Capacity Of Multiple Access Channel Research Articles

Cooperative Multiple-Access Channels With Distributed State Information

This paper studies a memoryless state-dependent multiple access channel (MAC) where two transmitters wish to convey a message to a receiver under the assumption of causal and imperfect channel state information at transmitters (CSIT) and imperfect channel state information at receiver (CSIR). In order to emphasize the limitation of transmitter cooperation between physically distributed nodes, we focus on the so-called distributed CSIT assumption, i.e., where each transmitter has its individual channel knowledge, while the message can be assumed to be partially or entirely shared a priori between transmitters by exploiting some on-board memory. Under this setup, the first part of the paper characterizes the common message capacity of the channel at hand for arbitrary CSIT and CSIR structure. The optimal scheme builds on Shannon strategies, i.e., optimal codes are constructed by letting the channel inputs be a function of current CSIT only. For a special case when CSIT is a deterministic function of CSIR, the considered scheme also achieves the capacity region of a common message and two private messages. The second part addresses an important instance of the previous general result in a context of a cooperative multi-antenna Gaussian channel under i.i.d. fading operating in frequency-division duplex mode, such that CSIT is acquired via an explicit feedback of perfect CSIR. The capacity of the channel at hand is achieved by distributed linear precoding applied to Gaussian codes. Surprisingly, we demonstrate that it is suboptimal to send a number of data streams bounded by the number of transmit antennas as typically considered in a centralized CSIT setup. Finally, numerical examples are provided to evaluate the sum capacity of the binary MAC with binary states as well as the Gaussian MAC with i.i.d. fading.

Playing games with multiple access channels

Communication networks have multiple users, each sending and receiving messages. A multiple access channel (MAC) models multiple senders transmitting to a single receiver, such as the uplink from many mobile phones to a single base station. The optimal performance of a MAC is quantified by a capacity region of simultaneously achievable communication rates. We study the two-sender classical MAC, the simplest and best-understood network, and find a surprising richness in both a classical and quantum context. First, we find that quantum entanglement shared between senders can substantially boost the capacity of a classical MAC. Second, we find that optimal performance of a MAC with bounded-size inputs may require unbounded amounts of entanglement. Third, determining whether a perfect communication rate is achievable using finite-dimensional entanglement is undecidable. Finally, we show that evaluating the capacity region of a two-sender classical MAC is in fact NP-hard.

On Multiple-Access in Queue-Length Sensitive Systems

We consider transmission of packets over queue-length sensitive unreliable links, where packets are randomly corrupted through a noisy channel whose transition probabilities are modulated by the queue-length. The goal is to characterize the capacity of this channel. We particularly consider multipleaccess systems, where transmitters dispatch encoded symbols over a system that is a superposition of continuous-time GIk/GI/1 queues. A server receives and processes symbols in order of arrivals with queuelength dependent noise. We first determine the capacity of single-user queue-length dependent channels. Further, we characterize the best and worst dispatch processes for GI/M/1 queues and the best and worst service processes for M/GI/1 queues. Then, the multiple-access channel capacity is obtained using point processes. When the number of transmitters is large and each arrival process is sparse, the superposition of arrivals approaches a Poisson point process. In characterizing the Poisson approximation, we show that the capacity of the multiple-access system converges to that of a single-user M/GI/1 queue-length dependent system, and an upper bound on the convergence rate is obtained. This implies that the best and worst server behaviors of single-user M/GI/1 queues are preserved in the sparse multiple-access case.

Near-Capacity Detection and Decoding: Code Design for Dynamic User Loads in Gaussian Multiple Access Channels

This paper considers the forward error correction (FEC) code design for approaching the capacity of a dynamic multiple access channel (MAC) where both the number of users and their respective signal powers keep constantly changing, resembling the scenario of an actual wireless cellular system. To obtain a low-complexity non-orthogonal multiple access (NOMA) scheme, we propose a serial concatenation of a low-density parity-check (LDPC) code and a repetition code (REP), this way achieving near Gaussian MAC (GMAC) capacity performance while coping with the dynamics of the MAC system. The joint optimization of the LDPC and REP codes is addressed by matching the analytical extrinsic information transfer (EXIT) functions of the sub-optimal multi-user detector (MUD) and the channel code for a specific and static MAC system, achieving near-GMAC capacity. We show that the near-capacity performance can be flexibly maintained with the same LDPC code regardless of the variations in the number of users and power levels. This flexibility (or elasticity) is provided by the REP code, acting as “user-load and power equalizer”, dramatically simplifying the practical implementation of NOMA schemes, as only a single LDPC code is needed to cope with the dynamics of the MAC system.

