Wireless Multi-access Channel Research Articles

Relay-Assisted Cooperative Federated Learning

Federated learning (FL) has recently emerged as a promising technology to enable artificial intelligence (AI) at the network edge, where distributed mobile devices collaboratively train a shared AI model under the coordination of an edge server. To significantly improve the communication efficiency of FL, over-the-air computation allows a large number of mobile devices to concurrently upload their local models by exploiting the superposition property of wireless multi-access channels. Due to wireless channel fading, the model aggregation error at the edge server is dominated by the weakest channel among all devices, causing severe straggler issues. In this paper, we propose a relay-assisted cooperative FL scheme to effectively address the straggler issue. In particular, we deploy multiple half-duplex relays to cooperatively assist the devices in uploading the local model updates to the edge server. The nature of the over-the-air computation poses system objectives and constraints that are distinct from those in traditional relay communication systems. Moreover, the strong coupling between the design variables renders the optimization of such a system challenging. To tackle the issue, we propose an alternating-optimization-based algorithm to optimize the transceiver and relay operation with low complexity. Then, we analyze the model aggregation error in a single-relay case and show that our relay-assisted scheme achieves a smaller error than the one without relays provided that the relay transmit power and the relay channel gains are sufficiently large. The analysis provides critical insights on relay deployment in the implementation of cooperative FL. Extensive numerical results show that our design achieves faster convergence compared with state-of-the-art schemes.

Binary Joint Network-Channel Coding for Reliable Multi-Hop Wireless Networks

The high and variable packet loss in wireless networks due to the unreliable nature of wireless channels, interference, link or router failure, fading, and shadowing wireless link are the main problems and challenges in implementing a reliable wireless network. Packet loss probability is the most important performance measure for real-time applications. Packet loss is due to errors, congestion in the queue because of high traffic, collision, hidden nodes, and link failures. In this paper, we focus on the links failure problem. To address this problem, improve the performance of wireless networks, and enhance and make the network survivability and availability more reliable, a new efficient binary joint network and channel coding (B-JNCC) technique is proposed. B-JNCC combines low-complexity parity-check (LCPC) channel coding and the wireless network erasure correcting with router code to detect lost packets to reduce the bit error rate and increase the packet delivery ratio over a wireless multi-access channel. B-JNCC is applied at the receiver node. Simulation results show that the proposed B-JNCC outperforms the binary symbol-wise JNCC, non-binary JNCC (NB-JNCC), and NB separate network-channel coding.

High-Mobility Multi-Modal Sensing for IoT Network via MIMO AirComp: A Mixed-Timescale Optimization Approach

Multiple-input-multiple-output (MIMO) over-the-air Computation (AirComp) is a promising technique for implementing high-mobility multi-modal sensing (HMS) to compute multiple target functions of distributed data by leveraging the superposition property of wireless multi-access channel, thereby significantly improving the computation efficiency as compared to the traditional orthogonal multi-access scheme. However, two obstacles constraining the potential gain of MIMO AirComp are high hardware cost and power consumption, and huge channel state information (CSI) signaling overhead. To overcome these challenges, we propose a novel mixed-timescale hybrid combining (MHC) scheme to minimize the average mean-square error (MSE) under some practical constraints in the uplink of a IoT network. Specifically, the radio frequency (RF) combiner is adapted to the long-term statistical CSI, while the baseband combiner is adapted to the real-time effective CSI. In order to tackle the resulting stochastic nonconvex optimization problem, we devise a mixed-timescale stochastic successive convex approximation (MSCA) algorithm. Simulation results are presented to verify the effectiveness of our proposed scheme over the state-of-the-art baselines.

Joint MAC and rate control for stability and delay in wireless multi-access channels

In this paper, we investigate the stability and delay issues of a two-user multi-access channel at the bit level. The two users, have the option to transmit at a higher instantaneous rate separately, or to transmit simultaneously but at a lower individual instantaneous rate due to interference caused by concurrent transmissions. Traffic burstiness is considered by modeling random arrivals at the users, and the stability region in terms of bits/slot is derived. Further, we determine the condition under which the maximum stability region is achieved when the users always transmit if their queues are non-empty; in this case, the stability region is shown to be convex. Subsequently, we study the minimum delivery time problem where each user is allocated an initial amount of traffic volume to be sent to the destination. For any initial queue size vector, we explicitly characterize the optimal policy that empties the two users’ queues within the shortest time.

Delay Optimal Transmission Policy in a Wireless Multiaccess Channel

In this correspondence, we consider the problem of delay optimal rate allocation in a (potentially asymmetric) multiaccess channel. The rate feasibility region of such a network is well studied and is shown to be of a polymatroid structure. We consider this problem with unsaturated sources, i.e., jobs arrive at sources at random times and the source has the possibility of being empty. In such a setting, all stable rate allocation policies result in a throughput matched with the average arrival rate. Hence, we are interested in rate allocation policies that minimize expected delay in the system. In this correspondence, we show that a policy of threshold type is optimal in minimizing the average queueing delay. We study the average delay criterion as the limit of an infinite-horizon discounted cost function when the discount factor approaches 1.