Search

In this paper, we analyze the high-SNR regime of the $M\times K$ Network MISO channel in which each transmitter has access to a different channel estimate, possibly with different precision. It has been recently shown that, for some regimes, this setting attains the same Degrees-of-Freedom as the ideal centralized setting with perfect Channel State Information (CSI) sharing, in which all the transmitters are endowed with the best estimate available at any transmitter. This result is restricted by the limitations of the Degrees-of-Freedom metric, as it only provides information about the slope of growth of the capacity as a function of the SNR, without any insight about the possible performance at a given SNR. In order to overcome this limitation, we analyze the affine approximation of the rate on the high-SNR regime for this decentralized Network MISO setting for the antenna configurations in which it achieves the Degrees-of-Freedom of the centralized setting. We show that, for a regime of antenna configurations, it is possible to asymptotically attain the same achievable rate as in the ideal centralized scenario. Consequently, it is possible to achieve the beamforming gain of the ideal perfect-CSI-sharing setting even if only a subset of transmitters is endowed with precise CSI, which can be exploited in scenarios such as distributed massive MIMO where the number of transmit antennas is much bigger than the number of served users. This outcome is a consequence of the synergistic compromise between CSI precision at the transmitters and consistency between the locally-computed precoders, which is an inherent trade-off of decentralized settings that does not exist in the centralized CSI configuration. We propose a precoding scheme achieving the previous result, which is built on an uneven structure in which some transmitters reduce the precision of their own precoding vector for the sake of using transmission parameters that can be more easily predicted by the other transmitters. [ABSTRACT FROM AUTHOR]

In this paper, we formulate the problem to cope with the transmission of extra bits over an existing coded transmission link (referred to as coded payload link) without any cost of extra transmission energy or extra bandwidth. This is possible since a gap to the channel capacity typically exists for a practical code. A new concept, termed as accessible capacity, is introduced to specify the maximum rate at which the superposition transmission of extra bits is reliable and has a negligible effect on the performance of the coded payload link. For a binary-input output-symmetric (BIOS) memoryless channel, the accessible capacity can be characterized as the difference between the channel capacity and the mutual information rate of the coded payload link, which can be numerically evaluated for very short payload codes. For a general payload code, we present a simple lower bound on the accessible capacity, given by the channel capacity minus the coding rate of the payload code. We then focus on the scenarios where low-density parity-check (LDPC) codes are implemented for the payload link. We propose to transmit extra bits by random superposition for encoding, and exhaustive search (with the aid of statistical learning) for decoding. We further propose, by establishing an auxiliary channel (called syndrome channel) induced from “zero-forcing” over the binary field, to transmit extra bits with structured codes such as repetition codes and first-order Reed-Muller (RM) codes. Numerical results show that up to 60 extra bits can be reliably transmitted along with a rate-1/2 LDPC code of length 8064. [ABSTRACT FROM AUTHOR]

In a recent work, Beierle, Brinkmann and Leander presented a recursive tree search for finding APN permutations with linear self-equivalences in small dimensions. In this paper, we describe how this search can be adapted to find many new instances of quadratic APN functions. In particular, we found 12,921 new quadratic APN functions in dimension eight, 35 new quadratic APN functions in dimension nine and five new quadratic APN functions in dimension ten up to CCZ-equivalence. Remarkably, two of the 35 new APN functions in dimension nine are APN permutations. Among the 8-bit APN functions, there are three extended Walsh spectra that do not correspond to any of the previously-known quadratic 8-bit APN functions and, surprisingly, there exist at least four CCZ-inequivalent 8-bit APN functions with linearity 27, i.e., the highest possible non-trivial linearity for quadratic functions in dimension eight. [ABSTRACT FROM AUTHOR]

This paper considers the transmission of an infinite sequence of messages (a streaming source) over a packet erasure channel, where every source message must be recovered perfectly at the destination subject to a fixed decoding delay. While the capacity of a channel that introduces only bursts of erasures has been known for more than fifteen years, only recently, the capacity of a channel with either one burst of erasures or multiple arbitrary erasures in any fixed-sized sliding window has been established. However, explicit codes shown to achieve this capacity for any admissible set of parameters require a large field size that scales exponentially with the delay. Recently, it has been shown that there exist codes that achieve the capacity with field size which scales quadratically with the delay. However, explicit constructions were shown only to specific combinations of parameters. This work describes an explicit rate-optimal construction for all admissible channel and delay parameters over a field size that scales quadratically with the delay. [ABSTRACT FROM AUTHOR]

A robust positioning pattern is a large array in which the contents of any subarray of given dimension can determine the subarray’s position, even if they are corrupted by errors. In this paper, we propose new explicit constructions and efficient locating algorithms for robust positioning patterns, which improve upon the previous results in two parameter regimes. For robustness against a constant fraction of errors, we construct the first infinite family of $q$ -ary robust positioning patterns whose rate asymptotically achieves the Singleton bound. For robustness against a constant number of errors, we present the first infinite family of binary robust positioning patterns where the gap between the redundancy and the lower bound is logarithmic in the subarray size and independent of the number of errors. Along with these explicit constructions, we also give efficient locating algorithms with time complexity quartic or cubic in the subarray size. [ABSTRACT FROM AUTHOR]

What is the minimum number of guesses needed on average to guess a realization of a random variable correctly? The answer to this question led to the introduction of a quantity called guesswork by Massey in 1994, which can be viewed as an alternate security criterion to entropy. In this paper, we consider the guesswork in the presence of quantum side information, and show that a general sequential guessing strategy is equivalent to performing a single quantum measurement and choosing a guessing strategy based on the outcome. We use this result to deduce entropic one-shot and asymptotic bounds on the guesswork in the presence of quantum side information, and to formulate a semi-definite program (SDP) to calculate the quantity. We evaluate the guesswork for a simple example involving the BB84 states, both numerically and analytically, and we prove a continuity result that certifies the security of slightly imperfect key states when the guesswork is used as the security criterion. [ABSTRACT FROM AUTHOR]

Let $n (>3)$ be a prime number and ${\mathbb {F}}_{2^{n}}$ a finite field of $2^{n}$ elements. Let $L ={\mathbb {F}}_{2^{n}}\cup \{\infty \}$ be the support set and $g(x)$ an irreducible polynomial of degree 6 over ${\mathbb {F}}_{2^{n}}$. In this paper, we obtain an upper bound on the number of extended irreducible binary Goppa codes $\Gamma (L, g)$ of degree 6 and length $2^{n}+1$. [ABSTRACT FROM AUTHOR]

