Data Buffer Size Research Articles

A Synchronous iEEG Data Acquisition Framework for Dual Brain Interchange Systems.

This study presents a new data acquisition Framework for synchronous dual Brain Interchange (BIC) systems recording. The setup expands the capacity for data recording by offering access to up to 64 channels. The environment utilizes our Simulink model, incorporating functionalities for synchronization using a master clock and email-based status updates. We evaluated the framework in the lab simulations, and we observed a 38 ms post-synchronization delay between the systems. We also demonstrated that this error can be minimized to as low as 5 ms through adjustments in the master clock resolution and data buffer size. We estimated units' sampling frequency with high accuracy to avoid desynchronization. We evaluated the setup on the intracranial EEG (iEEG) recording simultaneously with the clinical system and performed spike detection on the post-synchronized iEEG. We observed over 95% similarity rate between the dual BIC and clinical system. Additionally, we explored the optimal configuration for ground and reference connections between systems to achieve the highest signal quality, along with investigating the implications of frequency interference in dual-system operations.

Optimal Event-Based Policy for Remote Parameter Estimation in Wireless Sensing Architectures Under Resource Constraints

Energy is a resource bottleneck in wireless sensing networks (WSNs) relying on energy harvesting for their operations. This is pronounced in WSNs whose data is used for remote parameter estimation because only a subset of the measured information can be transmitted to the estimator. While much attention has been separately paid to communication schemes for energy-aware data transmission in WSNs under resource constraints and controlled parameter estimation, there has yet to emerge a censoring policy that minimizes the variance of a measured process’ estimated component parameters subject to realistic constraints imposed by the WSN. Consequently, this paper presents the derivation of an optimal event-based policy governing data collection and transmission that accounts for energy and data buffer sizes, stochastic models of harvested energy and event arrivals, value of information of measured data, and temporal death. The policy is optimal in the sense that it maximizes the information rate of transmitted data, thereby producing the best possible estimates of the process parameters using the modified maximum likelihood estimation given the system constraints. Experimental and simulation-based results reflect these objectives and illustrate that the framework is robust against significant uncertainty in the initial parameter estimates.

An Energy-Efficient Controller for Wirelessly-Powered Communication Networks

In a wirelessly-powered communication network (WPCN), an energy access point (E-AP) supplies the energy needs of the network nodes through radio frequency wave transmission, and the nodes store their received energy in their batteries for possible data transmission. In this paper, we propose an online control policy for energy transfer from the E-AP to the wireless nodes and for data transfer among the nodes. With our proposed control policy, all data queues of the nodes are stable, while the average energy consumption of the network is shown to be within a bounded gap of the minimum energy required for stabilizing the network. Our proposed policy is designed using a quadratic Lyapunov function to capture the limitations on the energy consumption of the nodes imposed by their battery levels. We show that under the proposed control policy, the backlog level in the data queues and the stored energy level in the batteries fluctuate in small intervals around some constant levels. Consequently, by imposing a negligible average data drop rate, the data buffer size and the battery capacity of the nodes can be significantly reduced.

Finite Blocklength Entropy-Achieving Coding for Linear System Stabilization

In this article, we consider the minimum data rate problem for linear system stabilization under noiseless communication channels. Previous results indicated that having a data rate very approaching the entropy bound leads to large delays and data buffer sizes. In analogy, the entropy bound in Shannon's source coding theorem in traditional information theory displays this behavior, where the data rate can be arbitrarily close to the entropy bound but only at the cost of boundlessly enlarging the blocklength. However, in this article, we show the analogy is not strict. We prove that it is possible to stabilize a linear system at a rate equal to the entropy bound with zero delay, i.e., where each system state is encoded and decoded within one time unit. We establish a set of sufficient conditions for guaranteeing zero-delay entropy-achieving codes. Following this, we design an entropy-achieving code with finite blocklength satisfying the set of sufficient conditions, where the codeword length is uniformly bounded.

Remote Estimation of Correlated Sources Under Energy Harvesting Constraints

Remote estimation with an energy harvesting sensor with a limited data and energy buffer is considered. The sensor node observes an unknown temporally correlated field and communicates its observations to a remote fusion center using the energy it harvested. The fusion center employs linear minimum mean-square error estimation to reconstruct the unknown field. We provide performance guarantees for the estimation error under a block transmission scheme, where at each transmission block, data and energy buffers are completely emptied. Our bounds provide insights into how statistical properties of the energy harvesting process and buffer sizes may affect the estimation error. In particular, these bounds suggest insensitivity of the performance to buffer sizes for signals with low degree of freedom and suggest performance improvements with increasing buffer sizes for signals with relatively higher degree of freedom. Depending only on the mean, variance, and finite support of the energy arrival process, these results provide insights for the energy and data buffer sizes for deployment in future energy harvesting wireless sensing systems.

