Performance analysis of multi-user scheduling over time-varying channel

Secrecy outage analysis of multi-user multi-eavesdropper cellular networks in the face of cochannel interference

The multi-user MIMO space time scheduling problem is the prime focus of the paper. Optimizing the link level performance of the MIMO system does not always imply achieving scheduling level optimization. Therefore,design optimization across the link layer and the scheduling layer is very important to exploit fully the temporal and spatial dimensions of the communication channel. We address the design of the optimal space time scheduler for a multi-user MIMO system based on an information theoretical approach. We assume a partial feedback channel, namely a scaler rate-feedback channel, is available to the transmitter, but the full channel matrix is unknown to the transmitter. With the partial feedback, we find that the optimal resource allocation strategy is water-filling in both the temporal domain and spatial domain. The optimal scheduler should allocate all the power to at most n/sub R/ users at any particular instant. The optimal scheduler performance depends on the total number of users (K), the number of receive antennas at the base station (n/sub R/), and the number of transmit antennas at the mobile (n/sub T/). The scheduling gain increases with n/sub R/ due to the distributed MIMO configuration formed between mobile users and base station. With a single power feedback channel, the scheduling gain reduces as n/sub T/ increases, illustrating that transmit diversity hurts the scheduling performance in the case of scalar feedback. Finally, scheduling performance improves as K increases due to the multi-user selection diversity.

This paper explores optimal and suboptimal packet schedulers for time-varying flat fading channels that trade-off between minimization of the average delay and the average transmitted power. Both uncorrelated and correlated block fading channels are investigated. Extending a previous work, we formulate the trade-off as a unconstrained Markov decision processes and find the stationary deterministic optimal policy using both relative value iteration and policy iteration algorithm. As well, we present constrained Markov decision processes formulation of the problem and linear programming algorithm to solve it and show that optimal schedulers are randomized in this case. In order to alleviate the computational complexity needed, to determine the optimal scheduling policy we propose a suboptimal log-scheduling policy that has performance close to that of the optimal scheduler. The proposed policy is also robust to different channel models. It is demonstrated that log-policy is favorable to the water-filling policy for very slow fading channels.

The article considers the resource allocation and scheduling problem in a grid computing environment. The article proposes system optimisation scheduling (SOS) that provides a potential solution of joint optimisation of objectives for both the resource and application layer, which combine both application-oriented and resource-oriented scheduling benefits. Grid systems will strive to find an optimal relation between user satisfaction and resource utilisation. Utility functions are used to express grid user's Quality of Service requirement, resource provider's benefit function and system's objectives. In order to verify the efficiency of the proposed scheduling algorithm, we compare the performance of application optimisation scheduling, resource optimisation scheduling, SOS with a traditional Round-Robin algorithm. The simulations study the effect of the request rate and task-to-resource ratio on the different scheduling algorithm.

We derive the capacity of time-varying channels with memory that have causal channel side information (CSI) at the sender and receiver. We obtain capacity of block-memoryless and asymptotically block-memoryless channels with block-memoryless or weakly decorrelating side information. Our coding theorems rely on causal generation of the codewords relative to the causal transmitter CSI. The CSI need not be perfect, and we consider the case where the transmitter and receiver have the same causal CSI as well as the case where the transmitter CSI is a deterministic function of the receiver CSI. For block-memoryless and asymptotically block-memoryless channels, our coding strategy averages mutual information density over multiple transmission blocks to achieve the maximum average mutual information. We apply the coding theorem associated with the block-memoryless channel to determine the capacity and optimal input distribution of intersymbol interference (ISI) time-varying channels with causal perfect CSI about the time-varying channel. The capacity of this channel cannot be found through traditional decomposition methods

Orthogonal time frequency space (OTFS) modulation exhibits excellent performance in time-varying channels with severe Doppler shifts. Diversity analysis is an effective method in predicting error performance of an OTFS system. Recent works have studied OTFS diversity in a time-varying channel assuming constant delay-Doppler (DD) channel realization per frame. However, in practice, DD channel realizations may vary in a frame due to the rapid changes in channel geometry relative to a frame duration, leading to a block time-varying (BTV) channel. This paper analyzes the OTFS diversity in such BTV channels. Specifically, we derive the pairwise error probability of the system and analyze its diversity by finding the minimum rank of the pairwise error matrix and its associated multiplicity. We also compare it with other known schemes and demonstrate its improved diversity. Finally, simulations are performed to validate the analytical results.