Blowfish privacy is a recent generalisation of differential privacy that enables improved utility while maintaining privacy policies with semantic guarantees, a factor that has driven the popularity of differential privacy in computer science. This paper relates Blowfish privacy to an important measure of privacy loss of information channels from the communications theory community: min-entropy leakage. Symmetry in an input data neighbouring relation is central to known connections between differential privacy and min-entropy leakage. But while differential privacy exhibits strong symmetry, Blowfish neighbouring relations correspond to arbitrary simple graphs owing to the framework’s flexible privacy policies. To bound the min-entropy leakage of Blowfish-private mechanisms we organise our analysis over symmetrical partitions corresponding to orbits of graph automorphism groups. A construction meeting our bound with asymptotic equality demonstrates tightness. [ABSTRACT FROM AUTHOR]

Consider the computations at a node in a message passing algorithm. Assume that the node has incoming and outgoing messages $\mathbf {x} = (x_{1}, x_{2}, \ldots, x_{n})$ and $\mathbf {y} = (y_{1}, y_{2}, \ldots, y_{n})$ , respectively. In this paper, we investigate a class of structures that can be adopted by the node for computing $\mathbf {y}$ from $\mathbf {x}$ , where each $y_{j}, j = 1, 2, \ldots, n$ is computed via a binary tree with leaves $\mathbf {x}$ excluding $x_{j}$. We make three main contributions regarding this class of structures. First, we prove that the minimum complexity of such a structure is $3n - 6$ , and if a structure has such complexity, its minimum latency is $\delta + \lceil \log (n-2^{\delta }) \rceil $ with $\delta = \lfloor \log (n/2) \rfloor $ , where the logarithm always takes base two. Second, we prove that the minimum latency of such a structure is $\lceil \log (n-1) \rceil $ , and if a structure has such latency, its minimum complexity is $n \log (n-1)$ when $n-1$ is a power of two. Third, given $(n, \tau)$ with $\tau \geq \lceil \log (n-1) \rceil $ , we propose a construction for a structure which we conjecture to have the minimum complexity among structures with latencies at most $\tau $. Our construction method runs in $O(n^{3} \log ^{2}(n))$ time, and the obtained structure has complexity at most (generally much smaller than) $n \lceil \log (n) \rceil - 2$. [ABSTRACT FROM AUTHOR]

The zero capacity region for hexagonal $(d,k)$ run-length constraints is known for many, but not all, $d$ and $k$. The pairs $(d,k)$ for which it has been unproven whether the capacity is zero or positive consist of: (i) $k=d+2$ when $d\ge 2$ ; (ii) $k=d+3$ when $d \ge 1$ ; (iii) $k=d+4$ when either $d=4$ or $d$ is odd and $d \ge 3$ ; and (iv) $k=d+5$ when $d=4$. Here, we prove that the capacity is zero for all of case (i), and for case (ii) whenever $d \ge 7$. The method used in this paper is to reduce an infinite search space of valid labelings to a finite set of configurations that we exhaustively examine using backtracking. In Part II of this two-part series, we use automated procedures to prove that the capacity is zero in case (i) when $2 \le d \le 9$ , in case (ii) when $3 \le d \le 11$ , and in case (iii) when $d \in \{ 4,5,7,9 \}$ , and that the capacity is positive in case (ii) when $d \in \{1,2\}$ , in case (iii) when $d = 3$ , and in case (iv). Thus, the only remaining unknown cases are now when $k=d+4$ , for any odd $d \ge 11$. [ABSTRACT FROM AUTHOR]

Parties connected to independent sources through a network can generate correlations among themselves. Notably, the space of feasible correlations for a given network, depends on the physical nature of the sources and the measurements performed by the parties. In particular, quantum sources give access to nonlocal correlations that cannot be generated classically. In this paper, we derive a universal limit on correlations in networks in terms of their covariance matrix. We show that in a network satisfying a certain condition, the covariance matrix of any feasible correlation can be decomposed as a summation of positive semidefinite matrices each of whose terms corresponds to a source in the network. Our result is universal in the sense that it holds in any physical theory of correlation in networks, including the classical, quantum and all generalized probabilistic theories. [ABSTRACT FROM AUTHOR]

Meta-learning automatically infers an inductive bias by observing data from a number of related tasks. The inductive bias is encoded by hyperparameters that determine aspects of the model class or training algorithm, such as initialization or learning rate. Meta-learning assumes that the learning tasks belong to a task environment, and that tasks are drawn from the same task environment both during meta-training and meta-testing. This, however, may not hold true in practice. In this paper, we introduce the problem of transfer meta-learning, in which tasks are drawn from a target task environment during meta-testing that may differ from the source task environment observed during meta-training. Novel information-theoretic upper bounds are obtained on the transfer meta-generalization gap, which measures the difference between the meta-training loss, available at the meta-learner, and the average loss on meta-test data from a new, randomly selected, task in the target task environment. The first bound, on the average transfer meta-generalization gap, captures the meta-environment shift between source and target task environments via the KL divergence between source and target data distributions. The second, PAC-Bayesian bound, and the third, single-draw bound, account for this shift via the log-likelihood ratio between source and target task distributions. Furthermore, two transfer meta-learning solutions are introduced. For the first, termed Empirical Meta-Risk Minimization (EMRM), we derive bounds on the average optimality gap. The second, referred to as Information Meta-Risk Minimization (IMRM), is obtained by minimizing the PAC-Bayesian bound. IMRM is shown via experiments to potentially outperform EMRM. [ABSTRACT FROM AUTHOR]

This paper proposes a new method of constructing compact fully-connected Quasi-Cyclic Low Density Parity Check (QC-LDPC) codes with girth $g$ = 8, 10, and 12. The originality of the proposed method is to impose constraints on the exponent matrix P to reduce the search space drastically. For a targeted lifting degree of $N$ , the first step of the method is to sieve the integer ring $\mathbb {Z}_{N}$ to make a particular sub-group with specific properties to construct the second column of P (the first column being filled with zeros). The remaining columns of P are determined recursively as multiples of the second column by adapting the sequentially multiplied column (SMC) method whereby a controlled greedy search is applied at each step. The codes constructed with the proposed semi-algebraic method show lengths that can be significantly shorter than their best counterparts in the literature. [ABSTRACT FROM AUTHOR]

