Multiple Access Channel Research Articles

Non-Orthogonal Multiple Access with One-Bit Analog-to-Digital Converters Using Threshold Adaptation.

In digital communication systems featuring high-resolution analog-to-digital converters (ADCs), the utilization of successive interference cancellation and detection can enhance the capacity of a Gaussian multiple access channel (MAC) by combining signals from multiple transmitters in a non-orthogonal manner. Conversely, in systems employing one-bit ADCs, it is exceedingly difficult to eliminate non-orthogonal interference using digital signal processing due to the considerable distortion present in the received signal when employing such ADCs. As a result, the Gaussian MAC does not yield significant capacity gains in such cases. To address this issue, we demonstrate that, under a given deterministic interference, the capacity of a one-bit-quantized channel becomes equivalent to the capacity without interference when an appropriate threshold value is chosen. This finding suggests the potential for indirect interference cancellation in the analog domain, facilitating the proposition of an efficient successive interference cancellation and detection scheme. We analyze the achievable rate of the proposed scheme by deriving the mutual information between the transmitted and received signals at each detection stage. The obtained results indicate that the sum rate of the proposed scheme generally outperforms conventional methods, with the achievable upper bound being twice as high as that of the conventional methods. Additionally, we have developed an optimal transmit power allocation algorithm to maximize the sum rate in fading channels.

Over-the-Air Federated Multi-Task Learning Over MIMO Multiple Access Channels

With the explosive growth of data and wireless devices, federated learning (FL) over wireless medium has emerged as a promising technology for large-scale distributed intelligent systems. Yet, the urgent demand for ubiquitous intelligence will generate a large number of concurrent FL tasks, which may seriously aggravate the scarcity of communication resources. By exploiting the analog superposition of electromagnetic waves, over-the-air computation (AirComp) is an appealing solution to alleviate the burden of communication required by FL. However, sharing frequency-time resources in over-the-air computation inevitably brings about the problem of inter-task interference, which poses a new challenge that needs to be appropriately addressed. In this paper, we study over-the-air federated multi-task learning (OA-FMTL) over the multiple-input multiple-output (MIMO) multiple access (MAC) channel. We propose a novel model aggregation method for the alignment of local gradients of different devices, which alleviates the straggler problem in over-the-air computation due to the channel heterogeneity. We establish a communication-learning analysis framework for the proposed OA-FMTL scheme by considering the spatial correlation between devices, and formulate an optimization problem for the design of transceiver beamforming and device selection. To solve this problem, we develop an algorithm by using alternating optimization (AO) and fractional programming (FP), which effectively mitigates the impact of inter-task interference on the FL learning performance. We show that due to the use of the new model aggregation method, device selection is no longer essential, thereby avoiding the heavy computational burden involved in selecting active devices. Numerical results demonstrate the validity of the analysis and the superb performance of the proposed scheme.

Efficient Massive Machine Type Communication (mMTC) via AMP

We propose efficient and low-complexity multiuser detection (MUD) algorithms for Gaussian multiple access channel (G-MAC) for short-packet transmission in massive machine type communications. To do so, we first formulate the G-MAC MUD problem as a sparse signal recovery problem and obtain the exact and approximate joint prior distribution of the sparse vector to be recovered. Then, we employ the Bayesian approximate message passing (AMP) algorithms with the optimal separable and nonseparable minimum mean squared error (MMSE) denoisers for soft decoding of the sparse vector. The effectiveness of the proposed MUD algorithms for a large number of devices is supported by simulation results. For packets of 8 information bits, while the state-of-the-art AMP with soft-threshold denoising achieves 8/100 of the upper bound at Eb/N0 = 4 dB, the proposed algorithms reach 4/7 and 1/2 of the upper bound.