In this paper, we study scheduling schemes for a two-user two-way relaying system. Two transmission modes are considered, i.e., point-to-point direct transmission and two-way amplify-and-forward (AF) relaying. An optimal scheduling method that opportunistically selects the best transmission mode for each user with instantaneous channel state information (CSI) is proposed to minimize the sum bit-error-rate (BER). However, for optimal scheduling, the scheduler requires the knowledge of all the channels. To reduce the feedback information of CSI, we also propose a suboptimal scheduling scheme that only the CSI of the two direct links is explored to select the transmission mode. Simulation results show that both the optimal and suboptimal schemes outperform the fixed direct transmission and fixed two-way relayed transmission, and the performance gap between the optimal and suboptimal scheduling scheme is small. These results show that the suboptimal scheduling scheme provides a good trade-off between implementation complexity and system performance.

In the context of cellular systems, it has been shown that multicell processing can eliminate inter-cell interference and provide high spectral efficiencies with respect to traditional interference-limited implementations. Moreover, it has been proved that the multiplexing sum-rate capacity gain of multicell processing systems is proportional to the number of base station (BS) antennas. These results have been also established for cellular systems, where BSs and user terminals (UTs) are equipped with multiple antennas. Nevertheless, a common simplifying assumption in the literature is the uncorrelated nature of the Rayleigh fading coefficients within the BSUT MIMO links. In this direction, this paper investigates the ergodic multicell-processing sum-rate capacity of the Gaussian MIMO cellular multiple-access channel in a correlated fading environment. More specifically, the multiple antennas of both BSs and UTs are assumed to be correlated according to the Kronecker product model. Furthermore, the current system model considers Rayleigh fading, uniformly distributed UTs over a planar coverage area and power-law path loss. Based on free probabilistic arguments, the empirical eigenvalue distribution of the channel covariance matrix is derived and it is used to calculate both optimal joint decoding and minimum mean square error (MMSE) filtering capacity. In addition, numerical results are presented, where the per-cell sum-rate capacity is evaluated while varying the cell density of the system, as well as the level of fading correlation. In this context, it is shown that the capacity performance is greatly compromised by BS-side correlation, whereas UT-side correlation has a negligible effect on the system's performance. Furthermore, MMSE performance is shown to be greatly suboptimal but more resilient to fading correlation in comparison to optimal decoding.

In this paper, we provide expressions for the secrecy outage probability (SOP) for suboptimal and optimal opportunistic scheduling schemes in a reconfigurable intelligent surface (RIS) aided single antenna system with multiple eavesdroppers in approximate closed form. A suboptimal scheduling (SS) scheme is analyzed, which is used when the channel state information (CSI) of the eavesdropping links is unavailable, and the optimal scheduling (OS) scheme is also analyzed, which is used when the global CSI is available. For each scheme, we provide a simplified expression for the SOP in the high signal-to-noise ratio (SNR) regime to demonstrate its behavior as a function of the key system parameters. At high SNR, the SOP saturates to a constant level which decreases exponentially with the number of RIS elements in the SS scheme and with the product of the number of RIS elements and the number of users in the OS scheme. We also show that the derived SOP of the SS scheme can directly provide the SOP for the best antenna-user pair scheduling scheme in a multiple antenna system. We compare the performance of the opportunistic user scheduling schemes with that of a non-orthogonal multiple access (NOMA) based scheduling scheme which chooses a pair of users in each time slot for scheduling and we show that the opportunistic schemes outperform the NOMA-based scheme. We also derive a closed-form expression for the SOP of a decode-and-forward (DF) relay-aided scheduling scheme in order to compare it with that of the RIS-aided system. It is found that the RIS-aided system outperforms the relay-aided system when the number of RIS elements is sufficiently large. An increased number of RIS elements is required to outperform the relay-aided system at higher operating frequencies.

We consider the fundamental delay tradeoffs for utility optimal scheduling in a general network with time-varying channels. A network controller acts on randomly arriving data and makes flow control, routing, and resource allocation decisions to maximize a fairness metric based on a concave utility function of network throughput. A simple set of algorithms are constructed that yield total utility within O(1/V) of the utility-optimal operating point, for any control parameter V>0, with a corresponding end-to-end network delay that grows only logarithmically in V. This is the first algorithm to achieve such super-fast performance. Furthermore, we show that this is the best utility-delay tradeoff possible. This work demonstrates that the problem of maximizing throughput utility in a data network is fundamentally different than related problems of minimizing average power expenditure, as these latter problems cannot achieve such performance tradeoffs

