Successive Decoding Strategy Research Articles

Achievable rate approximation for massive MIMO with limited number of interfering clients

Massive MIMO has become a core technology for the next generation of wireless communications, and the non-linear group decoding schemes can achieve better spectral and energy efficiency, which in turn leads to higher data rates, especially with the assistance of non-orthogonal frequency multiple access (NOMA). The purpose of this paper is to study the problems of formulating performance of such systems. In particular, this paper, closely approximates the achievable rate of large-scale MIMO systems occupied by clients (users) using the optimal successive group decoding (OSGD) scheme to mitigate the co-channel interference. In addition, we produce a simple mathematical representation to accurately approximate the outage probability of this system. We also formulate the effective capacity of the considered system in a simple way. The results obtained by the Monte Carlo simulation show the validity of the derived formulas. The findings of this paper show that increasing the number of antennas linearly increases the expected value of the achievable rate and decreases its variance. In addition, the systems capacity is increasing in a logarithmic manner with the allocated power. Also, we notice that the distribution of the random achievable rate tends to be closely approximated with log-normal distribution as the number of antennas increases.

Opportunistic Treating Interference as Noise

We consider a $K$-user interference network with $M$ states, where each transmitter has $M$ messages and over State $m$, Receiver $k$ wishes to decode the first $\pi_k(m) \in \{1,2,\cdots,M\}$ messages from its desired transmitter. This problem of channel with states models opportunistic communications, where more messages are sent for better channel states. The first message from each transmitter has the highest priority as it is required to be decoded regardless of the state of the receiver; the second message is opportunistically decoded if the state allows a receiver to decode 2 messages; and the $M$-th message has the lowest priority as it is decoded if and only if the receiver wishes to decode all $M$ messages. For this interference network with states, we show that if any possible combination of the channel states satisfies a condition under which power control and treating interference as noise (TIN) are sufficient to achieve the entire generalized degrees of freedom (GDoF) region of this channel state by itself, then a simple layered superposition encoding scheme with power control and a successive decoding scheme with TIN achieves the entire GDoF region of the network with $M$ states for all $KM$ messages.

A Multi-Layer Encoding and Decoding Strategy for Binary Erasure Channel

In this paper, we consider a binary erasure channel (BEC) with an unknown erasure probability of ${\delta }$ at a transmitter. In addition, we consider that ${\delta }$ has a constant value in each transmission block and the transmitter knows the probability distribution of ${\delta }$ . For this problem with the infinite block length, based on the distribution of the random variable ${\delta }$ , a multi-layer encoding strategy at the transmitter and a successive decoding strategy at the receiver are proposed. In our proposed scheme, based on the value of ${\delta }$ , the receiver can decode a part of transmitted data from different transmission layers. In order to have a comparison with other erasure correction codes, we generalize the result of the infinite block length to the finite block-length transmission. We show that considering the average transmission rate, our scheme has a better performance than the other erasure correction codes such as the Raptor and the MDS codes. We also analyze both the decoding complexity and bit error rate versus the erasure probability of the channel. Besides, by using a numerical comparison, we try to show that our proposed strategy outperforms other transmission strategies.

Binary Data Gathering With a Helper in Internet of Things: Distortion Analysis and Performance Evaluation

This paper focuses on one-helper assisted binary data gathering networks, for example, such as in Internet of Things, where a destination makes estimates of binary data relying on a number of agents and one helper. Due to the noise, corrupting errors already exist in the agent observations. To analyze the performance of this system, we formulate this system as a binary chief executive officer (CEO) problem with a helper. Initially, we use a successive decoding scheme to decompose the binary CEO problem with a helper into the multiterminal source coding and final decision problems. Then, we present an outer bound on the rate-distortion region for multiterminal source coding with binary sources and a helper. After solving a convex optimization problem formulated from the derived outer bound, we obtain the final distortion by substituting the minimized distortions of observation into the distortion propagating function, which is derived to bridge the relationship between the joint decoding results and final decision. Finally, we analyze the trade-off of rate-distortion through theoretical calculation and simulations. Both the theoretical and simulation results demonstrate that a helper can obviously reduce the signal-to-noise ratio threshold. We also have an in-depth discussion on the differences of system performance improvement between locating a helper and including an additional agent.