The road to achieve the capacity of the three-way degraded broadcast channel

In this paper, we consider the discrete memory-less three-way degraded broadcast channel (3WDBC). The main contribution of this paper is in characterizing the capacity region of the discrete memoryless 3WDBC. To end up with this goal, we first develop the achievable region. Then, an outer bound to the capacity region is also derived. Next, the achievable region is shown to meet the outer bound such that the capacity is achieved. After that, the 3WDBC is shown to encompass many well-known multi-user networks such as (i) broadcast channel, (ii) multiple access channel, (iii) two-way channel, and (iv) relay channel. Further, the achievable capacity region is then extended to the additive Gaussian noise channel. Specifically, superposition encoding is employed at each user such that a given user can appropriately allocate its power to broadcast to the other two users. In this direction, two design criteria are theoretically presented and numerically investigated to show the range that the power allocation factor at each user may have.

Effects of relay (source) side information on the coverage region in wireless Rayleigh and Rician fading multiple access relay channel

In this paper, we analyze the side information (SI) effect on the coverage region of the wireless multiple-access relay channel (MARC) in two different communications scenarios: SI only at one source and SI only at the relay. Aiming at this work, (I) a general achievable rate region is proved for wireless MARC with SI at one source and SI at the relay in two fading models (Rayleigh model, which has been widely studied in the literature; and Rician model the rate region of which for MARC has not been obtained before); (II) This obtained region, while including previous regions as its special cases, (a) shows the advantage of the SI known at the relay compared to the SI known at one encoder in removing the SI interference at the receiver (as seen in Theorem 2.2, Remark 2.1), and on the related coverage regions, too; and (b) can be exploited to prove corresponding coverage regions; (III) Then, the SI effect on the coverage region is scrutinized in different situations (SI only at one source and SI only at the relay), and also the differences between two fading models (Rayleigh and Rician) are investigated. (IV) Finally, theoretical findings are evaluated numerically.

Lossy State Communication over Fading Multiple Access Channels.

Joint communications and sensing functionalities integrated into the same communication network have become increasingly relevant due to the large bandwidth requirements of next-generation wireless communication systems and the impending spectral shortage. While there exist system-level guidelines and waveform design specifications for such systems, an information-theoretic analysis of the absolute performance capabilities of joint sensing and communication systems that take into account practical limitations such as fading has not been addressed in the literature. Motivated by this, we undertake a network information-theoretic analysis of a typical joint communications and sensing system in this paper. Towards this end, we consider a state-dependent fading Gaussian multiple access channel (GMAC) setup with an additive state. The state process is assumed to be independent and identically distributed (i.i.d.) Gaussian, and non-causally available to all the transmitting nodes. The fading gains on the respective links are assumed to be stationary and ergodic and available only at the receiver. In this setting, with no knowledge of fading gains at the transmitters, we are interested in joint message communication and estimation of the state at the receiver to meet a target distortion in the mean-squared error sense. Our main contribution here is a complete characterization of the distortion-rate trade-off region between the communication rates and the state estimation distortion for a two-sender GMAC. Our results show that the optimal strategy is based on static power allocation and involves uncoded transmissions to amplify the state, along with the superposition of the digital message streams using appropriate Gaussian codebooks and dirty paper coding (DPC). This acts as a design directive for realistic systems using joint sensing and transmission in next-generation wireless standards and points to the relative benefits of uncoded communications and joint source-channel coding in such systems.

Space-Time- and Frequency- Spreading for Interference Minimization in Dense IoT

In this article, we propose a space spreading-assisted framework that leverages either {time or frequency diversity or both to reduce interference and signal loss} owing to channel impairments and facilitate the efficient operation of large-scale dense Internet-of-Things (IoT). Our approach employs dispersion of data-streams transmitted from individual IoT devices over indexed space-time (ST), space-frequency (SF) or space-time-frequency (STF) blocks. As a result, no two devices transmit on the same block; only one is activated while the rest of the devices in the network is silent, thereby minimizing possibility of interference on the transmit side. On the receive side, multiple-antenna array ameliorates performance in presence of channel impairments while exploiting array-processing gain. As interference due to superposition of multiple data-streams is killed at its root, no extra energy is wasted in fighting interference and other impairments, thereby enabling energy-efficient transmission from multiple devices over multiple access channel (MAC). To validate the proposed concept, we simulate the performance of the framework against dense IoT networks deployed in generalized indoor and outdoor scenarios in terms of probability of signal outage. Results demonstrate that our conceptualized framework benefits from interference-free transmission as well as enhancement in overall system performance.