Let $q=p^{e}$ be a prime power and $\ell $ be an integer with $0\leq \ell \leq e-1$. The $\ell $ -Galois hull of classical linear codes is a generalization of the Euclidean hull and Hermitian hull. We provide a necessary and sufficient condition under which a codeword of a GRS code or an extended GRS code belongs to its $\ell $ -Galois dual code, generalizing both the Euclidean case and Hermitian case in the literature. By using four different tools: 1) the norm mapping from $\mathbb {F}_{q}^{\ast }$ to $\mathbb {F}_{p^{\ell }}^{\ast }$ ; 2) the direct product of two cyclic subgroups; 3) the coset decomposition of a cyclic group; 4) an additive subgroup of $\mathbb {F}_{q}$ and its cosets, we construct eleven families of $q$ -ary MDS codes with $\ell $ -Galois hulls of arbitrary dimensions, and give the related eleven families of $[[n,k,d;c]]_{q}$ entanglement-assisted quantum error-correcting codes (EAQECCs) with relatively large minimum distance in the sense that $2d=n-k+2+c$. We show that developing the theory on $\ell $ -Galois hulls of $q$ -ary MDS codes in this paper enables us to obtain new $q$ -ary EAQECCs with different kinds of length sets via different $\ell $ , where $2\ell \mid e$. [ABSTRACT FROM AUTHOR]

A large class of MDS linear codes is constructed. These codes are endowed with an efficient decoding algorithm. Both the definition of the codes and the design of their decoding algorithm only require from Linear Algebra methods, making them fully accessible for everyone. Thus, the first part of the paper develops a direct presentation of the codes by means of parity-check matrices, and the decoding algorithm rests upon matrix and linear maps manipulations. The somewhat more sophisticated mathematical context (non-commutative rings) needed for the proof of the correctness of the decoding algorithm is postponed to the second part. A final section locates the Reed-Solomon skew-differential codes introduced here within the general context of codes defined by means of skew polynomial rings. [ABSTRACT FROM AUTHOR]

We study the stability of low-density parity-check codes under blockwise or bitwise maximum a posteriori decoding, where transmission takes place over a binary-input memoryless output-symmetric channel. Our study stems from the consideration of constructing universal capacity-achieving codes under low-complexity decoding algorithms, where universality refers to the fact that we are considering a family of channels with equal capacity. Consider, e.g., the right-regular sequence by Shokrollahi and the heavy-tail Poisson sequence by Luby et al. Both sequences are provably capacity-achieving under belief propagation decoding when transmission takes place over the binary erasure channel. In this paper we show that many existing capacity-achieving sequences of low-density parity-check codes are not universal under belief propagation decoding. We reveal that the key to showing this non-universality result is determined by the stability of the underlying codes. More concretely, for an ordered and complete channel family and a sequence of low-density parity-check code ensembles, we determine a stability threshold associated with them, which gives rise to a sufficient condition under which the sequence is not universal under belief propagation decoding. Moreover, we show that the same stability threshold applies to blockwise or bitwise maximum a posteriori decoding as well. We demonstrate how the stability threshold can determine an upper bound on the corresponding blockwise or bitwise maximum a posteriori threshold, revealing the operational significance of the stability threshold. [ABSTRACT FROM AUTHOR]

Erasure coding has been recognized as a powerful method to mitigate delays due to slow or straggling nodes in distributed systems. This work shows that erasure coding of data objects can flexibly handle skews in the request rates. Coding can help boost the service rate region, that is, increase the overall volume of data access requests that the system can handle. This paper aims to postulate the service rate region as an important consideration in the design of erasure-coded distributed systems. We highlight several open problems that can be grouped into two broad threads: 1) characterizing the service rate region of a given code and finding the optimal request allocation, and 2) designing the underlying erasure code for a given service rate region. As contributions along the first thread, we find the rate regions of maximum-distance-separable, locally repairable, and simplex codes. We show the effectiveness of hybrid codes that combine replication and erasure coding in terms of code design. We also discover fundamental connections between multi-set batch codes and the problem of maximizing the service rate region. [ABSTRACT FROM AUTHOR]

Interleaved Reed–Solomon codes admit efficient decoding algorithms which correct burst errors far beyond half the minimum distance in the random errors regime, e.g., by computing a common solution to the Key Equation for each Reed–Solomon code, as described by Schmidt et al. If this decoder does not succeed, it may either fail to return a codeword or miscorrect to an incorrect codeword, and good upper bounds on the fraction of error matrices for which these events occur are known. The decoding algorithm immediately applies to interleaved alternant codes as well, i.e., the subfield subcodes of interleaved Reed–Solomon codes, but the fraction of decodable error matrices differs, since the error is now restricted to a subfield. In this paper, we present new general lower and upper bounds on the fraction of error matrices decodable by Schmidt et al.’s decoding algorithm, thereby making it the only decoding algorithm for interleaved alternant codes for which such bounds are known. [ABSTRACT FROM AUTHOR]

Shannon showed that to achieve perfect secrecy in point-to-point communication, the message rate cannot exceed the shared secret key rate giving rise to the simple one-time pad encryption scheme. In this paper, we extend this work from point-to-point to networks. We consider a connected network with pairwise communication between the nodes and assume that each node is provided with a certain amount of secret bits before communication commences. An eavesdropper with unlimited computing power has access to all communication and can hack a subset of the nodes not known to the rest of the nodes. We investigate the limits on information-theoretic secure communication with end-to-end encryption for this network. We establish a tradeoff between the secure channel rate (for a node pair) and the secure network rate (sum over all node pair rates) and show that information-theoretic secrecy can be achieved asymptotically if and only if the sum rate of any subset of unhacked channels does not exceed the shared unhacked-secret-bit rate of these channels. We also propose a practical scheme that achieves a good balance of network and channel rates with information-theoretic secrecy guarantee. This work has a wide range of potential applications for which strong secrecy is desired, such as cyber-physical systems, distributed-control systems, and ad-hoc networks. [ABSTRACT FROM AUTHOR]

This paper considers “ $\delta $ -almost Reed–Muller codes”, i.e., linear codes spanned by evaluations of all but a $\delta $ fraction of monomials of degree at most $d$. It is shown that for any $\delta > 0$ and any $\varepsilon >0$ , there exists a family of $\delta $ -almost Reed–Muller codes of constant rate that correct $1/2- \varepsilon $ fraction of random errors with high probability. For exact Reed–Muller codes, the analogous result is not known and represents a weaker version of the longstanding conjecture that Reed–Muller codes achieve capacity for random errors (Abbe-Shpilka-Wigderson STOC ’15). Our proof is based on the recent polarization result for Reed–Muller codes, combined with a combinatorial approach to establishing inequalities between the Reed–Muller code entropies. [ABSTRACT FROM AUTHOR]

