Multiuser Communication Channel Research Articles

Dependent Task Offloading and Resource Allocation via Deep Reinforcement Learning for Extended Reality in Mobile Edge Networks

Extended reality (XR) is an immersive technology widely applied in various fields. Due to the real-time interaction required between users and virtual environments, XR applications are highly sensitive to latency. Furthermore, handling computationally intensive tasks on wireless XR devices leads to energy consumption, which is a critical performance constraint for XR applications. It has been noted that the XR task can be decoupled to several subtasks with mixed serial–parallel relationships. Furthermore, the evaluation of XR application performance involves both subjective assessments from users and objective evaluations, such as of energy consumption. Therefore, in edge computing environments, ways to integrate task offloading for XR subtasks to meet users’ demands for XR applications is a complex and challenging issue. To address this issue, this paper constructs a wireless XR system based on mobile edge computing (MEC) and conducts research on the joint optimization of multi-user communication channel access and task offloading. Specifically, we consider the migration of partitioned XR tasks to MEC servers and formulate a joint optimization problem for communication channel access and task offloading. The objective is to maximize the ratio of quality of experience (QoE) to energy consumption while meeting the user QoE requirements. Subsequently, we introduce a deep reinforcement learning-based algorithm to address this optimization problem. The simulation results demonstrate the effectiveness of this algorithm in meeting user QoE demands and improving energy conversion efficiency, regardless of the XR task partitioning strategies employed.

On the Concatenated Code Constructions for Slotted Multiuser Communication Channel

On the Concatenated Code Constructions for Slotted Multiuser Communication Channel

Infinite Factorial Finite State Machine for Blind Multiuser Channel Estimation

New communication standards need to deal with machine-to-machine communications, in which users may start or stop transmitting at any time in an asynchronous manner. Thus, the number of users is an unknown and time-varying parameter that needs to be accurately estimated in order to properly recover the symbols transmitted by all users in the system. In this paper, we address the problem of joint channel parameter and data estimation in a multiuser communication channel in which the number of transmitters is not known. For that purpose, we develop the infinite factorial finite state machine model, a Bayesian nonparametric model based on the Markov Indian buffet that allows for an unbounded number of transmitters with arbitrary channel length. We propose an inference algorithm that makes use of slice sampling and particle Gibbs with ancestor sampling. Our approach is fully blind as it does not require a prior channel estimation step, prior knowledge of the number of transmitters, or any signaling information. Our experimental results, loosely based on the LTE random access channel, show that the proposed approach can effectively recover the data-generating process for a wide range of scenarios, with varying number of transmitters, number of receivers, constellation order, channel length, and signal-to-noise ratio.

The Multiway Relay Channel

The multiuser communication channel, in which multiple users exchange information with the help of a relay terminal, termed the multiway relay channel (mRC), is introduced. In this model, multiple interfering clusters of users communicate simultaneously, such that the users within the same cluster wish to exchange messages among themselves, i.e., each user multicasts its message to all the other users in its own cluster. It is assumed that the users cannot receive each other's signals directly. Hence, the relay terminal in this model is the enabler of communication. In particular, restricted encoders are considered, such that the encoding function of each user depends only on its own message and the received signal is used only for decoding the messages of the other users in the cluster. Achievable rate regions and an outer bound are characterized for the Gaussian mRC, and their comparison is presented in terms of the exchange rate, the symmetric rate point in the capacity region in a symmetric Gaussian mRC scenario. It is shown that the compress-and-forward (CF) protocol achieves exchange rates within a constant bit offset of the optimal exchange rate, independent of the power constraints of the terminals in the network. A finite bit gap between the exchange rates achieved by the CF and the amplify-and-forward protocols is also shown. The two special cases of the mRC, the full data exchange model, in which every user wants to receive messages of all other users, and the pairwise data exchange model which consists of multiple two-way relay channels, are investigated in detail. In particular for the pairwise data exchange model, in addition to the proposed random coding-based achievable schemes, a nested lattice coding-based scheme is also presented and is shown to achieve exchange rates within a constant bit gap of the exchange capacity.

Central limit theorem and large deviations of the fading Wyner cellular model via product of random matrices theory

We apply the theory of products of random matrices to the analysis of multi-user communication channels similar to the Wyner model, which are characterized by short-range intra-cell broadcasting. We study fluctuations of the per-cell sum-rate capacity in the non-ergodic regime and provide results of the type of the central limit theorem (CLT) and large deviations (LD). Our results show that CLT fluctuations of the per-cell sum-rate C m are of order $$ 1/\sqrt m $$, where m is the number of cells, whereas they are of order 1/m in classical random matrix theory. We also show an LD regime of the form P(|C m − C| > ɛ) ≤ e −mα with α = α(ɛ) > 0 and C = $$ \mathop {\lim }\limits_{m \to \infty } $$ C m , as opposed to the rate $$ e^{ - m^2 \alpha } $$ in classical random matrix theory.

Online activity detection in a multiuser environment using the matrix CUSUM algorithm

Early detection of the entrance of a new user into a multiuser communication channel is considered. The matrix CUSUM (cumulative-sum) test, a multialternative quickest change detection method, is introduced and then applied to this problem. The general behavior of this algorithm is described in analytical results and simulations.

Integrated spatial-temporal detectors for asynchronous Gaussian multiple-access channels

The optimum (maximum likelihood) multi-element global sequence detector is derived for the multiuser communication channel. The resulting integrated array-detector is composed of retrodirective beamformers in the user's directions followed by a bank of matched filters and a processor implementing a dynamic programming (Viterbi) algorithm. Suboptimal realizations of the multi-element detector are considered, offering reduced complexity as compared to the optimum solution. The performance of the multi-element detector is analyzed in terms of error probability, detection asymptotic efficiency and near-far resistance. In particular, the effects of array beamforming on the performance is discussed and shown to generalize existing results for the scalar (single channel) case. Processing which uses the additional dimension of space can result in significant improvements in performance over the scalar case. Following a general performance analysis, a comparison of several suboptimal multi-element detectors is given. In particular, it is shown that a new combined spatial-temporal processing is always uniformly superior to that of using beamformers which result in separation of signals, followed by single-user detectors.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Partitioning a sample using binary-type questions with ternary feedback

The problem is to find the largest observation in a random sample of size n by asking binary-type questions of people (or items) in the sample. At each stage of the search, a threshold is calculated and a binary-type question is posed to each member of the sample. The threshold is determined from answers given to the previous questions, and no exact data is ever collected, i.e., no member is asked to explicitly provide his observation. Arrow, Pesotchinsky, and Sobel (APS) calculated the optimal threshold sequence for two different objectives: (i) minimize the average number of questions required for a solution, and (ii) maximize the probability of solving the problem in, at most, r questions. APS have assumed that the number of respondents in the affirmative at each stage of the search is exactly known. There exist applications where the number of affirmative answers is only known to be one member of the set (0, 1, more than 1). For these applications, we calculate exactly the optimal thresholds, in the sense of maximizing the probability of getting precisely one affirmative answer to the next binary question. An application of the threshold-calculation procedure is demonstrated in resolution of packet collisions over multiuser communication channel.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>