This paper proposes a novel algorithm, named a quasi-neural network-based decoder (QNN-decoder), for a single-carrier communication system. The algorithm is designed for an inter-symbol-interference (ISI) channel that a trellis diagram can model. According to the trellis diagram, a quasi-neural network (QNN) is built to acquire the likelihoods of the received samples enabling the subsequent decoding. The QNN-decoder differs from artificial neural network (ANN) based algorithms, such as the online learning trellis diagram (OLTD), as it doesn’t rely on data but instead utilizes the physical system model. This means the QNN-decoder can use a much shorter pilot to train its network via backpropagation than OLTD. Meanwhile, the QNN-decoder doesn’t require explicit channel state information (CSI) or statistics of interference and noise. Instead, it can efficiently suppress non-Gaussian interference by learning the CSI and interference and noise statistics. Simulation results verify the QNN-decoder outperforms the state-of-the-art methods and approaches the performance limits provided by the conventional Viterbi with perfect CSI in Gaussian noise only. The QNN-detector outperforms the conventional Viterbi and OLTD with non-Gaussian interference. A few redundant nodes make the QNN-decoder robust to channel length uncertainty, and it may be easily extended to a multi-antenna system for even greater interference suppression.

This paper presents a quasi-neural network (Quasi-NN) approach for parametric channel estimation in multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. A unified Quasi-NN framework is proposed for both the single-user (SU) and the multi-user (MU) scenarios, enabling the joint estimation of the direction of arrival, direction of departure, time delay, complex gain, and the number of multipaths. The artificial neural network-like structure of the Quasi-NN allows the application of the backpropagation algorithm while requiring only real-time pilot signals for online network training. Furthermore, considering the issue of high pilot overhead in MU MIMO-OFDM systems, we develop a location assisted Quasi-NN (LA-QNN) that utilizes a location-parameter database to reduce the pilot overhead while maintaining accurate channel estimation. Simulation results show that the proposed Quasi-NN approaches the Cramér Rao bound, and the proposed LA-QNN scheme provides a better balance between channel estimation performance and pilot overhead in the MU scenario.

This paper proposes a Quasi-neural network-based detection algorithm, namely QNN-detector, for a single-carrier communication system in an inter-symbol interference (ISI) channel, which can be modeled by a trellis diagram. According to the trellis diagram, a quasi-neural network (QNN) is built to acquire the normalized likelihoods to enable the subsequent detection or decoding. The QNN can accommodate non-Gaussian interferences through ingeniously designing its hidden layers. Unlike the artificial neural network (ANN) based algorithms, which are data-driven, the QNN-detector relies on the physical system model and only needs a short pilot sequence for training. Moreover, it requires neither explicit channel state information (CSI) nor statistics of interference and noise. Simulation results illustrate that in a channel under white Gaussian noise, the QNN-detector significantly outperforms the ANN-based algorithms in that its network training requires a far shorter pilot sequence, and can approach the performance limits provided by the Viterbi detector with perfect CSI. The simulations also show that in the presence of non-Gaussian interferences, the QNN-detector can learn the distribution of the interferences and therefore suppress them effectively, while the conventional Viterbi detector fails to.

With the development of multi-access edge computing (MEC), the cloudlet at the edge of the network can provide nearby high-performance computing services, thus reducing the computational consumption of user equipments (UEs). To provide more real-time computing services to UEs, service providers face the challenge of optimizing the assignment of requests and the allocation of cloudlets’ computing resources to achieve low latency while dealing with the large number of offloaded requests from UEs. Therefore, in this paper, we study the problem of minimizing the total latency to complete the requests in the MEC network by jointly optimizing request assignment and computing resource allocation. The problem is formulated as a mixed integer nonlinear programming (MINLP) problem which is NP-hard. To solve the problem, we decompose the problem into two subproblems which respectively optimize the request assignment and the computing resource allocation. We first deal with the computing resource allocation problem by utilizing the Lagrangian multiplier method, and the resulting solution is applied for the request assignment problem. Then a novel primal-dual based approximation algorithm is devised to address the request assignment problem. Finally, to verify the efficiency of the proposed algorithm, we provide an upper bound on the approximation ratio. The experiment results show that the proposed algorithm outperforms baseline algorithms in terms of total latency, loading balancing, and computational speed.