Consider a massive random access scenario in which a small set of $k$ active users out of a large number of $n$ potential users need to be scheduled in $b\ge k$ slots. What is the minimum common feedback to the users needed to ensure that scheduling is collision-free? Instead of a naive scheme of listing the indices of the $k$ active users in the order in which they should transmit, at a cost of $k\log (n)$ bits, this paper shows that for the case of $b=k$ , the rate of the minimum fixed-length common feedback code scales only as $k \log (e)$ bits, plus an additive term that scales in $n$ as $\Theta (\log \log (n))$ for fixed $k$. If a variable-length code can be used, assuming uniform activity among the users, the minimum average common feedback rate still requires $k \log (e)$ bits, but the dependence on $n$ can be reduced to $O(1)$. When $b>k$ , the number of feedback bits needed for collision-free scheduling can be significantly further reduced. Moreover, a similar scaling on the minimum feedback rate is derived for the case of scheduling $m$ users per slot, when $k \le mb$. The problem of constructing a minimum collision-free feedback scheduling code is connected to that of constructing a perfect hashing family, which allows practical feedback scheduling codes to be constructed from perfect hashing algorithms. [ABSTRACT FROM AUTHOR]

The de Bruijn graph, its sequences, and their various generalizations, have found many applications in information theory, including many new ones in the last decade. In this paper, motivated by a coding problem for emerging memory technologies, a set of sequences which generalize the window property of de Bruijn sequences, on its shorter subsequences, are defined. These sequences can be also defined and viewed as constrained sequences. Hence, they will be called locally-constrained de Bruijn sequences and a set of such sequences will be called a locally-constrained de Bruijn code. Several properties and alternative definitions for such codes are examined and they are analyzed as generalized sequences in the de Bruijn graph (and its generalization) and as constrained sequences. Various enumeration techniques are used to compute the total number of sequences for any given set of parameters. A construction method of such codes from the theory of shift-register sequences is proposed. Finally, we show how these locally-constrained de Bruijn sequences and codes can be applied in constructions of codes for correcting synchronization errors in the $\ell $ -symbol read channel and in the racetrack memory channel. For this purpose, these codes are superior in their size to previously known codes. [ABSTRACT FROM AUTHOR]

Consider a channel $W$ along with a given input distribution $P_{X}$. In certain settings, such as in the construction of polar codes, the output alphabet of $W$ is ‘too large’, and hence we replace $W$ by a channel $Q$ having a smaller output alphabet. We say that $Q$ is upgraded with respect to $W$ if $W$ is obtained from $Q$ by processing its output. In this case, the mutual information $I(P_{X},W)$ between the input and output of $W$ is upper-bounded by the mutual information $I(P_{X},Q)$ between the input and output of $Q$. In this paper, we present an algorithm that produces an upgraded channel $Q$ from $W$ , as a function of $P_{X}$ and the required output alphabet size of $Q$ , denoted $L$. We show that the difference in mutual informations is ‘small’. Namely, it is $O(L^{-2/(| \mathcal {X}|-1)})$ , where $| \mathcal {X}|$ is the size of the input alphabet. This power law of $L$ is optimal. We complement our analysis with numerical experiments which show that the developed algorithm improves upon the existing state-of-the-art algorithms also in non-asymptotic setups. [ABSTRACT FROM AUTHOR]

This paper proposes a new algorithm, named Householder Dice (HD), for simulating dynamics on dense random matrix ensembles with rotational invariance. Examples include the Gaussian ensemble, the Haar-distributed random orthogonal ensemble, and their complex-valued counterparts. A “direct” approach to the simulation, where one first generates a dense $n \times n$ matrix from the ensemble, requires at least $\mathcal {O}(n^{2})$ resource in space and time. The HD algorithm overcomes this $\mathcal {O}(n^{2})$ bottleneck by using the principle of deferred decisions: rather than fixing the entire random matrix in advance, it lets the randomness unfold with the dynamics. At the heart of this matrix-free algorithm is an adaptive and recursive construction of (random) Householder reflectors. These orthogonal transformations exploit the group symmetry of the matrix ensembles, while simultaneously maintaining the statistical correlations induced by the dynamics. The memory and computation costs of the HD algorithm are $\mathcal {O}(nT)$ and $\mathcal {O}(nT^{2})$ , respectively, with $T$ being the number of iterations. When $T \ll n$ , which is nearly always the case in practice, the new algorithm leads to significant reductions in runtime and memory footprint. Numerical results demonstrate the promise of the HD algorithm as a new computational tool in the study of high-dimensional random systems. [ABSTRACT FROM AUTHOR]

The essential interactive capacity of a discrete memoryless channel is defined in this paper as the maximal rate at which the transcript of any interactive protocol can be reliably simulated over the channel, using a deterministic coding scheme. In contrast to other interactive capacity definitions in the literature, this definition makes no assumptions on the order of speakers (which can be adaptive) and does not allow any use of private/public randomness; hence, the essential interactive capacity is a function of the channel model only. It is shown that the essential interactive capacity of any binary memoryless symmetric (BMS) channel is at least 0.0302 its Shannon capacity. To that end, we present a simple coding scheme, based on extended-Hamming codes combined with error detection, that achieves the lower bound in the special case of the binary symmetric channel (BSC). We then adapt the scheme to the entire family of BMS channels, and show that it achieves the same lower bound using extremes of the Bhattacharyya parameter. [ABSTRACT FROM AUTHOR]

This paper presents finite-blocklength achievability bounds for the Gaussian multiple access channel (MAC) and random access channel (RAC) under average-error and maximal-power constraints. Using random codewords uniformly distributed on a sphere and a maximum likelihood decoder, the derived MAC bound on each transmitter’s rate matches the MolavianJazi-Laneman bound (2015) in its first- and second-order terms, improving the remaining terms to $\frac {1}2\frac {\log {n}}{n}+{O} \left ({\frac {1}{n}}\right)$ bits per channel use. The result $\vphantom {\sum ^{R}}$ then extends to a RAC model in which neither the encoders nor the decoder knows which of ${K}$ possible transmitters are active. In the proposed rateless coding strategy, decoding occurs at a time ${n}_{t}$ that depends on the decoder’s estimate ${t}$ of the number of active transmitters ${k}$. Single-bit feedback from the decoder to all encoders at each potential decoding time ${n}_{i}$ , ${i} \leq {t}$ , informs the encoders when to stop transmitting. For this RAC model, the proposed code achieves the same first-, second-, and third-order performance as the best known result for the Gaussian MAC in operation. [ABSTRACT FROM AUTHOR]