Massive Multiple-Input Multiple-Output (MIMO) is a key enabler of 5G and beyond mobile networks, significantly improving spectral efficiency through multiuser beamforming. However, in massive MIMO systems, the multiuser scheduling problem, selecting which users to serve concurrently on the same time-frequency resources, remains a critical challenge. Due to potential channel correlation among users, suboptimal multiuser scheduling can lead to inter-symbol interference and throughput degradation. Additionally, the scheduler must balance the achieved spectral efficiency with user fairness. While the Optimal Proportional Fair (Opt-PF) scheduler seeks to achieve this balance, applying it to the massive MIMO scheduling problem leads to an NP-hard optimization problem. Although existing approximation algorithms can reduce the computational complexity of the Opt-PF multiuser scheduler, they often fail to provide adequate fairness or adapt to fast varying channels, making them impractical for real-world deployment. As an alternative, Machine Learning (ML)-based methods, particularly Deep Reinforcement Learning (DRL) models, have shown promise in addressing this problem. To further foster innovation in this area, the International Telecommunication Union (ITU) AI/ML in 5G Challenge hosted a competition focused on enhancing the performance of a DRL-based multiuser scheduler. The provided baseline scheduler employed a user-grouping algorithm to cluster users with low channel correlation and a Soft Actor-Critic (SAC) DRL framework for user selection. This paper presents the winning solution to the ITU competition, which proposes two approaches to enhance the performance of the baseline scheduler.

In this paper, the performance of limited feedback precoded spatial multiplexing multiple-input multiple-output orthogonal frequency division multiplexing (MIMO - OFDM) systems is investigated in time varying fading channels. Most studies into limited feedback precoded (MIMO - OFDM) systems are based on the notion of time invariant channels throughout transmission. Therefore, the precoding matrix for each subcarrier can be designed at the receiver in terms of the current channel state, fed back to the transmitter, and used for the subsequent block of data symbols. However, in time varying channels, the feedback information becomes outdated, which results in significant system performance degradation. To mitigate this system performance degradation caused by outdated information due to a delay in the feedback channel, this paper considers a more practical system, where the channel varies from one block of symbols to another. A method is proposed based on modelling the channel as an autoregressive (AR) model and using a Kalman filter linear predictor at the receiver to predict the channels states, required to design the precoding matrices for the next OFDM symbol carriers whose indexes are fed back to the transmitter. The performance of this method is investigated using computer simulation, and the results obtained for the proposed channel prediction demonstrate improved bit error rate performance for time-varying Rayleigh fading channels, even in systems with high mobility.

We investigate how to allocate resources when the capacity of the channel fluctuates over time, and when the scheduler does not have full information regarding the actual state of the system. The main motivation comes from communication networks in which the available capacity might evolve over time. A nice introduction to the role of information in scheduling, mostly in the context of stability, is provided in [1]. This paper shows how the problem can be cast as a Partially Observable MDP (POMDP), and observes that in full generality there is very little that can be said. We thus set out to study a simple, yet interesting, instance of the problem in which the channel capacity can only take good values, ON, or bad values OFF. When the channel is ON, data can be transmitted through the channel, but not when it is OFF. Even though the scheduler does not have access to the state of every channel, it does observe successful transmissions.

The security-reliability tradeoff (SRT) in free-space optical (FSO) communications is the most critical property to highlight, especially with respect to the development of wireless optical communications. In this paper, opportunistic scheduling selection techniques are used to improve the SRT of multiuser FSO systems under the combined influence of atmospheric turbulence with Fisher-Snedecor <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {F}$ </tex-math></inline-formula> distribution, generalized pointing error, and path losses due to foggy weather. Due to the broadcast nature of wireless optical propagation, the optical transmission from the transmitting users to the legitimate receiver can be easily intercepted by eavesdroppers. Therefore, an optimal user scheduling (OUS) scheme is proposed in this work to protect the legitimate wireless transmission from eavesdroppers, where a user with the highest secrecy capacity is scheduled to transmit his confidential information to the receiver. Closed-form expressions for the outage probability, interception probability, and SRT are derived for the conventional round-robin scheduling (RRS) and the proposed OUS. In addition, an asymptotic analysis for the outage probability, interception probability, and SRT is performed to provide insight into the impact of user scheduling on the system performance. We also propose the use of “friendly jamming” techniques, where the user with the lowest secrecy capacity is selected by the authorized receiver to jam the existing interceptor. Finally, another SRT is formulated to determine the impact of a friendly jammer on the secrecy performance of the system. The results show that the proposed OUS outperforms the RRS in terms of intercept probability and SRT performance. The obtained exact and asymptotic results are validated by Monte-Carlo simulations.

Massive multiple-input multiple-output (MIMO) is a key technology for the 5th Generation wireless communication systems. Joint spatial division and multiplexing (JSDM) method can reduce the cost of downlink training and feedback for CSIT of frequency-division duplexing (FDD) system. In this paper, we studied user grouping and user scheduling problems based on JSDM, and proposed an improved greedy user scheduling algorithm with lower complexity. Numerical results show that the user grouping and scheduling based on the proposed improved algorithm have much lower computational complexity while resulting in only a slight loss in system performance.