Interference channels with bursty traffic and delayed feedback

We explore the benefit of feedback for physical layer interference management in wireless networks without centralized upper layer control mechanisms. Lack of coordination in the upper layer could make the interference experienced in the physical layer bursty. To understand how to harness such burstiness with feedback, we investigate a two-user bursty interference channel (IC), where the presence of interference is governed by a Bernoulli random state. We completely characterize the capacity region of the symmetric two-user linear deterministic bursty IC with feedback. The proposed two-phase scheme exploits feedback either for refining the previous interfered reception or for relaying additional information to the legitimate receiver of the other user. Matching outer bounds are derived by novel techniques that take the effect of delayed state information into account. We also use insights from the deterministic case to characterize the approximate symmetric capacity for the symmetric Gaussian bursty IC with feedback in the weak interference regime.

This chapter contains sections titled: Introduction Total Downlink Interference Temporal Behavior of Downlink Interference Self-Similarity of Downlink Interference Model-Based Optimal Downlink Interference Prediction Performance Optimization for Non-Real-Time Data Transmission Simulation Results Conclusion Glossary Cross References

This special issue is devoted to the new research addressing challenges in the areas of wireless computing and networking. Wireless computing and networking has enabled heterogeneous access technologies for providing services to ubiquitous users. The demanding networking environment of wireless communications imposes many challenges, such as channel estimation, network protocol design, resource management, and systematic design. Due to nonuniform spectrum allocation, various radio resource management policies, economic concerns, scarcity of radio resources, and user mobility, overcoming these issues becomes more demanding. This special issue includes ten research articles from five countries including the United States, China, South Korea, Norway, and India which covers various research issues in the areas of wireless computing and networking. These articles can be broadly classified into three categories: (1) data management and query processing in wireless networks; (2) backbone management in wireless networks; and (3) routing in wireless networks. A detailed description of the corresponding works in each category is given below.

Deterministic channel models have recently proven to be powerful tools for obtaining capacity bounds for Gaussian multiuser networks that are tight in the high SNR regime. In this work we apply this technique to the Gaussian cognitive radio channel: a 2×2 interference channel in which one transmitter has non-causal knowledge of the message of the other, whose capacity region in general is unknown. We approximate the Gaussian cognitive radio channel at high SNR by an underlying binary linear deterministic channel model for which we determine the exact capacity region.

This paper revisits the interference channels under strong interference and very strong interference, for both discrete memoryless and Gaussian channels. In particular, it is shown that for the classical definitions, while the conditions of strong interference for discrete and Gaussian cases coincide with each other, those of very strong interference are not consistent with each other in terms of mutual information inequalities. Based on this observation, a new class of discrete memoryless interference channels under very strong interference is introduced whose capacity region is characterized. This new class of channels is consistent with the Gaussian interference channel under very strong interference. Moreover, this new class of interference channels is not a subset of any existing classes of discrete memoryless interference channels with known capacity region.

Resource management in wireless networks refers to the processes and techniques used to allocate, control, and optimize the various resources in a wireless communication system to ensure efficient and reliable operation. These resources typically include spectrum (frequency bands), power, time slots, and network infrastructure (e.g., base stations, access points). Effective resource management is crucial for maximizing network performance, ensuring Quality of Service (QoS), and providing a good user experience, especially in environments with limited resources and high demand. Quality of Service (QoS) refers to the ability of the network to provide different levels of service quality to various types of traffic, applications, and users. QoS in 5G is essential due to the diverse range of services the network is designed to support, each with its own specific requirements for performance metrics such as latency, reliability, bandwidth, and availability. In this paper, we study how the QoS-aware resource allocation procedure is handled in wireless networks. Different resource management techniques were studied and also the paper looked at some of the existing QoS-aware resources allocation schemes by highlighting the operation, strength and weakness and the QoS parameters considered by each of the schemes.