Compute–Forward Multiple Access (CFMA): Practical Implementations

We present a practical strategy that aims to attain rate points on the dominant face of the multiple access channel capacity using a standard low complexity decoder. This technique is built upon recent theoretical developments of Zhu and Gastpar on compute-forward multiple access which achieves the capacity of the multiple access channel using a sequential decoder. We illustrate this strategy with off-the-shelf LDPC codes. In the first stage of decoding, the receiver first recovers a linear combination of the transmitted codewords using the sum-product algorithm (SPA). In the second stage, by using the recovered sum-of-codewords as side information, the receiver recovers one of the two codewords using a modified SPA, ultimately recovering both codewords. The main benefit of recovering the sum-of-codewords instead of the codeword itself is that it allows to attain points on the dominant face of the multiple access channel capacity without the need of rate-splitting or time sharing while maintaining a low complexity in the order of a standard point-to-point decoder. This property is also shown to be crucial for some applications, e.g., interference channels. For all the simulations with single-layer binary codes, our proposed practical strategy is shown to be within 1.7 dB of the theoretical limits, without explicit optimization on the off-the-self LDPC codes.

Performance Analysis of Lossy Decode-and-Forward for Non-Orthogonal MARCs

Non-orthogonal transmission is considered to be one of the promising techniques for improving the throughput of the existing and future wireless communication networks. We concentrate on the transmission of both independent and correlated binary sources over a non-orthogonal multiple access relay channel (MARC), which consists of two sources, one relay, and one destination. The lossy decode-and-forward (DF), developed from the conventional DF, is adopted at the relay. Two time slots are required with non-orthogonal transmission over such network setup, while three time slots are required with the conventional orthogonal transmission. We analyze the outage probability of transmission of independent binary sources over the non-orthogonal MARC based on the theorem of multiple access channel (MAC) with a helper, which combines the Slepian-Wolf rate region and the MAC capacity region. For the performance verification, we implement a practical coding-decoding chain, which is applicable to the transmission of both independent and correlated binary sources. Exclusive-OR based multi-user complete decoding is introduced at the relay node, and iterative joint decoding is utilized at the destination by taking into consideration the estimated intra-link error probability and correlation information between the sources. The practical simulation results are well matched with the theoretical analyses.

Sum Capacity for Single-Cell Multi-User Systems with M-Ary Inputs

This paper investigates the sum capacity of a single-cell multi-user system under the constraint that the transmitted signal is adopted from M-ary two-dimensional constellation with equal probability for both uplink, i.e., multiple access channel (MAC), and downlink, i.e., broadcast channel (BC) scenarios. Based on the successive interference cancellation (SIC) and the entropy power Gaussian approximation, it is shown that both the multi-user MAC and BC can be approximated to a bank of parallel channels with the channel gains being modified by an extra attenuate factor that equals to the negative exponential of the capacity of interfering users. With this result, the capacity of MAC and BC with arbitrary number of users and arbitrary constellations can be easily calculated which in sharp contrast with using traditional Monte Carlo simulation that the calculating amount increases exponentially with the increase of the number of users. Further, the sum capacity of multi-user under different power allocation strategies including equal power allocation, equal capacity power allocation and maximum capacity power allocation is also investigated. For the equal capacity power allocation, a recursive relation for the solution of power allocation is derived. For the maximum capacity power allocation, the necessary condition for optimal power allocation is obtained and an optimal algorithm for the power allocation optimization problem is proposed based on the necessary condition.

A Low-Complexity Multiuser Coding Scheme With Near-Capacity Performance

Nonorthogonal multiuser coding is an essential technique for superimposed transmission and improving spectrum efficiency of future wireless communication networks. In this paper, a low-complexity nonorthogonal coding scheme, called multiuser repetition-aided irregular repeat-accumulate (IRA) code, is proposed for a multiple access channel (MAC), and the scheme can be applied to fifth-generation (5G) nonorthogonal multiple access systems. The main idea is that not only parity checks, which are generated by an IRA encoder, but repetitions as well, are used in each user's codeword to reduce the coding and decoding complexities. Repetition is a simple way to construct a low-rate code and is shown to be beneficial for multiuser decoding iteration. With a deliberate control of the fraction of repetitions in the codeword and a degree distribution optimization of the IRA encoder, near MAC capacity performance is achieved. It is shown that as the number of users increases, more repetitions can be used, and therefore, very low encoding and decoding complexities are required.