Age of Processed Information Minimization Over Fading Multiple Access Channels

In many real-time applications, timely accessibility to actionable insights is an important requirement. We consider users equipped with sensors and actuators, where the sensors generate updates which need to be processed for driving the actuators. The processing task can be carried out locally or at an edge server located at a common base station (BS). The users access the BS via a fading multiple access channel (MAC) using a non-orthogonal multiple access technique. To measure the timeliness of processed information, we adopt a metric called age of processed information (AoPI), defined as the time elapsed since the generation of the last successfully processed packet available to the user. In this setting, we formulate an average AoPI minimization problem, when users are subjected to average power constraints. The BS has to decide when the users should generate status updates and whether to process them locally or at the edge server. We cast the problem as a constrained Markov decision process (CMDP) and obtain its solution via Lagrangian relaxation. We then obtain a simpler policy via Lyapunov optimization and bound its performance. Via numerical simulations, we finally illustrate properties of the CMDP solution and study behavior of average AoPIs under different policies when problem parameters are varied.

Compute-and-forward transmission scheme in cell-free massive MIMO systems

Cell-free massive multi-input-multi-output (MIMO) systems comprise a large number of distributed access points (APs) to serve a small number of user equipments (UEs). In this paper, compute-and-forward (CF) is investigated for uplink in cell-free massive MIMO. We propose a novel reverse CF precoding scheme for downlink transmission for cell-free massive MIMO. The APs send the linear equations of messages over a multiple access channel, and the UEs recover messages without interferences. The max–min power allocation is investigated to provide uniformly good service to all UEs. Numeral results demonstrate the performance improvement provided by the proposed reverse CF precoder against zero-forcing, conjugate beamforming, and linear minimum mean-square error precoder.

Rate-Splitting Multiple Access for Short-Packet Uplink Communications: A Finite Blocklength Analysis

In this letter, we investigate the performance of Rate-Splitting Multiple Access (RSMA) for an uplink communication system with finite blocklegnth (FBL). Considering a two-user Single-Input Single-Output (SISO) Multiple Access Channel (MAC), we study the impact of blocklength and target rate on the throughput and error probability performance of RSMA where one user message is split. We demonstrate that RSMA can outperform Non-Orthogonal Multiple Access (NOMA) in terms of throughput and error probability performance.

Joint Design of Polar Coding and Physical Network Coding for Two-User Downlink Non-Orthogonal Multiple Access.

In this paper, we propose a joint polar coding and physical network coding (PNC) for two-user downlink non-orthogonal multiple access (PN-DNOMA) channels, since the successive-interference-cancellation-aided polar decoding cannot be optimal for finite blocklength transmissions. In the proposed scheme, we first constructed the XORed message of two user messages. Then, the XORed message was superimposed with the message of the weak User 2 for broadcast. By doing so, we can utilize the PNC mapping rule and polar decoding to directly recover the message of User 1, while at User 2, we equivalently constructed a long-length polar decoder to obtain its user message. The channel polarization and decoding performance can be greatly improved for both users. Moreover, we optimized the power allocation of the two users with their channel conditions by considering the user fairness and the performance. The simulation results showed that the proposed PN-DNOMA can achieve performance gains of about 0.4-0.7 dB over the conventional schemes in two-user downlink NOMA systems.

MIMO Over-the-Air Computation for Distributed Estimation

MIMO over-the-air computation (MIMO-AirComp) is a recently proposed technique that leverages the superposition property of the multiple access channel to compute the target multifunction of various applications. This article presents how the MIMO-AirComp principle can be applied to the state estimation problem using distributed sensing data. The representative target function is explicitly formulated as a nomographic function matched to the structure of the multiple access channel with the proper processing function. The proposed framework efficiently computes the target multifunction by coordinating local preprocessing at each node, aggregation through the wireless channel, and postprocessing at the fusion center. We analyze and demonstrate that the proposed approach significantly improves the computation throughput for the distributed estimation application. Specifically, the proposed MIMO-AirComp framework outperforms the conventional separated communication and computation approach when the network system relies on noisy measurements obtained by the densely deployed sensors.

