We consider a function computation problem in a three-node wireless network. Nodes A and B observe two correlated sources X and Y, respectively, and want to compute a function f (X, Y). To achieve this, nodes A and B ...
详细信息
We consider a function computation problem in a three-node wireless network. Nodes A and B observe two correlated sources X and Y, respectively, and want to compute a function f (X, Y). To achieve this, nodes A and B send messages to a relay node C at rates R-A and R-B, respectively. The relay C then broadcasts a message to A and B at rate R-C. We allow block coding and study the achievable region of rate triples under both zero-error and epsilon-error. As a preparation, we first consider a broadcast network from the relay to A and B. A and B have side information X and Y, respectively. The relay node C observes both X and Y and broadcasts an encoded message to A and B. We want to obtain the optimal broadcast rate such that A and B can recover the function f (X, Y) from the received message and their individual side information X and Y, respectively. For this problem, we show equivalence between epsilon-error and zero-error computations-this gives a rate characterization for zero-error computation. As a corollary, this also gives a rate characterization for the relay network under zero error for a class of functions called component-wise one-to-one functions when the support set of pXY is full. For the relay network, the zero-error rate region for arbitrary functions is characterized in terms of graph coloring of some suitably defined probabilistic graphs. We then give a single-letter inner bound to this rate region. Furthermore, we extend the graph theoretic ideas to address the epsilon-error problem and obtain a single-letter inner bound.
For future wireless networks, enormous numbers of interconnections are required, creating a disorganized topology and leading to a great challenge in data aggregation. Instead of collecting data individually, a more e...
详细信息
ISBN:
(纸本)9781728131061
For future wireless networks, enormous numbers of interconnections are required, creating a disorganized topology and leading to a great challenge in data aggregation. Instead of collecting data individually, a more efficient technique, computation over multi-access channels (CoMAC), has emerged to compute functions by exploiting the signal-superposition property of wireless channels. However, the implementation of CoMAC in disorganized networks with multiple relays (hops) is still an open problem. In this paper, we combine CoMAC and orthogonal communication in the disorganized network to attain the computation of functions at the fusion center. First, to make the disorganized network more tractable, we reorganize the disorganized network into a hierarchical network with multiple layers that consists of subgroups and groups. In the hierarchical network, we propose multi-layer function computation where CoMAC is applied to each subgroup and orthogonal communication is adopted within each group. The general computation rate is derived and the performance is further improved through time allocation.
In this paper, the problem of function computation with privacy and secrecy constraints is considered. The considered model consists of three legitimate nodes (i.e., two transmitters, Alice and Bob, and a fusion cente...
详细信息
In this paper, the problem of function computation with privacy and secrecy constraints is considered. The considered model consists of three legitimate nodes (i.e., two transmitters, Alice and Bob, and a fusion center that acts as the receiver) that observe correlated sources and are connected by noiseless public channels, and an eavesdropper Eve who has full access to the public channels and also has its own source observations. The fusion center would like to compute a function of the distributed sources within a prefixed distortion level under a certain distortion metric. To facilitate the function computation, Alice and Bob will send messages to the fusion center. Different from the existing setups in function computation, we assume that there is a privacy constraint on the sources at Alice and Bob. In particular, Alice and Bob would like to enable the fusion center to compute the function, but at same time, they do not want the fusion center to learn too much information about the source observations. We introduce a quantity to precisely measure the privacy leakage to the fusion center. In addition to this privacy constraint, we also have a secrecy constraint to Eve and use equivocation of sources to measure this quantity. Under this model, we study the tradeoffs among message rates, private information leakage, equivocation, and distortion. We first consider a scenario that has only one transmitter, i.e., the source at Bob is empty, and fully singleletter characterize the corresponding regions. Then, we consider the more general case and provide both outer and inner bounds on the corresponding regions.
In this letter, we propose an adaptive analog function computation (AFC) via fading multiple-access channels in which multiple sensors simultaneously send their observations and then the fusion center computes the des...
详细信息
In this letter, we propose an adaptive analog function computation (AFC) via fading multiple-access channels in which multiple sensors simultaneously send their observations and then the fusion center computes the desired function via the superposition property of wireless channels. In particular, each sensor adaptively sends its observation to the fusion center based on its causal channel state information (CSI). Numerical results show that the adaptive AFC significantly outperforms the conventional non-adaptive AFC in terms of the outage probability of function estimation error. The adaptive AFC operates in a fully distributed manner with local and causal CSI, applicable to various practical sensor network applications.
The future Internet-of-Things network is expected to connect billions of sensors, which incurs high latency for data aggregation. To overcome this challenge, a new technique called over-the-air function computation wa...