A Communication Theoretical Analysis of Multiple-Access Channel Capacity in Magneto-Inductive Wireless Networks

Magneto-inductive (MI) wireless communications is an emerging subject with a rich set of applications, including local area networks for the Internet-of-Things, wireless body area networks, in-body and on-chip communications, and underwater and underground sensor networks as a low-cost alternative to radio frequency, acoustic or optical methods. Practical MI networks include multiple access channel (MAC) mechanisms for connecting a random number of coils without any specific topology or coil orientation assumptions covering both short and long ranges. However, there is not any information theoretical modeling of MI MAC (MIMAC) capacity of such universal networks with fully coupled frequency selective channel models and exact 3-D coupling model of circular coils instead of long range dipole approximations. In this paper, K-user MIMAC capacity is information theoretically modeled and analyzed, and two-user MIMACs are modeled with explicitly detailed channel responses, bandwidths and coupled thermal noise. K-user MIMAC capacity is achieved through Lagrangian solution with K-user water-filling optimization. Optimum orientations maximizing capacity and received power are theoretically analyzed, and numerically simulated for two-user MIMACs. Constructive gain and destructive interference mechanisms on MIMACs are introduced in comparison with the classical interference based approaches. The theoretical basis promises the utilization of MIMACs in 5G architectures.

An Improved Algorithm for Testing the Membership of Gaussian Multiple-Access Channel Capacity Region

In this letter, we investigate the problem of testing the membership of Gaussian multiple-access channel capacity region (GMACR), which involves $2^{N}-1$ inequalities, where $N$ is the number of senders. Given a fixed rate tuple and positive power constraint, we propose a simple but efficient algorithm to test the membership of the GMACR. It is unveiled that the algorithm can be accomplished in $O(N\log _{2}N)$ operations, which is much more efficient than the approach by Zhang et al. , whose complexity bound is larger than $O(N^{5})$ .

Partially Repeated SC-LDPC Codes for Multiple-Access Channel

A simple partially repeated spatially coupled low-density parity-check (SC-LDPC) code is proposed for a multiple-access channel (MAC) with a large number of users. This code is constructed by selecting part of bits of an SC-LDPC code to be repeated. Since the decoding increment is only a log-likelihood ratio (LLR) combination, compared with the original SC-LDPC code, the decoding complexity scarcely increases. It is shown that this LLR combination is effective to overcome the multi-user interference. By optimizing the ratio of coded bits for repetition, the decoding threshold of the partially repeated SC-LDPC code can be close to the MAC capacity. Experimental results show that the partially repeated SC-LDPC codes have better performance than the graph-extended and parallelly connected SC-LDPC codes over the MAC.

Capacity Bounds for a Class of Diamond Networks With Conferencing Relays

Diamond channels can model modern wireless communication infrastructure that use relays to increase network capacity. This letter investigates the effect of conferencing between the relays in a particular class of diamond networks with source-to-relay and conferencing channels modeled as noiseless bit-pipes of given capacities. A lower bound to the capacity for this network and an upper bound that is tighter than the cut-set bound are presented. Such networks with specific multiple access channel (MAC) types are studied and the minimum conferencing link capacity needed to achieve full cooperation for each MAC is ascertained. It is found that such minimum conferencing link capacity for the Gaussian MAC must be equal to the difference between the full cooperation capacity and the backhaul capacity. However, such a result does not hold in general as the binary adder MAC illustrates a counterexample.

Distributed Rate Adaptation and Power Control in Fading Multiple Access Channels

Traditionally, the capacity region of a coherent fading multiple access channel (MAC) is analyzed in two popular contexts. In the first, a centralized system with full channel state information at the transmitters (CSITs) is assumed, and the transmit power and data-rate can be jointly chosen for every fading vector realization. On the other hand, in fast-fading links with distributed CSIT, the lack of full CSI is compensated by performing ergodic averaging over sufficiently many channel realizations. Notice that the distributed CSI may necessitate decentralized power-control for optimal data-transfer. Apart from these two models, the case of slow-fading links and distributed CSIT, though relevant to many systems, has received much less attention. In this paper, a block-fading additive white Gaussian noise MAC with full CSI at the receiver and distributed CSI at the transmitters is considered. The links undergo independent fading, but otherwise have arbitrary fading distributions. The channel statistics and respective long-term average transmit powers are known to all parties. We first consider the case where each encoder has knowledge only of its own link quality, and not of others. For this model, we compute the adaptive capacity region, i.e., the collection of average rate-tuples under blockwise coding/decoding such that the rate-tuple for every fading realization is inside the instantaneous MAC capacity region. The key step in our solution is an optimal rate allocation function for any given set of distributed power control laws at the transmitters. This also allows us to structurally characterize the optimal power control for a wide class of fading models. Further extensions are also proposed for the case where each encoder has additional partial CSI about the other links.