Secure Transmission Over Multiple Access Wiretap Channel by Cross-Time Interference Injection

Due to the openness of wireless communication, information transmitted in the multiple access channel is vulnerable to eavesdropping by illegitimate users. Classic artificial noise-assisted schemes can improve security but are not suitable for power-constrained users. To address this problem, we propose a cross-time interference injection scheme in this paper, which improves security by introducing cross-time self-interference and inter-user interference, and does not require artificial noise. The proposed scheme exploits the properties of the Hadamard matrix and the wireless channels, so that the eavesdropping channel suffers from stronger interference than the legitimate channel. We analyze the security performance when both the legitimate receiver and the eavesdropper exploit the successive interference cancellation (SIC) scheme, including the secrecy sum rate, the secrecy capacity, and the secrecy outage probability. We further investigate the effect of the number of eavesdropper’s antennas, the number of legitimate users, and the number of time slots used by the scheme on the security performance, and obtain the corresponding relationship between these parameters when achieving a positive secrecy rate. Finally, simulation results corroborate our theoretical analysis.

Coded Over-the-Air Computation for Model Aggregation in Federated Learning

This letter introduces a new coded transmission design for model aggregation in federated learning (FL) over Gaussian multiple access channels (MAC), named coded over-the-air computation (codedAirComp). It enjoys the optimality of analog AirComp-based uncoded transmission for fast model aggregation, but also leverages the traditional source-channel separation principle for more practical uses. Specifically, the proposed codedAirComp employs stochastic uniform quantization for local gradient compression and nested lattice coding for channel transmission. Compared with the traditional coding scheme, the proposed scheme significantly reduces the model aggregation distortion and improves the overall learning accuracy.

A Survey on Over-the-Air Computation

Communication and computation are often viewed as separate tasks. This approach is very effective from the perspective of engineering as isolated optimizations can be performed. However, for many computation-oriented applications, the main interest is a function of the local information at the devices, rather than the local information itself. In such scenarios, information theoretical results show that harnessing the interference in a multiple access channel for computation, i.e., over-the-air computation (OAC), can provide a significantly higher achievable computation rate than separating communication and computation tasks. Moreover, the gap between OAC and separation in terms of computation rate increases with more participating nodes. Given this motivation, in this study, we provide a comprehensive survey on practical OAC methods. After outlining fundamentals related to OAC, we discuss the available OAC schemes with their pros and cons. We provide an overview of the enabling mechanisms for achieving reliable computation in the wireless channel. Finally, we summarize the potential applications of OAC and point out some future directions.

On the Convex Hull of the Achievable Capacity Region of the Two User FDM OMA Downlink

In multiple access channel systems, such as a mobile communication network, it is important to determine how to share the available resources (for example bandwidth and power) among the users. In recent years, one of the promising scheme is Non-Orthogonal Multiple Access (NOMA), where, unlike the traditional Orthogonal Multiple Access, OMA solution, signals for the different users overlap in some domain (power-domain NOMA, code-domain NOMA, etc). In order to evaluate the performance of any NOMA scheme, we need to compare the achievable bit rates of the users (the capacity region) to an OMA case with comparable parameters (for example, same total bandwidth, same total power and same channel conditions, etc). To make this comparison, we first need to know the capacity region for the OMA cases. Many papers make such comparison without detailing the derivation of the capacity region of the OMA case they compare to [1], [2], [3], [4]. In some cases, we have only one free parameter to choose (for example in uplink frequency division multiplexing systems for two users, it is the bandwidth ratio between the users), and the achievable capacity can be directly calculated for both users depending on the single parameter (hence the boundary of the capacity region is trivial). In other cases, such as downlink frequency division multiplexing systems, even for only two users, we have to allocate optimally two resources between the users: the bandwidth and the base station’s available power. Hence, it is far from being trivial to determine which combination is better and where the boundary of the capacity region is. In this paper, we provide a derivation for that case.