In this paper, we consider the problem for maximizing the throughput of a discrete-time wireless network, where only certain sets of links can transmit simultaneously. It is well known that each set of such links can be represented by a configuration vector and the convex hull of the configuration vectors determines the capacity region of the wireless network. In the literature, packet scheduling polices that stabilize any admissible traffic in the capacity region are mostly related to the maximum weighted matching algorithm (MWM) that identifies the most suitable configuration vector in every time slot. Unlike the MWM algorithm, we propose a dynamic frame sizing (DFS) algorithm that also stabilizes any admissible traffic in the capacity region. The DFS algorithm, as an extension of our previous work for wired networks, also does not have a fixed frame size. To determine the frame size, an optimization problem needs to be solved at the beginning of each frame. Once the frame size is determined, a hierarchical smooth schedule is devised to determine both the schedule for configuration vectors and the schedule for multicast traffic flows in each link. Under the assumption of Bernoulli arrival processes with admissible rates, we show that the number of packets of each multicast traffic flow inside the wireless network is bounded above by a constant and thus one only requires to implement a finite internal buffer in each link in such a wireless network.

Effective resource management plays a pivotal role in wireless networks, which, unfortunately, typically results in challenging mixed-integer nonlinear programming (MINLP) problems. Machine learning-based methods have recently emerged as a disruptive way to obtain near-optimal performance for MINLPs with affordable computational complexity. There have been some attempts in applying such methods to resource management in wireless networks, but these attempts require huge amounts of training samples and lack the capability to handle constrained problems. Furthermore, they suffer from severe performance deterioration when the network parameters change, which commonly happens and is referred to as the task mismatch problem. In this paper, to reduce the sample complexity and address the feasibility issue, we propose a framework of Learning to Optimize for Resource Management (LORM). In contrast to the end-to-end learning approach adopted in previous studies, LORM learns the optimal pruning policy in the branch-and-bound algorithm for MINLPs via a sample-efficient method, namely, imitation learning . To further address the task mismatch problem, we develop a transfer learning method via self-imitation in LORM, named LORM-TL , which can quickly adapt a pre-trained machine learning model to the new task with only a few additional unlabeled training samples. Numerical simulations demonstrate that LORM outperforms specialized state-of-the-art algorithms and achieves near-optimal performance, while providing significant speedup compared with the branch-and-bound algorithm. Moreover, LORM-TL, by relying on a few unlabeled samples, achieves comparable performance with the model trained from scratch with sufficient labeled samples.

On the optimal formulation of resource management in wireless networks by Jihui Zhang thesis 2005 xvi, 176 leaves : ill. ; 30 cm Resource management is of great importance in wireless networks, as it can improve the scarce wireless spectrum…Read more ›

Below the Media Access Control (MAC) layer is the Physical (PHY) layer which deals with the actual transmission of the bits received from the MAC layer above into electromagnetic signals. This layer is optimized to implore power management in wireless networks. Power management is a crucial issue in wireless and mobile networks. In this paper, we propose an Adaptive Context-Aware Rate Selection (ACARS) algorithm to handle the issue of power consumption in wireless networks. This algorithm is implemented by optimizing the PHY layer to transmit efficiently as the number of nodes changes and we estimate the Signal-to-Noise Ratio (SNR) to the PHY layer. Results show that by using the appropriate power management technique, ACARS is reliable and efficient for power consumption in wireless networks which is a high demand for vehicular networks.

We propose a framework for adaptive policy-based quality of service (QoS) management in wireless networks. The proposed framework is a multi-layer agent system where agents cooperate to dynamically adapt network behavior to maintain the delivered QoS level to the user. QoS prediction agents utilize user information such as the user profile, location and terminal characteristics, to predict future changes in the supplied QoS. These changes are then reported to a set of QoS adaptation agents, which dynamically adapt the network policies to meet the user's requirements. The proposed adaptive approach assembles policies at run-time given sets of imposed constraints and goals to be satisfied. The new approach gives more flexibility to users and applications to dynamically change their QoS requirements while maintaining a smooth delivery of QoS.