Quantum error-correcting codes play the role of suppressing noise and decoherence in quantum systems by introducing redundancy. Some strategies can be used to improve the parameters of these codes. For example, entanglement can provide a way for quantum error-correcting codes to achieve higher rates than the one obtained by means of the traditional stabilizer formalism. Such codes are called entanglement-assisted quantum error-correcting (EAQEC) codes. In this paper, we utilize algebraic geometry codes to construct several families of EAQEC codes derived from the Euclidean and the Hermitian construction. Three families constructed here consist of codes whose quantum Singleton defect is equal to zero, one, or two. We also construct families of EAQEC codes with an encoding rate exceeding the quantum Gilbert-Varshamov bound. Additionally, asymptotically good towers of linear complementary dual codes are used to obtain asymptotically good families of EAQEC codes consuming maximal entanglement. Furthermore, a simple comparison with the quantum Gilbert-Varshamov bound demonstrates that, by utilizing the proposed construction, it is possible to generate an asymptotically family of EAQEC codes that exceeds this bound. [ABSTRACT FROM AUTHOR]

In compressed sensing, the restricted isometry property (RIP) on $M \times N$ sensing matrices (where $M < N$) guarantees efficient reconstruction of sparse vectors. A matrix has the $(s,\delta)$ - $\mathsf {RIP}$ property if behaves as a $\delta $ -approximate isometry on $s$ -sparse vectors. It is well known that an $M\times N$ matrix with i.i.d. $\mathcal {N}(0,1/M)$ entries is $(s,\delta)$ - $\mathsf {RIP}$ with high probability as long as $s\lesssim \delta ^{2}~M/\log N$. On the other hand, most prior works aiming to deterministically construct $(s,\delta)$ - $\mathsf {RIP}$ matrices have failed when $s \gg \sqrt {M}$. An alternative way to find an RIP matrix could be to draw a random gaussian matrix and certify that it is indeed RIP. However, there is evidence that this certification task is computationally hard when $s \gg \sqrt {M}$ , both in the worst case and the average case. In this paper, we investigate the exact average-case time complexity of certifying the RIP property for $M\times N$ matrices with i.i.d. $\mathcal {N}(0,1/M)$ entries, in the “possible but hard” regime $\sqrt {M} \ll s\lesssim M/\log N$. Based on analysis of the low-degree likelihood ratio, we give rigorous evidence that subexponential runtime $N^{\tilde \Omega (s^{2}/M)}$ is required, demonstrating a smooth tradeoff between the maximum tolerated sparsity and the required computational power. This lower bound is essentially tight, matching the runtime of an existing algorithm due to Koiran and Zouzias. Our hardness result allows $\delta $ to take any constant value in (0, 1), which captures the relevant regime for compressed sensing. This improves upon the existing average-case hardness result of Wang et al., which is limited to $\delta = o(1)$. [ABSTRACT FROM AUTHOR]

The expansion complexity is a new figure of merit for cryptographic sequences. In this paper, we present an explicit formula of the (irreducible) expansion complexity of ultimately periodic sequences over finite fields. We also provide improved upper and lower bounds on the $N$ th irreducible expansion complexity when they are not explicitly determined. In addition, for some infinite sequences with given nonlinear complexity, a tighter upper bound of their $N$ th expansion complexity is given. [ABSTRACT FROM AUTHOR]

A complete characterization for the restricted isometry constant (RIC) bounds on $\delta _{{{ tk}}}$ for all $ {t}>0$ is an important problem on recovery of sparse signals with prior support information via weighted $\ell _{{p}}$ -minimization ($0 < {p} \leqslant 1$). In this paper, new bounds on the restricted isometry constants $\delta _{{{ tk}}}$ ($0 < {t} < \frac {4}{3}{d}$), where $d$ is a key constant determined by prior support information, are established to guarantee the sparse signal recovery via the weighted $\ell _{{p}}$ minimization in both noiseless and noisy settings. This result fills a vacancy on $\delta _{{{ tk}}}$ with $0 < {t} < \frac {4}{3}{d}$ , compared with previous works on $\delta _{{{ tk}}}$ (${t} \geqslant \frac {4}3{d}$). We show that, when the accuracy of prior support estimate is at least 50%, the new recovery condition in terms of $\delta _{{{ tk}}}$ ($0 < {t} < \frac {4}{3}{d}$) via weighted $\ell _{1}$ minimization is weaker than the condition required by classical $\ell _{1}$ minimization without weighting. Our weighted $\ell _{1}$ minimization gives better recovery error bounds in noisy setting. Similarly, the new recovery condition in terms of $\delta _{{{ tk}}}$ ($0 < {t} < \frac {4}{3}{d}$) is extended to weighted $\ell _{{p}}$ ($0 < {p} < 1$) minimization, and it is also weaker than the condition obtained by standard non-convex $\ell _{{p}}$ ($0 < {p} < 1$) minimization without weighting. Numerical illustrations are provided to demonstrate our new theoretical results. [ABSTRACT FROM AUTHOR]

This paper aims to address two fundamental challenges arising in eigenvector estimation and inference for a low-rank matrix from noisy observations: 1) how to estimate an unknown eigenvector when the eigen-gap (i.e. the spacing between the associated eigenvalue and the rest of the spectrum) is particularly small; 2) how to perform estimation and inference on linear functionals of an eigenvector—a sort of “fine-grained” statistical reasoning that goes far beyond the usual $\ell _{2}$ analysis. We investigate how to address these challenges in a setting where the unknown $n\times n$ matrix is symmetric and the additive noise matrix contains independent (and non-symmetric) entries. Based on eigen-decomposition of the asymmetric data matrix, we propose estimation and uncertainty quantification procedures for an unknown eigenvector, which further allow us to reason about linear functionals of an unknown eigenvector. The proposed procedures and the accompanying theory enjoy several important features: 1) distribution-free (i.e. prior knowledge about the noise distributions is not needed); 2) adaptive to heteroscedastic noise; 3) minimax optimal under Gaussian noise. Along the way, we establish valid procedures to construct confidence intervals for the unknown eigenvalues. All this is guaranteed even in the presence of a small eigen-gap (up to $O(\sqrt {n/\mathrm {poly}\log (n)}\,)$ times smaller than the requirement in prior theory), which goes significantly beyond what generic matrix perturbation theory has to offer. [ABSTRACT FROM AUTHOR]

