Hybrid Fuzzy Neural Network Based Still Image Compression

Information freshness, characterized by Age of Information (AoI), is important for sensor applications involving timely status updates. In many cases, the wireless signals from one sensor can be received by multiple access points (APs). This article investigates the average AoI for cooperative APs (Co-APs), in which they can share information through a wired backbone network. We first study a basic backbone-assisted Co-AP system where APs share only decoded packets. Experimental results on software-defined radios (SDRs) indicate that Co-AP significantly improves the average AoI performance over a single-AP system. Next, we investigate an improved Co-AP system, called Soft-Co-AP. In addition to sharing decoded packets, Soft-Co-AP shares and collects soft information of packets that the APs fail to decode for further joint decoding. A critical issue in Soft-Co-AP is determining the number of quantization bits that represent the soft information (each soft bit) shared over the backbone. While more quantization bits per soft bit improves the joint decoding performance, it leads to higher backbone delay. We experimentally study the average AoI of Soft-Co-AP by evaluating the tradeoff between the backbone delay and the number of quantization bits. SDR experiments show that when the number of sensors is large, Soft-Co-AP further reduces the average AoI by 12% compared with Co-AP. Interestingly, good average AoI performance is usually achieved when the number of quantization bits per soft bit is neither too large nor too small.

We present an image compression technique using a feedforward neural network. The neural network has three layers: one input layer, one hidden layer and one output layer. The inputs of the neural network are original image data, while the outputs are reconstructed image data which are close to the inputs. If the amount of data required to store the hidden unit values and the connection weights to the output layer of the neural network is less than the original data, compression is achieved. In our experiments, we achieved a compression ratio of about 10 with good reconstructed image quality. The neural network construction algorithm we used has an attractive feature that each addition of a hidden unit to the network will always improve the image quality. Thus our compression scheme is flexible in the sense that the user can trade between image quality and compression ratio depending on the application requirements.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

We propose a spectrally efficient digitized radio-over-fiber (D-RoF) system by grouping highly correlated neighboring samples of the analog signals into multidimensional vectors, where the k-means clustering algorithm is adopted for adaptive quantization. A 30 Gbit/s D-RoF system is experimentally demonstrated to validate the proposed scheme, reporting a carrier aggregation of up to 40 100MHz orthogonal frequency division multiplexing (OFDM) channels with quadrate amplitude modulation (QAM) order of 4 and an aggregation of 10 100MHz OFDM channels with a QAM order of 16384. The equivalent common public radio interface rates from 37 to 150 Gbit/s are supported. Besides, the error vector magnitude (EVM) of 8% is achieved with the number of quantization bits of 4, and the EVM can be further reduced to 1% by increasing the number of quantization bits to 7. Compared with conventional pulse coding modulation-based D-RoF systems, the proposed D-RoF system improves the signal-to-noise-ratio up to ∼9 dB and greatly reduces the EVM, given the same number of quantization bits.

We consider uplink multiuser wireless communications systems, where the base station (BS) receiver is equipped with a large-scale antenna array and resolution adaptive analog-to-digital converters (ADCs). The aim is to maximize the energy efficiency (EE) at the BS subject to constraints on the users’ quality-of-service. The approach is to jointly optimize both the number of quantization bits at the ADCs and the on/off modes of the radio frequency (RF) processing chains. The considered problem is a discrete nonlinear program, the optimal solution of which is difficult to find. We develop an efficient algorithm based on the discrete branch-reduce-and-bound (DBRnB) framework. It finds the globally optimal solutions to the problem. In particular, we make some modifications, which significantly improve the convergence performance. The numerical results demonstrate that optimizing jointly the number of quantization bits and on/off mode can achieve remarkable EE gains compared to only optimizing the number of quantization bits.

This paper focuses on the analysis of an optimal sensing and quantization strategy in a multi-sensor network where each individual sensor sends its quantized log-likelihood information to the fusion center (FC) for non-Bayesian quickest change detection. It is assumed that the sensors are equipped with a battery/energy storage device of finite capacity, capable of harvesting energy from the environment. The FC is assumed to have access to either non-causal or causal channel state information (CSI) and energy state information (ESI) from all the sensors while performing the quickest change detection. The primary observations are assumed to be generated from a sequence of random variables whose probability distribution function changes at an unknown time point. The objective of the detection problem is to minimize the average detection delay of the change point with respect to a lower bound on the rate of false alarm. In this framework, the optimal sensing decision and number of quantization bits for information transmission can be determined with the constraint of limited available energy due to finite battery capacity. This optimization is formulated as a stochastic control problem and is solved using dynamic programming algorithms for both non-causal and causal CSI and ESI scenario. A set of non-linear equations is also derived to determine the optimal quantization thresholds for the sensor log-likelihood ratios, by maximizing an appropriate Kullback-Leibler (KL) divergence measure between the distributions before and after the change. A uniform threshold quantization strategy is also proposed as a simple sub-optimal policy. The simulation results indicate that the optimal quantization is preferable when the number of quantization bits is low as its performance is significantly better compared to its uniform counterpart in terms of average detection delay. For the case of a large number of quantization bits, the performance benefits of using the optimal quantization as compared to its uniform counterpart diminish, as expected.