We provide a natural formulation of information theoretic games on interference channels. We analyze this game on a class of deterministic interference channels recently introduced to approximate Gaussian channels in the interference-limited regime. Our main result is a complete and simple characterization of the subset of the interference channel capacity region that can be achieved as Nash equilibria. We show that for all parameter values of the interference channel, there are always Nash equilibria which are efficient, i.e. on the boundary of the capacity region.

The N +1 user, 1 ×N single-input multiple-output (SIMO) Gaussian interference channel where each transmitter has a single antenna and each receiver has N antennas is studied. The sum capacity within O(1) is characterized for the symmetric case where all direct links have the same signal-to-noise ratio (SNR) and all undesired links have the same interference-to-noise ratio (INR). The gap to the exact capacity is a constant which is independent of SNR and INR. To get this result, we first generalize the deterministic interference channel introduced by El Gamal and Costa to model interference channels with multiple antennas. We derive the capacity region of this deterministic interference channel. Based on the insights provided by the deterministic channel, we characterize the generalized degrees of freedom (GDOF) of Gaussian case, which directly leads to the O(1) capacity approximation. On the achievability side, an interesting conclusion is that the GDOF regime where treating interference as noise is found to be optimal in the two-user interference channel, does not appear in the N + 1 user, 1 × N SIMO case. On the converse side, new multiuser outer bounds emerge out of this work that do not follow directly from the two-user case. In addition to the GDOF region, the outer bounds identify a strong interference regime where the capacity region is established.

In this paper, we have combined a game‐theoretic and an information‐theoretic notions to describe the selfish behavior of each user in a two‐user linear deterministic cognitive interference channel (LD‐CIC). The two‐user LD‐CIC is the simple case of the cognitive radio technology in the high SNR regime. This model is a solution to improve the spectral efficiency in software defined network (SDN) concept in 5G. In the two‐user LD‐CIC, the information bits of the primary/licensed transmitter is a‐priori provided at the cognitive/secondary/unlicensed transmitter. So that, the cognitive transmitter can cooperate and utilize the frequency spectrum to send the information bits of the both transmitters. In information‐theoretic approaches, full cooperation is assumed between transmitters for codebook and rate selection in this channel. This may not be a realistic assumption if transmitters are selfish and are willing to maximize their own benefit. In particular, we have defined the Stackelberg equilibrium (SE) region on the capacity region of the two‐user LD‐CIC to capture the behavior of each transmitter when they compete even though one of them is informed of the other transmitter information bits. We have implemented a two‐phase Stackelberg game with the primary and cognitive transmitters acting as the leader in the first phase, and the follower in the second phase, respectively. In this implementation, both transmitters are assumed to be selfish and only interested in optimizing their own pay‐offs. The SE region is characterized as a subset of capacity region in which the transmitters cannot gain more transmission rate by individually deviating from the SE strategy. The achievability of the resultant region has been configured using a randomized Han‐Kobayashi scheme for all the channel gains. Also, we have compared the obtained SE region with the previous equilibrium regions such as Nash equilibrium (NE) region of the linear deterministic interference channel (LD‐IC). The results have shown that the SE points on the sum‐rate of the LD‐CIC capacity region are more than the NE points on the sum rate of the capacity region of LD‐IC, so the SE region of the LD‐CIC is Pareto superior to the NE region of LD‐IC.

Cooperative communication is a promising technique for future wireless networks. It can be used in improving communication reliability, and enhancing power and spectrum efficiency. However, its performance gain degrades in the presence of co-channel interference which makes it essential to propose interference mitigation schemes. In this paper, we introduce two efficient cooperative communication schemes with interference management for multi-user cooperative wireless networks that are based on best relay and user selection (BRUS) technique. BRUS maximizes the received signal to noise ratio (SNR) while minimizing the interference by an optimal time slot allocation for the users. The first introduced scheme is using BRUS technique only if the user demands cooperation, and the second scheme is always using BRUS technique to enhance the system performance. We derive exact closed form expressions for the outage probability, ergodic capacity, average consumed power, and average bit error probability for the introduced cooperative schemes. Simulations are used to validate the analytical results and an agreement is observed. The results confirm the advantage of the introduced cooperation schemes in enhancing the system performance and improving the interference management.