We formulate and study the problem of ON-OFF privacy. ON-OFF privacy algorithms enable a user to continuously switch his privacy between ON and OFF. An obvious example is the incognito mode in internet browsers. But beyond internet browsing, ON-OFF privacy can be a desired feature in most online applications. The challenge is that the statistical correlation over time of a user’s online behavior can lead to leakage of information. We consider the setting in which a user is interested in retrieving the latest message generated by one of $N$ sources. The user’s privacy status can change between ON and OFF over time. When privacy is ON the user wants to hide his request. Moreover, since the user’s requests depend on personal attributes such as age, gender, and political views, they are typically correlated over time. As a consequence, the user cannot simply ignore privacy when privacy is OFF. We model the correlation between user’s requests by an $N$ state Markov chain. The goal is to design query schemes with optimal download rate, that preserve privacy in an ON-OFF privacy setting. In this paper, we present inner and outer bounds on the achievable download rate for $N$ sources. We also devise an efficient algorithm to construct an ON-OFF privacy scheme achieving the inner bound and prove its optimality in the case $N=2$ sources. For $N > 2$ , finding tighter outer bounds and efficient constructions of ON-OFF privacy schemes that would achieve them remains an open question. [ABSTRACT FROM AUTHOR]

With the prevalence of orthogonal frequency-division multiplexing (OFDM) in many standards, e.g., IEEE 802.11, IEEE 802.16, DVB-T, and DVB-T2, a number of variant modulation schemes based on OFDM have been proposed, which resort to signal sparsity to further enhance spectral efficiency and mitigate the high peak-to-average ratio (PAPR) problem. Among these variants, OFDM with subcarrier number modulation (OFDM-SNM) has been proven to be efficient for simple communication systems with low constellation modulation orders and limited decoding capability. To rigorously verify the performance advantages of OFDM-SNM, we present the study of OFDM-SNM in this paper from the information-theoretic perspective. In particular, we determine an upper bound on the mutual information of OFDM-SNM in closed form by using the log sum inequality. Also, we analyze the optimal pattern utilization probabilities (PUPs) for OFDM-SNM by channel-dependent coding and propose an easy-to-implement iterative algorithm to approach the optimal PUPs. Moreover, considering the practical achievability, we propose a Huffman coding based achievable PUP vector construction scheme to obtain the achievable PUPs and the corresponding achievable rate. We carry out numerical simulations to verify the effectiveness of this study and illustrate the efficiency of the obtained PUPs in comparison with several benchmarks. [ABSTRACT FROM AUTHOR]

In this paper, we consider a recently-proposed model of teaching and learning under uncertainty, in which a teacher receives independent observations of a single bit corrupted by binary symmetric noise, and sequentially transmits to a student through another binary symmetric channel based on the bits observed so far. After a given number $n$ of transmissions, the student outputs an estimate of the unknown bit, and we are interested in the exponential decay rate of the error probability as $n$ increases. We propose a novel block-structured teaching strategy in which the teacher encodes the number of 1s received in each block, and show that the resulting error exponent is the binary relative entropy $D\left({\frac {1}{2}\|\max (p,q)}\right)$ , where $p$ and $q$ are the noise parameters. This matches a trivial converse result based on the data processing inequality, and settles two conjectures of [Jog and Loh, 2021] and [Huleihel et al., 2019]. In addition, we show that the computation time required by the teacher and student is linear in $n$. We also study a more general setting in which the binary symmetric channels are replaced by general binary-input discrete memoryless channels. We provide an achievability bound and a converse bound, and show that the two coincide in certain cases, including (i) when the two channels are identical, and (ii) when the student-teacher channel is a binary symmetric channel. More generally, we give sufficient conditions under which our learning rate is the best possible for block-structured protocols. [ABSTRACT FROM AUTHOR]

In this paper, we give a new framework for constructing perfect sequences, called generalized Milewski sequences, over various alphabets including Polyphase (PSK) as well as Amplitude-and-Polyphase (APSK) in general, and for constructing optimal sets of such perfect sequences by using combinatorial designs, called circular Florentine arrays. Specifically, we prove that, given any positive integer $m\geq 1$ , (i) there exists a perfect sequence of period $mN^{2}$ for any positive integer $N$ if there exists a perfect sequence (polyphase or not) of length $m$ ; (ii) an optimal $k$ -set of perfect sequences of length $mN^{2}$ can be constructed if there exist both a $k \times N$ circular Florentine array and an optimal $k$ -set of perfect sequences all of length $m$. This enables us to find some optimal $k$ -set of perfect sequences where $k > p_{\text {min}}-1$ , where $p_{\text {min}}$ is the smallest prime factor of $mN^{2}$. [ABSTRACT FROM AUTHOR]

A multi-resolution quantizer is a sequence of quantizers where the output of a coarser quantizer can be deduced from the output of a finer quantizer. In this paper, we propose an asymptotically scale-invariant multi-resolution quantizer, which performs uniformly across any choice of average quantization step, when the length of the range of input numbers is large. Scale invariance is especially useful in worst case or adversarial settings, ensuring that the performance of the quantizer would not be affected greatly by small changes of storage or error requirements. We also show that the proposed quantizer achieves a tradeoff between rate and error that is arbitrarily close to the optimum. [ABSTRACT FROM AUTHOR]

This paper proposes the Guruswami-Sudan (GS) list decoding algorithm for one-point elliptic codes, in which the interpolation is realized by the module basis reduction (BR). Elliptic codes are a kind of algebraic-geometric (AG) codes with a genus of one. Over the same finite field, they have a greater codeword length than Reed-Solomon (RS) codes, capable of correcting more errors. The GS decoding consists of interpolation and root-finding, while the former that determines the interpolation polynomial $\mathcal {Q}(\text {x}, \text {y}, \text {z})$ dominates the decoding complexity. By defining the Lagrange interpolation function over an elliptic function field, a basis of the interpolation module can be constructed. The desired Gröbner basis that contains $\mathcal {Q}(\text {x}, \text {y}, \text {z})$ can be determined by reducing the constructed basis. This is namely the BR interpolation and it requires less finite field arithmetic operations than the conventional Kötter’s interpolation, facilitating the GS decoding. Re-encoding transform (ReT) is further introduced to facilitate the BR interpolation. This work also shows that both the BR interpolation and its ReT variant will have a lower complexity as the code rate ${k}/{n}$ increases, where n and ${k}$ are the length and dimension of the code, respectively. Our numerical results demonstrate the complexity advantage of the BR interpolation over Kötter’s interpolation, and the performance advantage of elliptic codes over RS codes. [ABSTRACT FROM AUTHOR]