This paper presents a novel bluetooth low energy (BLE) protocol enabling a BLE node to perform RF fingerprint acquisition by measuring the received signal strength (RSS) from its neighboring nodes and simultaneously broadcast the acquired fingerprint via its advertising packet. However, the fingerprint acquisition and broadcast process in a dense BLE network is very challenging owing to: 1) the likelihood of packet collision; and 2) the length-constrained packet. To this end, we exploit a compressive sensing (CS) framework allowing each node to acquire no more than$M$measurements from a very dense network, in which the number of nodes$N$is far greater than$M$. By aggregating the$M$-dimensional compressed fingerprint vector from$s

While many technologies (e.g., RFID, QR code, etc.) have been developed for items identification, they fail to provide continuous monitoring for items in transit. This paper introduces a Bluetooth Low Energy (BLE) beacon-based system, which can be deployed easily with any off-the-shelf smartphone without modification on the existing infrastructures. However, it is an elusive challenge to achieve a fast item identification and counting involving massive items stacked up inside a confined space (e.g., a container), resulting in an ultra-dense beacon network (UDBN). To this end, we propose a novel beaconing solution capable of informing the receiver about their own presence as well as the presence of their neighboring beacons for identification purpose. Specifically, our proposed solution provides a well-designed yet innovative protocol data unit (PDU) which allows the beacon to encapsulate its neighboring information into its own advertising packet. A prototype consisting of 300 beacons is implemented to demonstrate the feasibility of our proposed solution for real-world applications. The extensive experiment confirm the superiority of our proposed solution in delivering a fast item identification and counting in UDBN.

Bluetooth low-energy (BLE) beacon is a key enabler for smart interaction between the user device and the physical thing, in which the physical thing can actively engage users for interaction via its advertising packet. However, reliability is always an issue for the beacon-based interaction since the beacon employs an unreliable broadcasting approach which provides no way to check if the user device has received the correct packet. We define the sparse observation to describe the phenomenon where the number of packets received by the user device within an arbitrarily small time duration is less than the number of deployed beacons. This article studies the sparse observation causing by the following two factors: 1) the unpredictable environmental variations and 2) the uncontrollable operating conditions of a beacon. An analysis is provided to investigate the interaction reliability in connection with the above two factors. Motivated by the above challenges, a novel solution, which exploits the ambient RF fingerprinting to address the sparse-observation issues, is proposed to enhance the interaction reliability. Our proposed solution is validated with extensive experiments consisting of real data collected from both indoor and outdoor environments. Finally, the feasibility of our proposed solution is demonstrated with a proof-of-concept prototype implemented over multiple physical things.

Lately, there are many works exploited the radio frequency (RF) fingerprint for occupancy detection. However, most works suffer severe performance variations owing to the unreliable received signal strength (RSS). In this paper, we propose a deep learning approach to occupancy detection: 1) an unsupervised denoising-contractive autoencoder (DCAE) is built to learn a robust fingerprint representation from the raw RSS measurements, and 2) a supervised softmax function is added at the last layer for classification. A real testbed with Bluetooth Low Energy (BLE) beacons was built such that we can collect real-world RSS data for experiments. The data were collected via different devices at different times to better reflect environmental variations. The experimental results show that our proposed approach achieves a substantial performance gain in comparison to the conventional machine learning approaches. Specifically, our proposed DCAE is able to reconstruct the noisy and always changing data with less than 0.047 mean square error. Overall, our occupancy detection combining DCAE and softmax classifier achieves sub-room level accuracy for at least 99.3% of the time.

Considering the limitations on communication capability in the big data era, measurement selection plays an important role in obtaining the desired information by collecting only a part of data from the sensors. In this paper, we study the large-scale measurement selection problem, and propose a distributed algorithm exploiting the sparsity property extracted from the on-line data processing. Different to the existing works, we propose a mission-oriented framework to analyze the performance improvements for the specific mission of collecting new data. Specifically, a Bayesian hierarchical prior is adopted in order to quantify the importance of uncollected data by the on-line inference from the collected data. Based on the sparsity property obtained by on-line data processing, the sensors with important uncollected data will have high priority to access. Due to the massive number of sensors, the measurement selection algorithm is executed distributively at each device according to the common information broadcast by the fusion center. Simulation results demonstrate the performance gain of our proposed measurement selection method compared to the conventional schemes.

Bluetooth low energy (BLE) beacons have been widely deployed to deliver proximity-based services (PBSs) to user's smartphones when users are in the proximity of a beacon. Conventional proximity detection simply uses the received signal strength (RSS) to infer the proximity, and then retrieves the PBS by mapping the beacon ID with the corresponding service in the cloud database. Such an approach suffers two major issues: 1) the severe RSS fluctuation might confuse the smartphone during the detection and 2) a malicious PBS can be delivered by manipulating the same beacon ID. This paper proposes RF fingerprinting to label a beacon with an N-dimensional fingerprint vector, which consists of N RSS values from N deployed beacons. The contribution of our proposed method is twofold: 1) we infer the proximity based on the fingerprint vector instead of relying solely on the single RSS value and 2) we retrieve the PBS by mapping the fingerprint vector instead of the hard-coded beacon ID. The challenge with our proposed approach is the incomplete fingerprint observation during real-time detection, resulting in an underdetermined proximity detection problem. To this end, we exploit the compressive sensing (CS) approach based on the differential evolutional algorithm to address such an underdetermined problem. Extensive simulations with realworld datasets show that our proposed approach outperforms the legacy machine learning techniques with substantial performance gains.