On the Optimal Fronthaul Compression and Decoding Strategies for Uplink Cloud Radio Access Networks

This paper investigates the compress-and-forward scheme for an uplink cloud radio access network (C-RAN) model, where multi-antenna base-stations (BSs) are connected to a cloud-computing based central processor (CP) via capacity-limited fronthaul links. The BSs compress the received signals with Wyner-Ziv coding and send the representation bits to the CP; the CP performs the decoding of all the users' messages. Under this setup, this paper makes progress toward the optimal structure of the fronthaul compression and CP decoding strategies for the compress-and-forward scheme in C-RAN. On the CP decoding strategy design, this paper shows that under a sum fronthaul capacity constraint, a generalized successive decoding strategy of the quantization and user message codewords that allows arbitrary interleaved order at the CP achieves the same rate region as the optimal joint decoding. Further, it is shown that a practical strategy of successively decoding the quantization codewords first, then the user messages, achieves the same maximum sum rate as joint decoding under individual fronthaul constraints. On the joint optimization of user transmission and BS quantization strategies, this paper shows that if the input distributions are assumed to be Gaussian, then under joint decoding, the optimal quantization scheme for maximizing the achievable rate region is Gaussian. Moreover, Gaussian input and Gaussian quantization with joint decoding achieve to within a constant gap of the capacity region of the Gaussian multiple-input multiple-output (MIMO) uplink C-RAN model. Finally, this paper addresses the computational aspect of optimizing uplink MIMO C-RAN by showing that under fixed Gaussian input, the sum rate maximization problem over the Gaussian quantization noise covariance matrices can be formulated as convex optimization problems, thereby facilitating its efficient solution.

Game-theoretic resource allocation and decoding order control in OFDMA based multihop networks

It is well known that the information theoretic multiple access channel capacity region is known to be achieved by successive decoding schemes. In this paper, we study a distributed multiple access scheme in OFDMA-based wireless multihop networks. As a solution, we propose a game-theoretic resource allocation and decoding order control scheme, which is based on iterative multi-unit second price auction. The key concept of the proposed scheme is to update the auction value based on the difference between current and target bidding efficiency. We prove our proposal's convergence and stability through analysis and show its performance using simulation. It achieves around 80% of the full-search-based sum throughput while providing inter-user and inter-subchannel fairness.

On an Achievable Rate of Large Rayleigh Block-Fading MIMO Channels With No CSI

Training-based transmission over Rayleigh block-fading multiple-input multiple-output (MIMO) channels is investigated. As a training method a combination of a pilot-assisted scheme and a biased signaling scheme is considered. The achievable rates of successive decoding (SD) receivers based on the linear minimum mean-squared error (LMMSE) channel estimation are analyzed in the large-system limit, by using the replica method under the assumption of replica symmetry. It is shown that negligible pilot information is best in terms of the achievable rates of the SD receivers in the large-system limit. The obtained analytical formulas of the achievable rates can improve the existing lower bound on the capacity of the MIMO channel with no channel state information (CSI), derived by Hassibi and Hochwald, for all signal-to-noise ratios (SNRs). The comparison between the obtained bound and a high SNR approximation of the channel capacity, derived by Zheng and Tse, implies that the high SNR approximation is unreliable unless quite high SNR is considered. Energy efficiency in the low SNR regime is also investigated in terms of the power per information bit required for reliable communication. The required minimum power is shown to be achieved at a positive rate for the SD receiver with no CSI, whereas it is achieved in the zero-rate limit for the case of perfect CSI available at the receiver. Moreover, numerical simulations imply that the presented large-system analysis can provide a good approximation for not so large systems. The results in this paper imply that SD schemes can provide a significant performance gain in the low-to-moderate SNR regimes, compared to conventional receivers based on one-shot channel estimation.

Classical Communication Over a Quantum Interference Channel

Calculating the capacity of interference channels is a notorious open problem in classical information theory. Such channels have two senders and two receivers, and each sender would like to communicate with a partner receiver. The capacity of such channels is known exactly in the settings of "very strong" and "strong" interference, while the Han-Kobayashi coding strategy gives the best known achievable rate region in the general case. Here, we introduce and study the quantum interference channel, a natural generalization of the interference channel to the setting of quantum information theory. We restrict ourselves for the most part to channels with two classical inputs and two quantum outputs in order to simplify the presentation of our results (though generalizations of our results to channels with quantum inputs are straightforward). We are able to determine the exact classical capacity of this channel in the settings of "very strong" and "strong" interference, by exploiting Winter's successive decoding strategy and a novel two-sender quantum simultaneous decoder, respectively. We provide a proof that a Han-Kobayashi strategy is achievable with Holevo information rates, up to a conjecture regarding the existence of a three-sender quantum simultaneous decoder. This conjecture holds for a special class of quantum multiple access channels with average output states that commute, and we discuss some other variations of the conjecture that hold. Finally, we detail a connection between the quantum interference channel and prior work on the capacity of bipartite unitary gates.