详细信息
The future Internet-of-Things network is expected to connect billions of sensors, which incurs high latency for data aggregation. To overcome this challenge, a new technique called over-the-air function computation was recently developed to enable fusion center to receive a desired function directly. It utilizes the superposition property of wireless channel to realize the uniform summation of the desired function. In order to compensate the non-uniform fading of different sensors, we propose a novel uniform-forcing transceiver design for over-the-air function computation. A corresponding min-max optimization problem is formulated to minimize the distortion of the computation which is measured by mean squared error. Due to the non-convexity of the problem, it is relaxed to semidefinite programming first. Then, the performance of the initial solution is improved through successive convex approximation. Simulation results show that the proposed design is able to achieve significant performance gain with low complexity.
computation of function values is critical for designing datapath units in digital signal processors (DSP) and graphics processing units (GPU). Most function computation methods requires lookup tables (LUT) and simple...
详细信息
ISBN:
(纸本)9781538648810
computation of function values is critical for designing datapath units in digital signal processors (DSP) and graphics processing units (GPU). Most function computation methods requires lookup tables (LUT) and simple arithmetic components. In table-bound methods, LUT size takes a significant portion of total hardware area, in particular for high-precision applications. This paper presents a new multi-level lossless table decomposition to further reduce total table size in a recently proposed hierarchical multipartite (HMP) table method which is a generation of the prior bipartite/multipartite table-addition methods. Furthermore, hardware design parameters are optimized by jointly considering all the error sources. Experimental results shows that the proposed design has the chance of further reducing the total table size of HMP, which already has significant table-size saving over previous similar designs.
In this paper, the problem of lossy function computation with secrecy and privacy constraints is considered. The considered model consists of two legitimate terminals Alice and Bob, and an eavesdropper Eve. Under a ce...
详细信息
ISBN:
(纸本)9781538635124
In this paper, the problem of lossy function computation with secrecy and privacy constraints is considered. The considered model consists of two legitimate terminals Alice and Bob, and an eavesdropper Eve. Under a certain distortion constraint, Bob would like to compute a function of two distributed source sequences observed at Bob and Alice respectively. To assist the function computing at Bob's side, Alice is allowed to transmit messages to Bob while satisfying two conditions: 1) the information leakage about Alice's source sequence to Bob is limited, which is denoted as privacy constraint;and 2) Eve will not learn too much information about the Alice's source sequence, which is denoted as secrecy constraint. Under this model, we study the relationship among message rate, privacy constraint, secrecy constraint and the function distortion level. We fully characterized the achievable region of these parameters.
computation of function values is widely used in applications of computer vision, communication, and artificial neural networks. Piecewise polynomial approximation (PPA) with lookup tables (LUT) assisted by arithmetic...
详细信息
ISBN:
(纸本)9781538673768
computation of function values is widely used in applications of computer vision, communication, and artificial neural networks. Piecewise polynomial approximation (PPA) with lookup tables (LUT) assisted by arithmetic units is usually adopted in hardware implementation. In high-precision requirements, the degree of per-segment polynomial is usually increased in order to reduce the size of LUT, but the cost of arithmetic units usually dominates the entire design. This paper analyzes different architectural designs of function computation units based on degree-three polynomial interpolation where the arithmetic units include truncated multipliers, squaring unit (squarer), and cubic unit (cuber). Significant savings in area and power are achieved by selecting architectures without cuber. Furthermore, we design reconfigurable functional computation units supporting four different precision modes with shared hardware resources. The designs are applied to the computation of several arithmetic functions including some popular activation functions for deep neural networks where the bit-accuracy requirement across network layer might vary.
In this manuscript, the problem of function computation with privacy constraints is considered. The problem consists of two legitimate nodes Alice and Bob, and an eavesdropper Eve. Bob would like to compute a function...
详细信息
ISBN:
(纸本)9781538618233
In this manuscript, the problem of function computation with privacy constraints is considered. The problem consists of two legitimate nodes Alice and Bob, and an eavesdropper Eve. Bob would like to compute a function, and the arguments of the function are two source observation sequences that are distributed over the legitimate nodes respectively. To make the function computable at Bob, Alice is allowed to transmit a message to Bob via a public noiseless channel, which Eve has full access to. Under this model, we study the relationship among message rate, private information leakage to Bob and equivocation of Alice's source observations at Eve, and fully single-letter characterize the region of these achievable parameter tuples.
In this manuscript, the problem of function computation with privacy constraints is considered. The problem consists of two legitimate nodes Alice and Bob, and an eaves-dropper Eve. Bob would like to compute a functio...
详细信息
In this manuscript, the problem of function computation with privacy constraints is considered. The problem consists of two legitimate nodes Alice and Bob, and an eaves-dropper Eve. Bob would like to compute a function, and the arguments of the function are two source observation sequences that are distributed over the legitimate nodes respectively. To make the function computable at Bob, Alice is allowed to transmit a message to Bob via a public noiseless channel, which Eve has full access to. Under this model, we study the relationship among message rate, private information leakage to Bob and equivocation of Alice's source observations at Eve, and fully single-letter characterize the region of these achievable parameter tuples.
暂无评论