This paper considers an uplink multiuser multiple-input-multiple-output (MU-MIMO) system. In this system, we have presented a sparse-aware minimum-mean-square-error (SA-MMSE) detector which improves an underlying linear detector using the sparsity of a residual error vector (difference from the transmit vector and the detected one by the linear detector). Despite its attractive performance, the conventional SA-MMSE detector is only available for 4-QAM systems. In this paper, we generalize the SA-MMSE detector for a higher-order modulation system in a non-trivial method. This is referred to as generalized SA-MMSE (GSA-MMSE) detector. The key idea of the proposed detector is to exploit the hierarchical structure of a residual error vector. To be specific, the residual error vector can be decomposed into orthogonal sub-error vectors and, leveraging the orthogonality, the sub-error vectors can be decoded using the corresponding SA-MMSE detector in a successive fashion. Via simulation results, we demonstrate that the GSA-MMSE detector significantly outperforms the conventional linear detectors with a comparable complexity.

An algorithm of power control in two-tier heterogeneous networks is proposed in this paper. We consider femtocell base stations (FBSs) dense deployment in the macrocell base station (MBS) coverage, the MBS dynamically estimates total uplink interference of femtocell user equipments (FUEs). In order to cope with interference issues, the MBS decides the transmit power of macrocell user equipment (MUE) according to the uplink power budget. In the meanwhile, the interference pricing mechanism is introduced. We assume that the MBS protects itself by pricing the interference on each FUEs, so as to achieve the goal of controlling the interference from FUEs. Simulation results show that the proposed algorithm yields a significant performance improvement in terms of the channel capacity.

A proximity-based service (PBS) leverages the estimated proximity to provide users the accessibility to object or location restricted service. This paper exploits the interaction between Bluetooth Low Energy (BLE) Beacon and smartphone to set forth the fundamental building block of a beacon-based PBS system. In real-world scenarios, a beacon-based PBS system might suffer from sparse conditions when some beacons malfunction or beacons can only be deployed in a few specific positions. Motivated by such limitations, a similarity filter extended with compressive sampling matching pursuit (SF-CoSaMP) is proposed to ensure the reliability of proximity detection under such sparse conditions before smartphone proceed to retrieve the corresponding PBS. An extensive simulation with large volume of collected data has been conducted and the results prove the reliability of the proposed algorithm with high detection accuracy in an environment with sparse deployment.

This paper addresses the problem of pilot contamination in multi-cell multiuser Large-Scale Multiple-Input Multiple-Output (LS-MIMO) aided orthogonal frequency division multiplexing (OFDM) systems, and the exact closed-form expression for the mean square error (MSE) of the classical least square (LS) channel estimation algorithm is derived. Then, a pilot design criterion is proposed to design the optimal pilot sequences for mitigating the pilot contamination. Following this criterion, the improved Chu sequences with perfect autocorrection property are employed. Finally, simulation results verify the effectiveness of the pilot design scheme.

This paper proposes an incremental localization algorithm in wireless sensor network based on the estimation of distribution algorithms. In this algorithm, the distances between an unknown node and the anchor nodes are measured, and then samples are chosen in the area in which the unknown node is located possibly. Thus, the high accuracy samples selection is based on the calculated fitness, and the probability distribution of the location coordinates is updated. It optimizes the location results through the learning and evolution of the samples and the probability distribution. Simulation results show that the proposed algorithm achieves the comparable performance with other up-to-date complex localization algorithms at low noise level.

In this paper, we propose a simple but effective lattice reduction (LR) scheme for an uplink with two antennas at the base station and two-users, each employing the Alamouti space-time block coding (STBC). By exploiting the inherent STBC structure of transmitted symbols from users, the proposed scheme computes the LLL-reduced channel matrix with low complexity. Simulation results reveal that the proposed scheme can achieve the same performance as a conventional lattice reduction schemes while saving about 70% computational complexity in terms of floating-point operations (flops).

In this paper, we analysis the capacity of the clustered network multiple-input multiple-output (MIMO) systems with overlap. We firstly propose a strategy for clustering base stations (BSs) for the analysis tractability. With an assumption of a large number of transmit and receive antennas, an analytical expression of the ergodic capacity using random matrix theorems is derived. In particular, we develop an asymptotic capacity in low signal to noise ratio (SNR) regime. Based on that, we suggest that the number of overlapping degree of freedom (ODOF) is larger than ⌈B/6⌉ for the sake of the capacity increment, where B refers to the total number of BSs in the multicell network. We show through numerical examples that the analytical results match the simulation results even when the number of antennas is small.