Twin Delayed DDPG based Dynamic Power Allocation for Mobility in IoRT

The internet of robotic things (IoRT) is a modern as well as fast-evolving technology employed in abundant socio-economical aspects which connect user equipment (UE) for communication and data transfer among each other. For ensuring the quality of service (QoS) in IoRT applications, radio resources, for example, transmitting power allocation (PA), interference management, throughput maximization etc., should be efficiently employed and allocated among UE. Traditionally, resource allocation has been formulated using optimization problems, which are then solved using mathematical computer techniques. However, those optimization problems are generally nonconvex as well as nondeterministic polynomial-time hardness (NP-hard). In this paper, one of the most crucial challenges in radio resource management is the emitting power of an antenna called PA, considering that the interfering multiple access channel (IMAC) has been considered. In addition, UE has a natural movement behavior that directly impacts the channel condition between remote radio head (RRH) and UE. Additionally, we have considered two well-known UE mobility models i) random walk and ii) modified Gauss-Markov (GM). As a result, the simulation environment is more realistic and complex. A data-driven as well as model-free continuous action based deep reinforcement learning algorithm called twin delayed deep deterministic policy gradient (TD3) has been proposed that is the combination of policy gradient, actor-critics, as well as double deep Q-learning (DDQL). It optimizes the PA for i) stationary UE, ii) the UE movements according to random walk model, and ii) the UE movement based on the modified GM model. Simulation results show that the proposed TD3 method outperforms model-based techniques like weighted MMSE (WMMSE) and fractional programming (FP) as well as model-free algorithms, for example, deep Q network (DQN) and DDPG in terms of average sum-rate performance.

Subgradient Descent Learning Over Fading Multiple Access Channels With Over-the-Air Computation

Subgradient Descent Learning Over Fading Multiple Access Channels With Over-the-Air Computation

Intelligent Over-the-Air Computing Environment

A key service of the sixth generation (6G) of wireless networks is envisioned to be native Artificial Intelligence, which calls for radical changes to the way the nodes communicate and perform computations, as well as the role of wireless environment. For this purpose, over-the-air computing (AirComp) is a promising technique for ultra low-latency wireless data aggregation, enabled by the waveform superposition properties of a multiple access channel. In this letter, the synergy of decentralized AirComp, reconfigurable intelligent surfaces (RISs) and machine learning is proposed, to transform the wireless environment to intelligent AirComp environment (IACE), i.e., with inherent and advanced capabilities to perform computations in a fully decentralized way at the physical layer. Specifically, we minimize the AirComp error, i.e., the average mean-square errors of devices with respect to a target function, by jointly optimizing the RIS phase-shift vector and the transmission and reception scaling factors of devices. Also, to solve this challenging problem, we propose an online deep neural network (DNN) optimization approach. Finally, simulation results validate the effectiveness of IACE and the proposed DNN approach.

Link Analysis for Solving Multiple-Access MDPs With Large State Spaces

Wireless communication networks can be well-modeled by Markov Decision Processes (MDPs). While traditional dynamic programming algorithms such as value and policy iteration have lower complexity than brute force strategies, they still suffer from complexity issues for large state spaces. In this paper, the development of moderate complexity algorithms with high performance is sought for wireless network control. To this end, the approximate value function can be computed by projecting the original value function into a lower-dimensional subspace with the careful choice of basis vectors using tools from graph signal processing (GSP). Although GSP theory mainly focuses on undirected graphs, the transition graphs of MDPs for wireless networks are generally directed. For this reason, graph symmetrization based on the co-link method is considered due to its ability to preserve multi-hop dependencies in the graph. Given the multiple-access model under consideration, key properties of the transition probability matrix are exploited to determine the optimal subspace for the approximate value function with low complexity. The numerical results for a multiple-access channel show that the proposed algorithm can find the optimal basis vectors with high accuracy, and furthermore, the approach is robust to changes in system parameters. It is also shown that the projected equation method outperforms the state aggregation technique in producing higher accuracy with a lower runtime complexity.