In this paper, by the Hasse-Weil bound, we determine the necessary and sufficient condition on coefficients $a_{1},a_{2},a_{3}\in {\mathbb F} _{2^{n}}$ with $n=2m$ such that $f(x) = {x}^{3\cdot 2^{m}} + a_{1}x^{2^{m+1}+1} + a_{2} x^{2^{m}+2} + a_{3}x^{3}$ is an APN function over ${\mathbb F}_{2^{n}}$. Our work together with the follow-up work by Chase and Lisoněk indicates that all such APN quadrinomials $f(x)$ are affine equivalent to two instances of Gold functions, which resolves the first half of an open problem by Carlet at the International Workshop on the Arithmetic of Finite Fields, 83-107, 2014. [ABSTRACT FROM AUTHOR]

In this paper, we aim to construct a class of complete permutations $\mathcal F$ over $\mathbb F_{q}^{n}$ from some polynomials $f_{1},f_{2},\ldots,f_{n}$ over $\mathbb F_{q}$. First of all, we determine a necessary and sufficient condition such that $\mathcal F$ is complete. Briefly, we transform the completeness of $\mathcal F$ into showing the permutation properties of two polynomials over $\mathbb F_{q}$ obtained from these $f_{i}$ ’s. Then, following the wide applications, we investigate the constructions of linear complete permutations over $\mathbb F_{2}^{n}$ based on the rotations and XORs. The following two cases are considered: the first one is to use some different circularly left shift transforms $f_{i}$ ’s and the second one is to assume $f_{i}$ ’s are of the form $b_{i}f$ with a fixed $f$ and different $b_{i}$ ’s in $\mathbb F_{q}$. In both cases, we show that the completeness of the permutation is closely related to the ranks of some matrices with particular forms, which can be determined by the cycle decomposition of the permutation over the $n$ branches. Besides, we present several explicit linear complete permutations which might be used in the design as well as the provable security of cryptographic schemes. [ABSTRACT FROM AUTHOR]

Deletion errors are notoriously difficult to correct. The capacity of the binary deletion channel is not yet fully known, and there is no completely satisfactory solution even to the specific problem of correcting only two deletion errors. In this paper, we show that polarization theory, along with the powerful methods of polar coding and successive-cancellation decoding, can be extended to channels with deletion errors. Our results further generalize to channels corrupted by insertion errors and/or combinations of insertions and deletions. It was recently proposed to correct deletion errors using polar codes designed for the BEC, precoded with a sufficiently powerful CRC code. In this approach, in order to correct ${d}$ deletions in a codeword of length ${n}$ , successive-cancellation decoding is run separately for each of the $\binom {n}{d}$ possible deletion patterns, while treating the deleted symbols as erasures. The resulting complexity of decoding is ${O}\bigl ({n}^{d+1}\log {n}\bigr)$. In contrast, we develop herein a natural generalization of successive-cancellation decoding to channels with deletion errors. Our algorithm is based on the recursive structure of polar codes, processing the channel output in ${m} = \log _{2} {n}$ decoding layers, just like the conventional successive-cancellation decoder. Every node in each decoding layer propagates its uncertainty about the deletion pattern to the nodes in the next layer and, eventually, the correct deletion pattern becomes visible at the decoder output, with high probability. This makes it possible to consider at most $\binom {d+2}{2}$ scenarios at each node, and the overall decoding complexity is only ${O}\bigl ({d}^{3} {n} \log {n}\bigr)$ , which scales polynomially rather than exponentially with the number of deletions. Based on the proposed algorithm, we also investigate the channel polarization phenomenon. We are able to prove weak polarization where the number of deletions ${d}$ is small, i.e. ${d}={o}({n})$. We also conjecture the strong polarization for when ${d}={O}(1)$ and provide some evidence to support this conjecture. The strong polarization, if proven, implies that our coding scheme achieves the capacity of any binary-input symmetric channel which furthermore introduces a constant number of deletions. [ABSTRACT FROM AUTHOR]

The study of constant-weight codes in $\ell _{1}$ -metric was motivated by the duplication-correcting problem for data storage in live DNA. It is interesting to determine the maximum size of a code given the length ${n}$ , weight ${w}$ , minimum distance ${d}$ and the alphabet size ${q}$. In this paper, based on graph decompositions, we determine the maximum size of ternary codes with constant weight w and distance $2{w}-2$ for all sufficiently large length ${n}$. Previously, this was known only for a very sparse family ${n}$ of density $4/{w}({w}-1)$. [ABSTRACT FROM AUTHOR]

The paper deals with linear information inequalities valid for entropy functions induced by discrete random variables. Specifically, the so-called conditional Ingleton inequalities are in the center of interest: these are valid under conditional independence assumptions on the inducing random variables. We discuss five inequalities of this particular type, four of which has appeared earlier in the literature. Besides the proof of the new fifth inequality, simpler proofs of (some of) former inequalities are presented. These five information inequalities are used to characterize all conditional independence structures induced by four discrete random variables. [ABSTRACT FROM AUTHOR]

The basic goal of threshold group testing is to identify up to $d$ defective items among a population of $n$ items, where $d$ is usually much smaller than $n$. The outcome of a test on a subset of items is positive if the subset has at least $u$ defective items, negative if it has up to $\ell $ defective items, where $0 \leq \ell < u$ , and arbitrary otherwise. This is called threshold group testing. The parameter $g = u - \ell - 1$ is called the gap. In this paper, we focus on the case $g > 0$ , i.e., threshold group testing with a gap. Note that the results presented here are also applicable to the case $g = 0$ ; however, the results are not as efficient as those in related work. Currently, a few reported studies have investigated test designs and decoding algorithms for identifying defective items. Most of the previous studies have not been feasible because there are numerous constraints on their problem settings or the decoding complexities of their proposed schemes are relatively large. Therefore, it is compulsory to reduce the number of tests as well as the decoding complexity, i.e., the time for identifying the defective items, for achieving practical schemes. The work presented here makes five contributions. The first is a more accurate theorem for a non-adaptive algorithm for threshold group testing proposed by Chen and Fu. The second is an improvement in the construction of disjunct matrices, which are the main tools for tackling (threshold) group testing and other tasks such as constructing cover-free families or learning hidden graphs. Specifically, we present a better exact upper bound on the number of tests for disjunct matrices compared with that in related work. The third and fourth contributions are a reduced exact upper bound on the number of tests and a reduced asymptotic bound on the decoding time for identifying defective items in a noisy setting on test outcomes. The fifth contribution is a simulation on the number of tests of the resulting improvements for previous work and the proposed theorems. [ABSTRACT FROM AUTHOR]

