The Channel Capacity of General Complex-Valued Load Modulation for Backscatter Communication
1
Citation
9
Reference
10
Related Paper
Citation Trend
Abstract:
This paper studies achievable information rates of backscatter communication systems where the tag performs load modulation with a freely adaptable passive termination. We find that the complex phasor of the tag current is constrained to a disk and that the capacity problem can therefore be described with existing results on peak-power-limited quadrature channels. This allows us to state the channel capacity and the capacityachieving distribution of the load impedance, which is described by non-concentric circles in the right half-plane. For the low-SNR case (SNR < 4.8 dB) we find that channel capacity is achieved by a purely reactive load with Cauchy-distributed reactance. The exposition is based on a system model that abstracts all relevant classes of backscatter communication systems, including RFID. To address practicality, we construct a symbol alphabet that allows for a near-capacity information rate of more than 6 bit per load-switching period at reasonably high SNR. We also find that the rate hardly decreases when typical value-range constraints are imposed on the load impedance.We consider the problem of finding the channel with the highest capacity among several discrete memoryless channels (DMCs) with the same input-output alphabet sizes by means of exploration using multi-armed bandits. This setting is motivated by the problem of exploring channel statistics in communication systems by the invocation of training sequences. We particularly focus on the best arm identification problem and rank the candidate DMCs by their capacities. We propose a capacity estimator based on channel sensing and derive associated concentration results. Using this capacity estimator, we introduce BestChanID, a gap-elimination algorithm, oblivious to the capacity-achieving input distribution, which is guaranteed to output the best DMC, i.e., DMC with the largest capacity, with a desired confidence. We further introduce NaiveChanSel, an algorithm that outputs with certain confidence a DMC whose capacity is close to the largest capacity, and can be used as a subroutine in BestChanID. We analyze the sample complexity of both algorithms, i.e., the total number of channel senses, as a function of the desired confidence parameter, the number of available channels, and the input and output alphabet sizes of the channels. We show that the cost of best channel identification scales cubically with the alphabet size.
Identification
Rank (graph theory)
Cite
Citations (1)
Given a communication system in which there is associated with each channel a channel capacity and a channel reliability, algorithms are developed for finding routes between a given pair of stations, with maximum capacity and with reliability not less than some prescribed minimum value.
Value (mathematics)
Cite
Citations (8)
We consider finite state channels, where the state of the channel is its previous output. We refer to these as Previous Output is the STate (POST) channels. We first focus on POST(α) channels. These channels have binary inputs and outputs, where the state determines if the channel behaves as a Z or an S channel, both with parameter α. We show that the nonfeedback capacity of the POST(α) channel equals its feedback capacity, despite the memory of the channel. The proof of this surprising result is based on showing that the induced output distribution, when maximizing the directed information in the presence of feedback, can also be achieved by an input distribution that does not utilize the feedback. We show that this is a sufficient condition for the feedback capacity to equal the nonfeedback capacity for any finite state channel. We show that the result carries over from the POST(α) channel to a binary POST channel, where the previous output determines whether the current channel will be binary with parameters (a, b) or (b, a). Finally, we show that, in general, feedback may increase the capacity of a POST channel.
Binary erasure channel
Cite
Citations (41)
Shannon's theoremShannon's theorem is one of the most important results in the foundation of information theory (Shannon & Weaver, 1949). It says that the Channel capacity channel capacity Channel Capacity Channel capacity c determines exactly what can effectively be transmitted across the Channel channel. If you want to transmit less than c bits of information per time unit across the Channel channel, you can manage to do it in such a way that you can recover the original information from the channel output with high fidelity (i.e., with low error probabilities Error probability ). However, if you want to transmit more than c bits per time unit across the Channel channel, this cannot be done with high fidelity. This theorem again underlines the fact that information is incompressible (like water) and that a given Channel channel can only transmit a given amount of it in a given time.
Shannon–Hartley theorem
Information Theory
Binary erasure channel
Cite
Citations (1)
One important quantity in assessing the viability of local, autonomous, dynamic channel allocation for microcellular systems is user capacity, defined as the average number of users per channel per cell. Here, we determine the capacity for infinite linear and planar arrays of microcells using a very idealized environment. In particular, propagation and interference considerations are simply represented by the constraint that, if a channel is used in a given cell, it cannot be used in R-consecutive rings of cells around that cell. We investigate the elementary case where there is only a single channel available for use in the system. Using this representation, we compute the best and worst user capacities as well as the capacity achieved by random channel placement. While the environment under which these capacities are derived is highly idealized, the results are useful in two important ways. First, the best capacity and the random channel placement capacity we find here for single-channel, self-organized access are fundamental for computing the traffic characteristics of important multichannel dynamic channel allocation algorithms. Second, the random channel placement capacity is close enough to the best that can be achieved to suggest that local, autonomously implemented, dynamic channel allocation loses little capacity when compared with centrally administered fixed channel allocation.< >
Channel allocation schemes
Cite
Citations (24)
A new timing channel, known as the delay selector channel (DSC), is proposed as an abstract model for applications with timing noise. In this model, channel inputs are delayed by a random amount, and delayed transmissions are summed at the output. Molecular communication is discussed as a principal application of the DSC, since the channel mimics the propagation and reception of molecules under Brownian motion. In this paper, the DSC is described in detail, and a closed-form lower bound is given on capacity.
Molecular Communication
Cite
Citations (11)
We study the interactive channel capacity of an ε-noisy channel. The interactive channel capacity C(ε) is defined as the minimal ratio between the communication complexity of a problem (over a non-noisy channel), and the communication complexity of the same problem over the binary symmetric channel with noise rate ε, where the communication complexity tends to infinity.
Binary symmetric channel
Communication Complexity
Binary erasure channel
Cite
Citations (80)
Given a family of binary-input memoryless output-symmetric (BMS) channels having a fixed capacity, we derive the BMS channel having the highest (resp. lowest) capacity among all channels that are degraded (resp. upgraded) with respect to the whole family. We give an explicit characterization of this channel as well as an explicit formula for the capacity of this channel.
Binary erasure channel
Characterization
Binary symmetric channel
Cite
Citations (4)
In general,communication system includes the source,channel and receiver.Channel refers to the media of information transmission.Channel capacity is the most important parameter.Simply computation of the channel capacity of special channels is introduced,and the Frank-Wolfe method is used to solve the problem of capacity to general discrete memoryless channel,which is more simple than that of iteration.
Binary erasure channel
Cite
Citations (1)
This paper investigates the impact of on-body channel modeling on the accurate estimation of BAN system performance. Channel capacity is used as a measure of the on-body system performance. The actual channel capacity is calculated based on signal measurements conducted in an indoor environment. Then it is compared to the channel capacity calculated for five theoretical channel models obtained through statistical analysis of the measured data.
SIGNAL (programming language)
Cite
Citations (0)