Doohwan Lee

Dense SDM (12-Core

We propose long-haul space-division-multiplexing (SDM) transmission systems employing parallel multiple-input multiple-output (MIMO) frequency-domain equalization (FDE) and transmission fiber with low differential mode delay (DMD). We first discuss the advantages of parallel MIMO FDE technique in long-haul SDM transmission systems in terms of the computational complexity, and then, compare the complexity required for parallel MIMO FDE as well as the conventional time-domain equalization techniques. Proposed parallel MIMO FDE that employs low baud rate multicarrier signal transmission with a receiver-side FDE enables us to compensate for 33.2-ns DMD with considerably low-computational complexity. Next, we describe in detail the newly developed fiber and devices we used in the conducted experiments. A graded-index (GI) multicore few-mode fiber (MC-FMF) suppressed the accumulation of DMD as well as intercore crosstalk. Mode dependent loss/gain effect was also mitigated by employing both a ring-core FM erbium-doped fiber amplifier and a free-space optics type gain equalizer. By combining these advanced techniques together, we finally demonstrate 12-core × 3-mode dense SDM transmission over 527-km GI MC-FMF without optical DMD management.

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In this paper, ranging process in IEEE 802.16e OFDMA systems is analyzed and performance is evaluated. Ranging process provides initial network entry, uplink synchronization, and system coordination. In the system initialization state, multiple users transmit randomly selected ranging code set and BS conducts multiple user identification by ranging code identification and uplink synchronization by transmission delay estimation. Ranging detection is based on peak detection exploiting the cross-correlation property of ranging code. However, the cross-correlation property is prone to be affected by channel characteristics and interference in the multipath fading channel. This paper analyzed the performance of ranging process in the multipath fading channel with 6 practical channel models and investigated the effect of channel characteristics and SNR. For the probabilistic analysis PDF of ranging signal is also induced using Rayleigh approximation. Metrics of the performance evaluation consider both PHY and MAC layer. Simulations are conducted concerning various factors such as multipath profiles, ranging detection miss and error thresholds, cyclic prefix duration, and MAC layer parameters. The results of will give a thorough understanding on ranging process.

3-Mode) Transmission Over 527 km With 33.2-ns Mode-Dispersion Employing Low-Complexity Parallel MIMO Frequency-Domain Equalization

We present a high throughput data gathering protocol for wireless sensor network. Different from existing works we elaborate a token-scheduled multi-channel TDMA protocol named TKN-TWN that provides better scalability and topology adaptability. TDMA is more suitable for high throughput data collection than CSMA. However, TDMA-based protocols require complex scheduling of the transmission time slot. The scheduling burden is a primary stumbling block of topology adaptability. To ease the scheduling burden while exploiting the advantages of TDMA, we propose to use two tokens arbitrate transmission and associate the ownership of tokens with transmission slot assignment toward throughput optimization. Due to the simplified scheduling based on tokens, TKN-TWN is able to provide scalability and topology adaptability. In addition, TKN-TWN allows burst transmission by leveraging the contention-free nature of TDMA, and sustains high throughput with burst data transmission when sensor nodes have similar traffic pattern. An extensive simulation study indicates that TKN-TWN achieves robust data delivery even in densely deployed large scale sensor networks. Experimental results on Tmote Sky show that TKN-TWN is able to provide throughput over 10 KByte/s, an improvement of about 23% over a prior work.

Performance Analysis of Ranging Process in IEEE 802.16e OFDMA Systems

It is often observed that various differential mode delays (DMDs) coexist in single multi-core fiber (MCF) and/or few-mode fiber (FMF) transmission. From a multi-input and multi-output (MIMO) equalization perspective, this indicates optimum equalization tap length for each multi-core and/or multi-mode signal varies according to its DMD. Correspondingly, complex calculation of finding each optimum tap length is necessary to obtain satisfactory performance. This paper presents a new adaptive MIMO equalization method to deal with various DMDs while avoiding such complex calculation. The method uses the same tap length for all multi-core and/or multi-mode signals according to the maximum DMD to reduce the calculation cost. To rectify negative effects such as noise enhancement due to the non-optimum tap length setting, the method applies the improved proportionate normalized least mean square (IPNLMS) with leveraged equalization coefficients update on the basis of the overall sparsity of coefficients. IPNLMS inherently updates equalization coefficients by proportionately promoting the previous coefficients in order that the coefficients of signal part are promoted while those of noise part are suppressed. To determine the IPNLMS parameters that govern the amount of the promotion, the presented method uses a simple sparsity metric that calculates the overall sparsity of coefficients. Then, the sparsity metric is mapped to IPNLMS parameters in a manner that the overall sparsity of coefficients is progressively facilitated as adaptation. Evaluations using experimental data of FMF transmission over 527 km end-to-end with 33.2 ns maximum DMD show the presented method effectively deals with various DMDs to suppress the noise and obtains 0.7 dB of Q-factor performance enhancement comparing to the conventional method.

A Token Scheduled High Throughput Multi-Channel Data Collection Protocol for Wireless Sensor Network

Recently developed compressed sensing theory enables signal acquisition and reconstruction from incomplete information with high probability provided that the signal is sparsely represented in some basis. This paper applies compressed sensing for spectrum sensing in a networked system. To tackle the calculation and communication cost problems, this paper also applies structured compressed sensing and 1-bit compressed sensing. Measurement using the partial random circulant matrices can reduce the calculation cost at the sacrifice of a slightly increased number of measurements by utilizing the fact that a circulant matrix is decomposed by multiplications of structured matrices. This paper investigates the tradeoff between calculation cost and compression performance. 1-bit compressed sensing extracts only sign data (1-bit quantization) from measured data, and reconstructs the original signal from the extracted sign data. Therefore, 1-bit compressed sensing can save communication costs associated with spectrum sensing in a networked system. This paper evaluates the efficiency of 1-bit compressed sensing. In addition, this paper also proposes a block reconstruction algorithm for 1-bit compressed sensing that uses the block sparsity of the signals. Empirical study shows that partial random circulant matrices work as efficient as completely random measurement matrices for spectrum sensing and that 1-bit compressed sensing can be used for spectrum sensing with greatly reduced communication costs.

