper presents a game theoretic solution for distributed subchannel allocation problem in small cell networks (SCNs) analyzed under the physical interference model. The objective is to find a distributed solution that maximizes the welfare of the SCNs, defined as the total system capacity. Although the problem can be addressed through best-response (BR) dynamics, the existence of a steady-state solution, i.e., a pure strategy Nash equilibrium (NE), cannot be guaranteed. Potential games (PGs) ensure convergence to a pure strategy NE when players rationally play according to some specified learning rules. However, such a performance guarantee comes at the expense of complete knowledge of the SCNs. To overcome such requirements, properties of PGs are exploited for scalable implementations, where we utilize the concept of marginal contribution (MC) as a tool to design learning rules of players’ utility and propose the marginal contribution-based best-response (MCBR) algorithm of low computational complexity for the distributed subchannel allocation problem. Finally, we validate and evaluate the proposed scheme through simulations for various performance metrics.

Because of their simple design structure, end-to-end deep learning (E2E-DL) models have gained a lot of attention for speech enhancement. A number of DL models have achieved excellent results in eliminating the background noise and enhancing the quality as well as the intelligibility of noisy speech. Designing resource-efficient and compact models during real-time processing is still a key challenge. In order to enhance the accomplishment of E2E models, the sequential and local characteristics of speech signal should be efficiently taken into consideration while modeling. In this paper, we present resource-efficient and compact neural models for end-to-end noise-robust waveform-based speech enhancement. Combining the Convolutional Encode-Decoder (CED) and Recurrent Neural Networks (RNNs) in the Convolutional Recurrent Network (CRN) framework, we have aimed at different speech enhancement systems. Different noise types and speakers are used to train and test the proposed models. With LibriSpeech and the DEMAND dataset, the experiments show that the proposed models lead to improved quality and intelligibility with fewer trainable parameters, notably reduced model complexity, and inference time than existing recurrent and convolutional models. The quality and intelligibility are improved by 31.61% and 17.18% over the noisy speech. We further performed cross corpus analysis to demonstrate the generalization of the proposed E2E SE models across different speech datasets.

Abstract-We study the problem of channel selection of noncooperative OFDMA femtocells in two-tiered macro-femto networks. Using a physical channel model, we consider a distributed channel allocation of femtocells and a total capacity objective, which is the total capacity of uplink macro users and femto users. We assume a time-slotted system, a time-invariant channel model (no fading), each user knows the signal-to-interferenceplus-noise ratio (SINR) of all channels, and the channel selection happens only at the beginning of each time-slot. We study the performance of a best-response strategy where each user chooses to transmit in the highest-SINR channel. For simplicity, we focus on the homogeneous 3-link, 2-channel case and show that if all users update their actions every time-slot (i.e., all users make simultaneous moves), an oscillation can occur and result in the worst performance. To avoid the oscillation and achieve the highest total capacity, while still assuming no coordination among the users, we propose a stochastic best-response algorithm, where each user updates its channel selection with a selection probability p. We use a Markov chain to analyze the average capacity performance and use simulation results to confirm our analysis and also provide performance of other homogeneous cases with more number of homogeneous links and channels. It is shown that the highest total capacity can be achieved when the selection probability p is very small. This stochastic best response with small p in effect provides a sequential move mechanism which requires no coordination among the users and hence avoids the oscillation problem from the simultaneous moves.