Weak Secrecy in the Multiway Untrusted Relay Channel With Compute-and-Forward

We investigate the problem of secure communications in a Gaussian multiway relay channel applying the compute-and-forward scheme under usage of nested lattice codes. All nodes employ half-duplex operation and can exchange confidential messages only via an untrusted relay. The relay is assumed to be honest but curious, i.e., an eavesdropper that conforms to the system rules and applies the intended relaying scheme. We start with the general case of the single-input multiple-output L-user multiway relay channel and provide an achievable secrecy rate region under a weak secrecy criterion. We show that the securely achievable sum rate is equivalent to the difference between the computation rate and the multiple access channel (MAC) capacity. In particular, we show that all nodes must encode their messages such that the common computation rate tuple falls outside the MAC capacity region of the relay. We provide results for the single-input single-output and the multiple-input single-input L-user multiway relay channel as well as the two-way relay channel. We discuss these results and show the dependence between channel realization and achievable secrecy rate. We further compare our result to available results in the literature for different schemes and show that the proposed scheme operates close to the compute-and-forward rate without secrecy.

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.

Power allocation for maximizing the MAC capacity via majorization

Optimal power allocation for maximizing the sum capacity of multiple access channel (MAC) with quality-of-service (QoS) constraints is investigated in this paper. Majorization theory is the underlying mathematical theory on which our method hinges. It is shown that the optimal structure of the solution can be easily obtained via majorization theory. Furthermore, based on our new approach, an efficient searching method for power allocation is developed by restricting the attention to a new searching variable. Our new method requires less than half of the computational cost of the existing method in the worst case and is even much faster in general. Simulation results demonstrate the effectiveness of our method.

Finite Field Spreading for Multiple-Access Channel

As a generalization of the binary spreading scheme in conventional direct-sequence code-division multiple-access (DS-CDMA) and interleave-division multiple-access (IDMA), a finite field spreading scheme is proposed for a synchronous multiple-access channel (MAC) with Gaussian noise and equal-power users. For each user, each information symbol over a finite field is spread into a length-L field vector by L-field multiplications. At the receiver, an iterative multi-user decoding algorithm on a factor graph is developed to recover each user's information symbol. To estimate the bit error rate performance of an uncoded finite field spreading system, an extrinsic information transfer analysis of the finite field despreading is given. This analysis shows that in addition to overcoming multi-user interference, the finite field spreading scheme can also provide an additional coding gain to overcome Gaussian noise compared with the conventional spreading scheme. This coding gain increases with the field order. The finite field spreading serially concatenated with a nonbinary low-density parity-check (LDPC) code, with field order 64, approaches the MAC capacity within 0.26 dB at a sum rate of 0.25.

Capacity of Data Collection in Wireless Sensor Networks Based on Mutual Information and MMSE Estimation

We investigate the properties of data collection in wireless sensor networks, in terms of both capacity and power allocation strategy. We consider a scenario in which a number of sensors observe a target being estimated at fusion center (FC) using minimum mean-square error (MMSE) estimator. Based on the relationship between mutual information and MMSE (I-MMSE), the capacity of data collection in coherent and orthogonal multiple access channel (MAC) models is derived. Considering power constraint, the capacity is derived under two scenarios: equal power allocation and optimal power allocation of both models. We provide the upper bound of capacity as a benchmark. In particular, we show that the capacity of data collection scales as Θ((1/2)log(1+L)) when the number of sensors L grows to infinity. We show through simulation results that for both coherent and orthogonal MAC models, the capacity of the optimal power is larger than that of the equal power. We also show that the capacity of coherent MAC is larger than that of orthogonal MAC, particularly when the number of sensors L is large and the total power P is fixed.

Achieving AWGN Multiple Access Channel Capacity with Spatial Graph Coupling

Communication over a multiple access channel is considered. Each user modulates his signal as a superposition of redundant data streams where interconnection of data bits can be represented by means of a sparse graph. The receiver observes a signal resulting from the coupling of the sparse modulation graphs. Iterative interference cancellation decoding is analyzed. It is proved that spatial graph coupling allows to achieve the AWGN channel capacity with equal power and rate transmissions.

Capacity and Power Allocation for Cognitive MAC with Imperfect Channel Estimation

This paper presents a lower-bound on the ergodic sum capacity of single-input single-output (SISO) opportunistic spectrum sharing multiple access channel (MAC) in Rayleigh fading channels with imperfect channel estimates. Unlike the previous studies, this paper considerers the channel estimation errors for both sets of SISO channels between the secondary user (SU) transmitters and the SU receiver and between the SU transmitters and the primary user receivers. By considering these channel estimation errors, an optimal power allocation policy for each SU transmitter is derived to achieve a lower-bound of the ergodic sum capacity of opportunistic spectrum sharing MAC, subject to a set of SUs transmit power constraints and a set of interference power constraints at each PU receiver. Furthermore, convex optimization techniques are exploited to derive an efficient algorithm to obtain the optimal power allocation for each SU transmitter. Numerical results are presented to illustrate the influences of the channel estimation errors on the ergodic sum capacity.