Fading Cognitive Multiple Access Channels: Outage Capacity Regions and Optimal Power Allocation

This paper considers a spectrum sharing based cognitive radio network where a M-user fading multiple access network shares the same spectrum with an existing primary network. The primary network's transmission is assumed to be protected by the interference power constraint. Under this interference power constraint together with the individual transmit power constraint of each user, the outage capacity regions for the fading cognitive multiple access channel (C-MAC) are defined for two different scenarios, i.e., an outage must be declared simultaneously for all users (common outage) and outages are declared individually for each user (individual outage). Then, optimal power allocation strategies to achieve the boundary points of these outage capacity regions are derived by considering their equivalent problems, i.e., the common/individual usage probability maximization for given rate vectors. It is rigorously proved that the optimal decoding strategy is the successive decoding strategy, and the decoding order is determined by the dual variables and the channel power gains of the involved channels. Then, a modified ellipsoid method is proposed to obtain the optimal dual variables. Finally, several numerical examples are given to validate the proposed studies.

Polar Codes are Optimal for Lossy Source Coding

We consider lossy source compression of a binary symmetric source using polar codes and a low-complexity successive encoding algorithm. It was recently shown by Arikan that polar codes achieve the capacity of arbitrary symmetric binary-input discrete memoryless channels under a successive decoding strategy. We show the equivalent result for lossy source compression, i.e., we show that this combination achieves the rate-distortion bound for a binary symmetric source. We further show the optimality of polar codes for various multiterminal problems including the binary Wyner-Ziv and the binary Gelfand-Pinsker problems. Our results extend to general versions of these problems.

Design and Analysis of Successive Decoding With Finite Levels for the Markov Channel

This paper proposes a practical successive decoding scheme with finite levels for the finite-state Markov channels (FSMCs) where there is no a priori state information at the transmitter or the receiver. The design employs either a random interleaver or a deterministic interleaver with an irregular pattern and an optional iterative estimation and decoding procedure within each level. The interleaver design criteria may be the achievable rate or the extrinsic information transfer (EXIT) chart, depending on the receiver type. For random interleavers, the optimization problem is solved efficiently using a pilot-utility function, while for deterministic interleavers, a good construction is given using empirical rules. Simulation results demonstrate that the new successive decoding scheme combined with irregular low-density parity-check (LDPC) codes can approach the identically and uniformly distributed (i.u.d.) input capacity on the Markov-fading channel using only a few levels.

Outage Capacities and Optimal Power Allocation for Fading Multiple-Access Channels

We derive the outage capacity region of an M-user fading multiple-access channel (MAC) under the assumption that both the transmitters and the receiver have perfect channel side information (CSI). The outage capacity region is implicitly obtained by deriving the outage probability region for a given rate vector. Given a required rate and average power constraint for each user, we find a successive decoding strategy and a power allocation policy that achieves points on the boundary of the outage probability region. We discuss the scenario where an outage must be declared simultaneously for all users (common outage) and when outages can be declared individually (individual outage) for each user.

Lower bounds on reliability functions of variable-length nonsystematic convolutional codes for channels with noiseless feedback

A variable-length, nonsystematic, convolutional encoding, and successive-decoding scheme is devised to establish significant improvements in the reliability functions of memoryless channels with noiseless decision feedback. It is shown that, for any but pathological discrete memoryless channels with noiseless feedback, there exists a variable-length convolutional code such that the reliability function of the channel can be bounded below by the channel capacity <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</tex> for all transmission rates less than <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</tex> . By employing a modified version of this scheme, it is also constructively shown that, for an additive-white-Gaussian-noise (AWGN) channel with noiseless feedback it is possible to find a variable-length convolutional code such that the channel reliability function can be bounded below by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">\alpha_0 c_{infty}</tex> for all rates less than the channel capacity <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C_{\infty}</tex> , where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">\alpha_0 = max (1, \gamma/2)</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">\gamma</tex> is the maximum allowable expected-peak-to-expected-average-power ratio at the transmitter.

Successive decoding scheme for memoryless channels

In this paper a new decoding scheme for random convolutional codes is described. This scheme is different from other effective decoding schemes, such as sequential decoding [1] and low-density parity check codes [2]. The new scheme yields (for a certain region of information rates) an upper bound on the average number of computations which is {\em independent} of the coding constraint length. Furthermore, unlike sequential decoding, a bound on the total number of computations (rather than just on the "incorrect subset") is derived in this paper.