114 research outputs found
Timely Estimation Using Coded Quantized Samples
The effects of quantization and coding on the estimation quality of a
Gauss-Markov, namely Ornstein-Uhlenbeck, process are considered. Samples are
acquired from the process, quantized, and then encoded for transmission using
either infinite incremental redundancy or fixed redundancy coding schemes. A
fixed processing time is consumed at the receiver for decoding and sending
feedback to the transmitter. Decoded messages are used to construct a minimum
mean square error (MMSE) estimate of the process as a function of time. This is
shown to be an increasing functional of the age-of-information, defined as the
time elapsed since the sampling time pertaining to the latest successfully
decoded message. Such (age-penalty) functional depends on the quantization
bits, codeword lengths and receiver processing time. The goal, for each coding
scheme, is to optimize sampling times such that the long term average MMSE is
minimized. This is then characterized in the setting of general increasing
age-penalty functionals, not necessarily corresponding to MMSE, which may be of
independent interest in other contexts.Comment: To appear in ISIT 202
Sample, Quantize and Encode: Timely Estimation Over Noisy Channels
The effects of quantization and coding on the estimation quality of
Gauss-Markov processes are considered, with a special attention to the
Ornstein-Uhlenbeck process. Samples are acquired from the process, quantized,
and then encoded for transmission using either infinite incremental redundancy
(IIR) or fixed redundancy (FR) coding schemes. A fixed processing time is
consumed at the receiver for decoding and sending feedback to the transmitter.
Decoded messages are used to construct a minimum mean square error (MMSE)
estimate of the process as a function of time. This is shown to be an
increasing functional of the age-of-information (AoI), defined as the time
elapsed since the sampling time pertaining to the latest successfully decoded
message. Such functional depends on the quantization bits, codewords lengths
and receiver processing time. The goal, for each coding scheme, is to optimize
sampling times such that the long-term average MMSE is minimized. This is then
characterized in the setting of general increasing functionals of AoI, not
necessarily corresponding to MMSE, which may be of independent interest in
other contexts.
We first show that the optimal sampling policy for IIR is such that a new
sample is generated only if the AoI exceeds a certain threshold, while for FR
it is such that a new sample is delivered just-in-time as the receiver finishes
processing the previous one. Enhanced transmissions schemes are then developed
in order to exploit the processing times to make new data available at the
receiver sooner. For both IIR and FR, it is shown that there exists an optimal
number of quantization bits that balances AoI and quantization errors, and
hence minimizes the MMSE. It is also shown that for longer receiver processing
times, the relatively simpler FR scheme outperforms IIR.Comment: Accepted for publication in the IEEE Transactions on Communications.
arXiv admin note: substantial text overlap with arXiv:2004.1298
Cognitive Communications in White Space: Opportunistic Scheduling, Spectrum Shaping and Delay Analysis
abstract: A unique feature, yet a challenge, in cognitive radio (CR) networks is the user hierarchy: secondary users (SU) wishing for data transmission must defer in the presence of active primary users (PUs), whose priority to channel access is strictly higher.Under a common thread of characterizing and improving Quality of Service (QoS) for the SUs, this dissertation is progressively organized under two main thrusts: the first thrust focuses on SU's throughput by exploiting the underlying properties of the PU spectrum to perform effective scheduling algorithms; and the second thrust aims at another important QoS performance of the SUs, namely delay, subject to the impact of PUs' activities, and proposes enhancement and control mechanisms. More specifically, in the first thrust, opportunistic spectrum scheduling for SU is first considered by jointly exploiting the memory in PU's occupancy and channel fading. In particular, the underexplored scenario where PU occupancy presents a {long} temporal memory is taken into consideration. By casting the problem as a partially observable Markov decision process, a set of {multi-tier} tradeoffs are quantified and illustrated. Next, a spectrum shaping framework is proposed by leveraging network coding as a {spectrum shaper} on the PU's traffic. Such shaping effect brings in predictability of the primary spectrum, which is utilized by the SUs to carry out adaptive channel sensing by prioritizing channel access order, and hence significantly improve their throughput. On the other hand, such predictability can make wireless channels more susceptible to jamming attacks. As a result, caution must be taken in designing wireless systems to balance the throughput and the jamming-resistant capability. The second thrust turns attention to an equally important performance metric, i.e., delay performance. Specifically, queueing delay analysis is conducted for SUs employing random access over the PU channels. Fluid approximation is taken and Poisson driven stochastic differential equations are applied to characterize the moments of the SUs' steady-state queueing delay. Then, dynamic packet generation control mechanisms are developed to meet the given delay requirements for SUs.Dissertation/ThesisPh.D. Electrical Engineering 201
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