The main objective of this paper is to propose a Neuro-Fuzzy based algorithm for Image compression. The inputs to the network are original image data, while the outputs are reconstructed image data, which are close to the inputs. If the amount of data required to store the hidden unit values and the connection weights to the output layer is less than the original data, compression is achieved. The compression ratio achieved in this paper is about 9 with good reconstructed image quality. The proposed network has an additional feature that each addition of a hidden unit to the network will always improve the image quality. Further the user can trade between image quality and compression ratio depending on the application requirement. The results are found to be better than the conventional methods.

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> A power-efficient clock/data distribution technique for the input registers of the polyphase comb decimation filter is presented. A general form of the proposed technique is developed with respect to the decimation factor. Both proposed and conventional comb filters are implemented using Xilinx Spartan3 low-power field-programmable gate array family. The implementation results show that applying the proposed technique reduces the dynamic power consumption of the second- and third-order polyphase comb filters up to 62.87% and 57.6%, respectively, depending on the decimation factor and the number of quantizer bits. For a particular power consumption, a higher input sampling rate can be utilized by applying the proposed technique. Consequently, the signal-to-noise ratio of a second-order <formula formulatype="inline"><tex Notation="TeX">$\Sigma\Delta$</tex></formula> modulator is increased using second- and third-order modified filters by 21.6 and 20.5 dB, respectively, depending on the decimation factor and the number of quantizer bits. </para>

This article explores the effects of quantization of feedback information on energy consumption in multiuser wireless communication systems. In order to optimize the energy consumption of the system, the article concentrates on the amount of transmit energy, the additional energy due to quantization and the probability of power outage. Closed form expressions for such parameters are obtained, where the impact of the number of quantization bits is explicitly outlined. An optimization problem is then formulated to find the optimum number of quantization bits able to minimize the consumption in the energy resources. Simulations demonstrate the good results obtainable with the presented optimization strategy, and provide effective validation of the analytic solution presented in the article.

The availability of the Channel State Information (CSI) at the transmitter is crucial for the precoder design in Multi-user Multiple Input Single Output (MU-MISO) systems. In Frequency Division Duplex (FDD) systems, CSI can be just available at the transmitter through a limited feedback channel [1], where we assume that each user estimates, normalizes, and finally quantizes its channel direction with a finite number of quantization bits relayed back error-free and quasi-instantaneous. In this paper, we consider a simple sequential uniform scalar quantization (SQ) scheme of the individual components (real and imaginary parts) of the Channel Direction Information (CDI). Although vector quantization (VQ) schemes [2], [3] still outperform this scalar scheme in terms of quantization error and Bit Error Rate (BER), the former scheme suffers from an exponential search complexity and high storage requirements at the receiver for high number of feedback bits.

Consider a scenario where a subset of sensors has been selected for target tracking in an energy-constrained wireless sensor network. They communicate their quantized state measurements over noisy Rayleigh fading channels to a fusion center, where centralized estimation of target state is performed. In this context, we consider the problem of determining optimum quantization bits and transmission power per bit for all sensors such that both resource utilization and overall estimation error at the fusion center are minimized. Unlike prior efforts, we incorporate the operating state (characterized by the amount of residual battery power) of the sensors in the optimization framework. Analytical solutions for optimum power and number of quantization bits are derived and validated with numerical examples through simulations. We study the effect of channel quality, local measurement noise, and operating states of the sensors on their optimum choice for quantization bits and transmit power per bit.