A Sparsity Managed Adaptive MIMO Equalization for Few-Mode Fiber Transmission With Various Differential Mode Delays

Cognitive radio sensor networks (CRSNs) is the state-of-the-art communication paradigm for power constrained short range data communication. It is one of the potential technologies adopted for Internet of Things (IoT) and other futuristic machine-to-machine-based applications. Many of these applications are power constrained and delay sensitive. Therefore, CRSN architecture must be coupled with different adaptive and robust communication schemes to take care of the delay and energy efficiency at the same time. Considering the tradeoff that exists in terms of energy efficiency and overhead delay for a given data packet length, it is proposed to transmit the physical layer payload with an optimal packet size (OPS) depending on the network condition. Furthermore, due to the cognitive feature of CRSN architecture overhead energy consumption due to channel sensing and channel handoff plays a critical role. Based on the above premises, in this paper, we propose a heuristic exhaustive search-based Algorithm-1 and a computationally efficient suboptimal low complexity Karuh–Kuhn–Tucker (KKT) condition-based Algorithm-2 to determine the OPS in CRSN architecture using variable rate m-QAM modulation. The proposed algorithms are implemented along with two main cognitive radio assisted channel access strategies based on distributed time slotted-cognitive medium access control (DTS-CMAC) and centralized common control channel-based cognitive medium access control (CC-CMAC) and their performances are compared. The simulation results reveal that proposed Algorithm-2 outperforms Algorithm-1 by a significant margin in terms of its implementation time. For the exhaustive search-based Algorithm-1 the average time consumed to determine OPS for a given number of cognitive users is 1.2 s, while for KKT-based Algorithm-2, it is of the order of 5–10 ms. CC-CMAC with OPS is most efficient in terms of overall energy consumption but incurs more delay as compared to DTS-CMAC with OPS scheme.

Spectrum Sensing for Networked System Using 1-Bit Compressed Sensing with Partial Random Circulant Measurement Matrices

This paper presents a preambleless communication system to realize industrial wireless control networks (WCN) which are characterized by short payloads and strict real-time requirements. The reduction of the preamble is greatly beneficial for the real-time operation because the transmission time of preambles is longer than that of the short payloads. Our system is implemented on a software defined radio (SDR) platform using GNU Radio toolkit. Experiments show the packet error rate (PER) performance is close to that of the theoretical value in an AWGN (additive white Gaussian noise) environment. Demonstrations show the real-time remote control of inverted pendulums on this system.

Packet Size Optimization for Cognitive Radio Sensor Networks Aided Internet of Things

The determination of optimal packet size (OPS) for a cognitive radio-assisted sensor networks (CRSNs) architecture is non-trivial. State of the art in this area describes various complex techniques to determine OPS for CRSNs. However, it is observed that under high interference from the surrounding users, it is not possible to determine a feasible OPS of data transmission under the simple point-to-point CRSN topology. This is contributed primarily to the peak transmit power constraint of the cognitive nodes. To address this specific challenge, this paper proposes a multiple-input multiple output-based CRSNs (MIMO-CRSNs) architecture for futuristic technologies, such as Internet of Things and machine-to-machine communications. A joint optimization problem is formulated, considering network constraints, such as the overall end-to-end latency, interference duration caused to the non-cognitive users, average BER, and transmit power. We propose our Algorithm 1 based on the generic exhaustive search technique to solve the optimization problem. Furthermore, a low complexity suboptimal Algorithm 2 based on solving classical Karush–Kuhn–Tucker conditions is proposed. These algorithms for MIMO-CRSNs are implemented in conjunction with two different channel access schemes. These channel access schemes are time-slotted distributed cognitive medium access control denoted as MIMO-DTS-CMAC and CSMA/CA-assisted centralized common control channel-based cognitive medium access control denoted as MIMO-CC-CMAC. Simulations reveal that the proposed MIMO-CRSN outperforms the conventional point-to-point CRSN in terms of overall energy consumption. Moreover, the proposed Algorithm 1 and Algorithm 2 show a perfect match, and the implementation complexity of Algorithm 2 is less than Algorithm 1. Algorithm 1 takes almost 680 ms to execute and provides OPS value for a given number of users, whereas Algorithm 2 takes 4–5 ms on average to find the OPS for the proposed MIMO-CRSN framework.

Preambleless TDD/TDMA OFDM system for real-time wireless control networks

An adaptive MIMO equalization method is presented for few-mode fiber transmission where various differential mode delays (DMDs) simultaneously occur. Evaluation using experimental data shows its use of sparsity to promote equalization effectively deals with various DMDs to suppress the noise.

Packet-Size Optimization for Multiple-Input Multiple-Output Cognitive Radio Sensor Networks-Aided Internet of Things

Wireless sensor networks generate big-data which leads to overwhelming traffic in the networks. Compressed sensing (CS) can exploit signal sparsity to reduce the amount of traffic. We evaluate the performance of 1-bit CS with a circulant random bipolar measurement matrix. The results show that 1-bit CS can decrease traffic at higher degree in wireless sensor networks compared to the conventional CS. Moreover, applying a circulant random bipolar measurement matrix overcomes the limitation of memory in sensor nodes. The scheme is studied in terms of reconstruction accuracy and memory consumption by using real sensor data. The implementation on sensor nodes validates the viability on a practical system.