Relaying protocols for buffer‐aided energy harvesting wireless cooperative networks

Here, the authors study a buffer-aided relay system, in which the source node and the relay node are energy harvesting nodes, and the source node transmits the information to the destination node via the relay node. Both the source node and relay node are assumed to equipped with an energy buffer and can temporarily store the harvested energy; in addition, the relay node possesses a data buffer to store the received information temporarily. Particularly, the authors propose two buffer-aided energy harvesting relay protocols, namely the harvest-then-transmit (HtT) protocol and the joint link selection and power allocation (JLSPA) protocol. For the HtT protocol, the expressions of the average achievable throughput and the optimal power splitting factor are obtained in close form. For the JLSPA protocol, based on the channel state information and buffer state information, a JLSPA protocol which maximises the average achievable throughput is presented. Furthermore, for the JLSPA protocol, the bounds on average achievable rate, average data buffer size, and average delay are analysed and the throughput-delay trade-off is revealed. The simulation results show that the proposed buffer-aided relaying scheme could improve the throughput performance of energy harvesting relay system significantly.

Delay Constrained Energy Harvesting Networks with Limited Energy and Data Storage

This paper studies energy harvesting transmitters in the single user channel, the two-way channel, and the two-way relay channel with block fading. Each transmitter is equipped with a finite battery to store the harvested energy, and a finite buffer to store the data that arrive during the communication session. We consider delay sensitive applications and maximize throughput while enabling timely delivery of data with delay constraints. We show that the resulting delay limited throughput maximization problem can be solved using alternating maximization of two decoupled problems termed the energy scheduling problem and the data scheduling problem. We solve the energy scheduling problem using a modified directional waterfilling algorithm with right permeable taps, water pumps, and overflow bins and the data scheduling problem with forward induction. Additionally, we identify the online optimum policy for throughput maximization. We provide numerical results to verify our analytical findings and to demonstrate the impact of the finite data buffer capacity and the delay requirements on the throughput. We observe that larger buffer sizes become useful for more lenient delay requirements, and a data buffer size that is comparable to the throughput within one time slot accounts for the majority of the increase in throughput.

Source-Channel Coding Under Energy, Delay, and Buffer Constraints

Source and channel coding for an energy-limited wireless sensor node is investigated. The sensor node observes independent Gaussian source samples with variances changing over time slots. The channel is modeled as a flat fading channel, whose gain remains constant during each time slot, and changes from one time slot to the next. The compressed samples are stored in a finite data buffer, and need to be delivered to the destination in at most d time slots. The objective is to minimize the average squared-error distortion between the source samples and their reconstructions. First, a battery operated system, in which the sensor node has a finite amount of energy at the beginning of transmission, is investigated. Then, the impact of energy harvesting, and the energy cost of processing and sampling are considered. The optimal compression and transmission policy is formulated as the solution of a convex optimization problem, and the properties of the optimal policies are identified. For the strict delay case, d=1, a two-dimensional (2D) waterfilling interpretation is provided. Numerical results are presented to illustrate the structure of the optimal policy, and to analyze the effect of the delay constraints, data buffer size, energy harvesting, and processing and sampling costs.

Cross-layer analysis of protocol delay in mobile devices receiving BCMCS

Broadcast and multicast services allow the high-speed delivery of multimedia content to multiple subscribers over CDMA2000 wireless networks. This relies on a high-rate broadcast packet data system with an air interface governed by two interacting protocols: the medium access control (MAC) protocol specifies the methods of multiplexing and of forward error correction used to reduce the radio link error-rate seen by the higher layers; and the security protocol specifies the procedures used to encrypt and decrypt content, following the Advanced Encryption Standard. We investigated the mutual effect of these protocols, in the context of an ARM9-based mobile platform, and their influence on delay. This allowed us to propose a novel analytic model that can predict the total delay by summing the separate but related delays incurred by implementations of the MAC and security protocols with particular parameters. This cross-layer model includes the characteristics of error control in the MAC layer and the varying condition of the fading channel in the physical layer. We can use this model to estimate the size of data buffers that mobiles require to provide a seamless multimedia service.

Performance analysis of packet data buffer in CDMA2000 radio access network supporting VPOP (Voice Preference Over Packet Data)

The performance of a packet data buffer in a CDMA2000 radio access network supporting voice preference over packet data (VPOP) is investigated. Through numerical analysis the relationship between the packet data buffer size and the voice call holding time is investigated.