This paper considers a low-resolution wireless communication system in which transmitted signals are corrupted by fading and additive noise. First, a universal lower bound on the average symbol error probability (SEP), correct for all M-ary modulation schemes, is obtained when the number of quantization bits is not enough to resolve M signal points. Second, in the special case of M-ary phase shift keying (M-PSK), the optimum maximum likelihood detector for equiprobable signal points is derived. Third, utilizing the structure of the derived optimum receiver, a general average SEP expression for the M-PSK modulation with n-bit quantization is obtained when the wireless channel is subject to fading with a circularly-symmetric distribution. Finally, an extensive simulation study of the derived analytical results is presented for general Nakagami-m fading channels. It is observed that a transceiver architecture with n-bit quantization is asymptotically optimum in terms of communication reliability if n is greater than or equal to log_2(M +1). That is, the decay exponent for the average SEP is the same and equal to m with infinite-bit and n-bit quantizers for n greater than or equal to log_2(M+1). On the other hand, it is only equal to half and 0 for n = log_2(M) and n &lt; log_2(M), respectively. Hence, for fading environments with a large value of m, using an extra quantization bit improves communication reliability significantly.

In this paper, a two-dimensional (2-D) vector quantization with vector linear prediction (VLP-VQ) is proposed to improve the transmission performance of the digital mobile fronthaul (MFH). Compared with the conventional vector quantization based on k-means clustering algorithm, the VLP technique can decrease the mean square error of the orthogonal frequency division multiplexing (OFDM) samples by around 5-50% and thereby greatly reduce the quantization noise at the transmitter. In the proof-of-concept experiment, the proposed scheme is demonstrated in a 25-km 10 Gbaud/λ four-level pulse amplitude modulation intensity modulation and direct detection system. In the 5G new radio environment, the experimental results proved the ability of the proposed digital MFH to support 27, 20, 16, and 13 100-MHz OFDM with 4, 16, 64, and 256 quadrature amplitude modulation based antenna-carrier containers at quantization bits per sample of 3, 4, 5, and 6. Compared with the conventional 2-D vector quantization, the error vector magnitude performance of the proposed scheme is significantly improved with the same number of quantization bits.

A differential pulse code modulation (DPCM) based digital mobile fronthaul architecture is proposed and experimentally demonstrated. By using a linear predictor in the DPCM encoding process, the quantization noise can be effectively suppressed and a prediction gain of 7~8 dB can be obtained. Experimental validation is carried out with a 20 km 15-Gbaud/λ 4-level pulse amplitude modulation (PAM4) intensity modulation and direct detection system. The results verify the feasibility of supporting 163, 122, 98, 81 20-MHz 4, 16, 64, 256 QAM based antenna-carrier (AxC) containers with only 3, 4, 5, 6 quantization bits at a sampling rate of 30.72MSa/s in LTE-A environment. Further increasing the number of quantization bits to 8 and 9, 1024 quadrature amplitude modulation (1024 QAM) and 4096 QAM transmission can be realized with error vector magnitude (EVM) lower than 1% and 0.5%, respectively. The supported number of AxCs in the proposed DPCM-based fronthaul is increased and the EVM is greatly reduced compared to the common public radio interface (CPRI) based fronthaul that uses pulse code modulation. Besides, the DPCM-based fronthaul is also experimentally demonstrated to support universal filtered multicarrier signal that is one candidate waveform for the 5th generation mobile systems.

Focusing on massive multiple-input and multiple-output (MIMO) relaying systems with variable resolution analog- to-digital converters (ADCs) and digital-to-analog converters (DACs), we derive the exact and approximate closed-form expressions for the achievable rate over Rayleigh fading channels. Numerical results reveal that the low-resolution DACs degrade the system performance much more than the low-resolution ADCs, and that the low-resolution ADCs and DACs achieve better energy efficiency. Moreover, provided that the total number of quantization bits is constrained, we find that the achievable rate is optimal when all ADCs and DACs adopt the same quantization bits from the perspective of statistic.

Cooperative sensing (CS) is important for efficiently acquiring spectrum awareness in cognitive radio systems. The main focus of this paper is on the BEP (bit error probability) walls for soft decision (SD) based CS in cognitive radios. The BEP wall is a performance limitation for CS caused by imperfect reporting channels. In this paper, a distributed detection system using parallel sensing topology and a fusion center (FC) is considered. Each secondary user (SU) sends energy-based multibit SD to the FC over a reporting channel with a certain BEP. The maximum output entropy (MOE) quantization is considered for quantizing the estimated energy. For the considered case, it is established that the BEP wall values for SD based CS with MOE quantization are identical for arbitrary number of bits for quantization. Moreover, the BEP wall values for SD based CS with MOE quantization are proven to be the same as the BEP wall values for the Chair-Varshney fusion rule, which is an optimal fusion rule for one-bit hard decision based CS. Later relation between the BEP wall values of the SD based CS and the K-out-of-N fusion rules is also established.