This paper focuses on constructions of MDS self-dual codes from (extended) generalized Reed-Solomon (GRS) codes. Let $q = r^{2}$ be an odd prime power. We show that, there exists a $q$ -ary self-dual (extended) GRS code for each even length in the range $[{2r,3r-3}]$ , and for each singly even length in the range $[3r-1,4r]$. This extends the only known consecutive range $[2,2r]$ to $[{2,3r-3}]$ for this case. Furthermore, our general constructions provide many MDS self-dual codes with new parameters which, to the best of our knowledge, were not reported before. [ABSTRACT FROM AUTHOR]

In a recent paper [1], Rini et al. proved a capacity result for the discrete memoryless cognitive interference channel, under the condition named “better cognitive decoding.” We show that this capacity region is the same as the capacity region characterized by Wu et al. in [2]. [ABSTRACT FROM PUBLISHER]

We investigate the problem of downlink user scheduling in multiple-input multiple-output (MIMO) systems with M transmit antennas and K homogeneous multiantenna receiving users. We develop a simple algorithm to schedule a subset of M active users for data transmission.We first identify a set of L candidate users that should provide channel information feedback. We then select M active users among the L candidates based on joint consideration of their effective channel gains and directions. We derive an asymptotic upper bound for the sum rate gap between the maximum sum rate of the M selected active users, achieved by dirty paper coding, and the full sum capacity of the original MIMO broadcast channel. Utilizing the upper bound, we show that the sum rate gap can be reduced to meet user requirement by suitably choosing a large but still finite L. Furthermore, we also investigate the performance of the low complexity zero-forcing beamforming (ZFBF) for the M active users. [ABSTRACT FROM AUTHOR]

For general memoryless systems, the existing information-theoretic solutions have a "single-letter" form. This reflects the fact that optimum performance can be approached by a random code (or a random binning scheme), generated using independent and identically distributed copies of some scalar distribution. Is that the form of the solution of any (information-theoretic) problem? In fact, some counter examples are known. The most famous one is the "two help one" problem: Körner and Marton showed that if we want to decode the modulo-two sum of two correlated binary sources from their independent encodings, then linear coding is better than random coding. In this paper we provide another counter example, the "doubly-dirty" multiple-access channel (MAC). Like the Körner-Marton problem, this is a multiterminal scenario where side information is distributed among several terminals; each transmitter knows part of the channel interference while the receiver only observes the channel output. We give an explicit solution for the capacity region of the binary doubly-dirty MAC, demonstrate how this region can be approached using a linear coding scheme, and prove that the "best known single-letter region" is strictly contained in it. We also state a conjecture regarding the capacity loss of single-letter characterization in the Gaussian case. [ABSTRACT FROM AUTHOR]

The interference channel with degraded message sets (IC-DMS) refers to a communication model, in which two senders attempt to communicate with their respective receivers simultaneously through a common medium, and one sender has complete and a priori (noncausal) knowledge about the message being transmitted by the other. A coding scheme that collectively has advantages of cooperative coding, collaborative coding, and dirty paper coding, is developed for such a channel. With resorting to this coding scheme, achievable rate regions of the IC-DMS in both discrete memoryless and Gaussian cases are derived. The derived achievable rate regions generally include several previously known rate regions as special cases. A numerical example for the Gaussian case further demonstrates that the derived achievable rate region offers considerable improvements over these existing results in the high-interference-gain regime. [ABSTRACT FROM AUTHOR]

A simple signaling method for broadcast channels with multiple-transmit multiple-receive antennas is proposed. In this method, for each user, the direction in which the user has the maximum gain is determined. The best user in terms of the largest gain is selected. The corresponding direction is used as the modulation vector (MV) for the data stream transmitted to the selected user. The algorithm proceeds in a recursive manner where in each step, the search for the best direction is performed in the null space of the previously selected MVs. It is demonstrated that with the proposed method, each selected MV has no interference on the previously selected MVs. Dirty-paper coding is used to cancel the remaining interference. For the case that each receiver has one antenna, the presented scheme coincides with the known scheme based on Gram-Schmidt orthogonalization (QR decomposition). To analyze the performance of the scheme, an upper bound on the cumulative distribution function (CDF) of each subchannel is derived which is used to establish the diversity order and the asymptotic sum-rate of the scheme. It is shown that using fixed rate codebooks, the diversity order of the jth data stream, 1 ≤ j ≤ M, is equal to N(M - j + 1)(K - j + 1), where M, N, and K indicate the number of transmit antennas, the number of receive antennas, and the number of users, respectively. Furthermore, it is proven that the throughput of this scheme scales as M log log(K) and asymptotically (K → ∞) tends to the sum--capacity of the multiple-input multiple-output (MIMO) broadcast channel. The simulation results indicate that the achieved sum--rate is close to the sum--capacity of the underlying broadcast channel. [ABSTRACT FROM AUTHOR]

Capacity improvement from transmitter and receiver cooperation is investigated in a two-transmitter0 two-receiver network with phase fading and full channel state information (CSI) available at all terminals. The transmitters cooperate by first exchanging messages over an orthogonal transmitter cooperation channel, then encoding jointly with dirty-paper coding. The receivers cooperate by using Wyner-Ziv compress-and-forward over an analogous orthogonal receiver cooperation channel. To account for the cost of cooperation, the allocation of network power and bandwidth among the data and cooperation channels is studied. It is shown that transmitter cooperation outperforms receiver cooperation and improves capacity over noncooperative transmission under most opt, rating conditions when the cooperation channel is strong. However, a weak cooperation channel limits the transmitter cooperation rate; in this case, receiver cooperation is more advantageous. Transmitter-and-receiver cooperation offers sizable additional capacity gain over transmitter-only cooperation at low signal-to-noise ratio (SNR), whereas at high SNR transmitter cooperation alone captures most of the cooperative capacity improvement. [ABSTRACT FROM AUTHOR]