On Coding for Reliable Communication over Packet Networks

We present a capacity-achieving coding scheme for unicast or multicast over lossy packet networks. In the scheme, intermediate nodes perform additional coding yet do not decode nor even wait for a block of packets before sending out coded packets. Rather, whenever they have a transmission opportunity, they send out coded packets formed from random linear combinations of previously received packets. All coding and decoding operations have polynomial complexity. We show that the scheme is capacity-achieving as long as packets received on a link arrive according to a process that has an average rate. Thus, packet losses on a link may exhibit correlation in time or with losses on other links. In the special case of Poisson traffic with i.i.d. losses, we give error exponents that quantify the rate of decay of the probability of error with coding delay. Our analysis of the scheme shows that it is not only capacity-achieving, but that the propagation of packets carrying "innovative" information follows the propagation of jobs through a queueing network, and therefore fluid flow models yield good approximations. We consider networks with both lossy point-to-point and broadcast links, allowing us to model both wireline and wireless packet networks.Comment: 33 pages, 6 figures; revised appendi

Scheduling of Multicast and Unicast Services under Limited Feedback by using Rateless Codes

Many opportunistic scheduling techniques are impractical because they require accurate channel state information (CSI) at the transmitter. In this paper, we investigate the scheduling of unicast and multicast services in a downlink network with a very limited amount of feedback information. Specifically, unicast users send imperfect (or no) CSI and infrequent acknowledgements (ACKs) to a base station, and multicast users only report infrequent ACKs to avoid feedback implosion. We consider the use of physical-layer rateless codes, which not only combats channel uncertainty, but also reduces the overhead of ACK feedback. A joint scheduling and power allocation scheme is developed to realize multiuser diversity gain for unicast service and multicast gain for multicast service. We prove that our scheme achieves a near-optimal throughput region. Our simulation results show that our scheme significantly improves the network throughput over schemes employing fixed-rate codes or using only unicast communications

Distributed Wireless Multicast: Throughput and Delay

Multicast transmission, in which data is sent from a source to multiple destinations, is an important form of data communication in wireless networks. Numerous applications require multicast transmission, including content distribution, conferencing, and military and emergency messages, as well as certain network control mechanisms, such as timing synchronization and route establishment. Finding a means to ensure efficient, reliable multicast communication that can adapt to changing channel conditions and be implemented in a distributed way remains a challenging open problem. In this dissertation, we propose to meet that challenge through the use of random coding of data packets coupled with random access to a shared channel. We present an analysis of both the throughput and delay performance of this scheme. We first analyze the multicast throughput in a random access network of finitely many nodes, each of which serves as either a source or a destination node. Our work quantifies throughput in terms of both the Shannon capacity region and the stable throughput region and indicates the extent to which a random linear coding scheme can outperform a packet retransmission scheme. Next, we extend these notions to a random access network of general topology in which each node can act as a receiver or a sender for multiple multicast flows. We present schemes for nodes in the network to compute their random access transmission probabilities in such a way as to maximize a weighted proportional fairness objective function of the multicast throughput. In the schemes we propose, each node can compute its transmission probability using information from neighboring nodes up to two hops away. We then turn our focus to queueing delay performance and propose that random coding of packets be modeled as a bulk-service queueing system, where packets are served and depart the queue in groups. We analyze within this framework two different coding schemes: one with fixed expected coding rate and another with a coding rate that adapts to the traffic load. Finally, we return to the question of multicast throughput and address the effects of packet length, overhead, and the time-varying nature of the wireless channel

