Age of information, as a metric measuring the data freshness, has drawn increasing attention due to its importance in many data update applications. Most existing studies have assumed that there is one single channel in the system. In this work, we are motivated by the plethora of multi-channel systems that are being developed, and investigate the following question: how can one exploit multi-channel resources to improve the age performance? We first derive a policy-independent lower bound of the expected long-term average age in a multi-channel system. The lower bound is jointly characterized by the external arrival process and the channel statistics. Since direct analysis of age in multi-channel systems is very difficult, we focus on the asymptotic regime, when the number of users and number of channels both go to infinity. In the many-channel asymptotic regime, we propose a class of Maximum Weighted Matching policies that converge to the lower bound near exponentially fast. In the many-user asymptotic regime, we design a class of Randomized Maximum Weighted Matching policies that achieve a constant competitive ratio compared to the lower bound. Finally, we use simulations to validate the aforementioned results.

In this paper, we study the effect of predictive caching on the delay of wireless networks. We explore the possibility of caching at wireless end-users where caches are typically very small, orders of magnitude smaller than the catalog size. We develop a predictive multicasting and caching scheme, where the Base Station (BS) in a wireless cell proactively multicasts popular content for end-users to cache, and access locally if requested. We analyze the impact of this joint multicasting and caching on the delay performance. Our analysis uses a novel application of Heavy-Traffic theory under the assumption of vanishing caches to show that predictive caching fundamentally alters the asymptotic throughput-delay scaling. This in turn translates to a several-fold delay improvement in simulations over the on-demand unicast baseline as the network operates close to the full load. We highlight a fundamental delay-memory trade-off in the system and identify the correct memory scaling to fully benefit from the network multicasting gains.

A fundamental challenge in wireless multicast has been how to simultaneously achieve high-throughput and low-delay for reliably serving a large number of users. In this paper, we show how to harness substantial throughput and delay gains by exploiting multi-channel resources. We develop a new scheme called Multi-Channel Moving Window Codes (MC-MWC) for multi-channel multi-session wireless multicast. The salient features of MC-MWC are three-fold. (i) High throughput: we show that MC-MWC achieves order-optimal throughput in the many-user many-channel asymptotic regime. Moreover, the number of channels required by a conventional channel-allocation based scheme is shown to be doubly-exponentially larger than that required by MC-MWC. (ii) Low delay: using large deviations theory, we show that the delay of MC-MWC decreases linearly with the number of channels, while the delay reduction of conventional schemes is no more than a finite constant. (iii) Low feedback overhead: the feedback overhead of MC-MWC is a constant that is independent of both the number of receivers in each session and the number of sessions in the network. Finally, our trace-driven simulation and numerical results validate the analytical results and show that the implementation complexity of MC-MWC is low.

Full duplex techniques can potentially double the channel capacity and achieve lower delays by empowering two radios to simultaneously transmit in thesame frequency band.However, full duplex is only available between two adjacent nodes within the communication range. In this paper, we present BiPass to break this limitation.With the help of full duplex capable relays, weenable simultaneous bidirectional in-band cut-through transmissions between two far apart nodes, so they can do full duplex communications as if they were within each other's transmission range.To design such a system, we analyze interference patterns and propose a loop-back interference cancellation strategy. We identify the power amplification problem at relay nodes and develop an algorithm to solve it. We also develop a routing algorithm, an opportunistic forwarding scheme, and a real-time feedback strategy to leverage this system in ad-hoc networks.To evaluate the real world performance of BiPass, we build a prototype and conduct experiments using software defined radios. We show that BiPass can achieve 1.6x median throughput gain over state-of-the-art one-way cut-through systems, and 4.09x gain over the decode-and-forward scheme. Our simulations further reveal that even when the data traffic is not bidirectional, BiPass has 1.36x throughput gain and 47\% delay reduction overone-way cut-through systemsin large networks.

An essential step for achieving multiplexing gain in MIMO downlink systems is to collect accurate channel state information (CSI) from the users. Traditionally, CSIs have to be collected before any data can be transmitted. Such a sequential scheme incurs a large feedback overhead, which substantially limits the multiplexing gain especially in a network with a large number of users. In this paper, we propose a novel approach to mitigate the feedback overhead by leveraging the recently developed Full-duplex radios. Our approach is based on the key observation that using Full-duplex radios, when the base-station (BS) is collecting CSI of one user through the uplink channel, it can use the downlink channel to simultaneously transmit data to other (non-interfering) users for which CSIs are already known. By allowing concurrent channel probing and data transmission, our scheme can potentially achieve a higher throughput compared to traditional schemes using Half-duplex radios. The new flexibility introduced by our scheme, however, also leads to fundamental challenges in achieving throughout optimal scheduling. In this paper, we make an initial effort to this important problem by considering a simplified group interference model. We develop a throughput optimal scheduling policy with complexity O((N/I)I), where N is the number of users and I is the number of user groups. To further reduce the complexity, we propose a greedy policy with complexity O(N log N) that not only achieves at least 2/3 of the optimal throughput region, but also outperforms any feasible Half-duplex solutions. We derive the throughput gain offered by Full-duplex under different system parameters and show the advantage of our algorithms through numerical studies.

For a multicast group of n receivers, existing techniques either achieve high throughput at the cost of prohibitively large (e.g., O(n)) feedback overhead, or achieve low feedback overhead but without either optimal or near-optimal throughput guarantees. Simultaneously achieving good throughput guarantees and low feedback overhead has been an open problem and could be the key reason why wireless multicast has not been successfully deployed in practice. In this paper, we develop a novel anonymous-query based rate control, which approaches the optimal throughput with a constant feedback overhead independent of the number of receivers. In addition to our theoretical results, through implementation on a software-defined ratio platform, we show that the anonymous-query based algorithm achieves low-overhead and robustness in practice.

A major challenge of wireless multicast is being able to support a large number of users while simultaneously maintaining low delay and low feedback overhead. In this paper, we develop a joint coding and feedback scheme named moving window network coding with anonymous feedback (MWNC-AF) that simultaneously achieves constant decoding delay and constant feedback overhead, irrespective of the number of receivers n, without sacrificing either throughput or reliability. We explicitly characterize the asymptotic decay rate of the tail probability of the decoding delay and prove that injecting a fixed amount of information bits into the MWNC-AF encoder buffer in each time slot (called “constant data injection process”) achieves the fastest decay rate, thus showing how to obtain delay optimality in a large deviation sense. We then investigate the average decoding delay of MWNC-AF and show that, when the traffic load approaches capacity, the average decoding delay under the constant injection process is at most one half of that under a Bernoulli injection process. We prove that the per-packet encoding and decoding complexities of MWNC-AF both scale as O(logn) and are thus insensitive to the increase of the number of receivers n. Our simulations further underscore the performance of our scheme through comparisons with existing schemes and show that the delay, encoding, and decoding complexities are low even for a large number of receivers, demonstrating the efficiency, scalability, and ease of implementability of MWNC-AF.