Distributed Stochastic Power Control in Ad-hoc Networks: A Nonconvex Case

Utility-based power allocation in wireless ad-hoc networks is inherently nonconvex because of the global coupling induced by the co-channel interference. To tackle this challenge, we first show that the globally optimal point lies on the boundary of the feasible region, which is utilized as a basis to transform the utility maximization problem into an equivalent max-min problem with more structure. By using extended duality theory, penalty multipliers are introduced for penalizing the constraint violations, and the minimum weighted utility maximization problem is then decomposed into subproblems for individual users to devise a distributed stochastic power control algorithm, where each user stochastically adjusts its target utility to improve the total utility by simulated annealing. The proposed distributed power control algorithm can guarantee global optimality at the cost of slow convergence due to simulated annealing involved in the global optimization. The geometric cooling scheme and suitable penalty parameters are used to improve the convergence rate. Next, by integrating the stochastic power control approach with the back-pressure algorithm, we develop a joint scheduling and power allocation policy to stabilize the queueing systems. Finally, we generalize the above distributed power control algorithms to multicast communications, and show their global optimality for multicast traffic.Comment: Contains 12 pages, 10 figures, and 2 tables; work submitted to IEEE Transactions on Mobile Computin

TOWARD LAYERLESS COOPERATION AND RATE CONTROL IN WIRELESS MULTI-ACCESS CHANNELS

In wireless networks, a transmitted message may successfully reach multiple nodes simultaneously, which is referred to as the Wireless Multicast Advantage. As such, intermediate nodes have the ability to capture the message and then contribute to the communication toward the ultimate destination by cooperatively relaying the received message. This enables cooperative communication, which has been shown to counteract the effects of fading and attenuation in wireless networks. There has been a great deal of work addressing cooperative methods and their resulting benefits, but most of the work to date has focused on physical-layer techniques and on information-theoretic considerations. While compatible with these, the main thrust of this dissertation is to explore a new approach by implementing cooperation at the network layer. First, we illustrate the idea in a multi-hop multi-access wireless network, in which a set of source users generate packets to deliver to a common destination. An opportunistic and dynamic cooperation protocol is proposed at the network level, where users with a better channel to the destination have the capability and option to relay packets from users that are farther afield. The proposed mode of cooperation protocol is new and relies on MAC/Network-level of relaying, but also takes into account physical-layer parameters that determine successful reception at the destination and/or the relay. We explicitly characterize the stable throughput and average delay performance. Our analysis reveals that cooperation at the network layer leads to substantial performance gains for both performance metrics. Next, on top of the network-layer cooperation, we investigate enhanced cooperative techniques that exploit more sophisticated physical-layer properties. Specifically, we consider dynamic decode-and-forward, superposition coding, and multipacket reception capability, and we quantify the extent to which the enhancement techniques can further improve the stable throughput region. Then we revert back to the two-user multi-access channel with single-packet reception, which has been extensively studied in the case of no cooperation. After cooperation is permitted between the two users, we revisit the relationship between the stability region and the throughput region under both scheduled access and random access schemes. Finally, we shift our focus from the packet-level to bit-level multi-access channels. By exploiting the bit-nature of a packet, we create a bridge between traditional physical-layer-based transmission rates and classical MAC/Network-layer-based throughput rates. We first obtain the closed form of the stability region in bits/slot. Then, as a separate, but related issue, we look at the minimum delivery time policy; for any initial queue size vector, the optimal policy that empties all bits in the system within the shortest time is characterized

Scheduling and Power Control for Wireless Multicast Systems via Deep Reinforcement Learning

Multicasting in wireless systems is a natural way to exploit the redundancy in user requests in a Content Centric Network. Power control and optimal scheduling can significantly improve the wireless multicast network's performance under fading. However, the model based approaches for power control and scheduling studied earlier are not scalable to large state space or changing system dynamics. In this paper, we use deep reinforcement learning where we use function approximation of the Q-function via a deep neural network to obtain a power control policy that matches the optimal policy for a small network. We show that power control policy can be learnt for reasonably large systems via this approach. Further we use multi-timescale stochastic optimization to maintain the average power constraint. We demonstrate that a slight modification of the learning algorithm allows tracking of time varying system statistics. Finally, we extend the multi-timescale approach to simultaneously learn the optimal queueing strategy along with power control. We demonstrate scalability, tracking and cross layer optimization capabilities of our algorithms via simulations. The proposed multi-timescale approach can be used in general large state space dynamical systems with multiple objectives and constraints, and may be of independent interest.Comment: arXiv admin note: substantial text overlap with arXiv:1910.0530