We consider the problem of routing packets across a multi-hop network consisting of multiple sources of traffic and wireless links while ensuring bounded expected delay. Each packet transmission can be overheard by a random subset of receiver nodes among which the next relay is selected opportunistically. The main challenge in the design of minimum-delay routing policies is balancing the trade-off between routing the packets along the shortest paths to the destination and distributing the traffic according to the maximum backpressure. Combining important aspects of shortest path and backpressure routing, this paper provides a systematic development of a distributed opportunistic routing policy with congestion diversity (D-ORCD). D-ORCD uses a measure of draining time to opportunistically identify and route packets along the paths with an expected low overall congestion. D-ORCD with single destination is proved to ensure a bounded expected delay for all networks and under any admissible traffic, so long as the rate of computations is sufficiently fast relative to traffic statistics. Furthermore, this paper proposes a practical implementation of D-ORCD which empirically optimizes critical algorithm parameters and their effects on delay as well as protocol overhead. Realistic QualNet simulations for 802.11-based networks demonstrate a significant improvement in the average delay over comparable solutions in the literature.

Licensed Assisted Access (LAA) represents a new paradigm for LTE, where unlicensed bands are used in conjunction with licensed bands to increase network capacity. The main challenge of LAA is ensuring that unlicensed bands are shared with WiFi systems in an efficient manner. In this paper, we study a coexistence solution based on channel selection. A centralized channel selection mechanism which increases network capacity of LTE in unlicensed bands is proposed. We also describe the implementation of our scheme using existing LTE signaling mechanisms. Simulation results show that channel selection can improve the performance of both LTE and WiFi by over 100\%.

This paper reports on a comprehensive study comparing congestion-aware routing algorithms for wireless mesh networks with a state-of-the-art shortest-path routing protocol: Link-Quality Source Routing (LQSR). In particular, a set of congestion-aware protocols in the literature, Backpressure (BP), Enhanced-Backpressure (E-BP) and Congestion Diversity Protocol (CDP) are suitably adapted for implementation on 802.11-compatible radios. A testbed consisting of 802.11g nodes is deployed to empirically compare the performance of these congestion-aware routing protocols against LQSR. The results show that, under moderate to heavy UDP traffic, CDP delivers significant improvement compared to LQSR in 80-90 percent of the instances studied, while backpressure-based routing algorithms (BP and E-BP) frequently show significant degradation with respect to LQSR for both UDP and TCP traffic.

In this paper, the multi-channel performance of large scale deployment of Long Term Evolution (LTE) in Unlicensed (LTE-U) band and WiFi using stochastic geometry is studied. LTE-U and WiFi cell placement is modeled as a Poisson Point Process (PPP), while transmission activity is modeled as hardcore simple sequential inhibition (SSI) point processes, which have been shown to accurately model Carrier sense multiple access (CSMA) access. This stochastic geometry based framework is used to analyze the co-existence of LTE-U and WiFi systems operating in the same set of available channels in unlicensed band. Several interesting results are derived. First, it is shown that fair coexistence can be achieved by LTE-U implementing a Listen-Before-Talk (LBT) protocol. Different variations of LBT are explored to improve the co-existence. Secondly, it is shown that the classical hidden node problem can severely affect performance, and that channel selection mechanisms can significantly alleviate the problem.

A distributed adaptive opportunistic routing scheme for multihop wireless ad hoc networks is proposed. The proposed scheme utilizes a reinforcement learning framework to opportunistically route the packets even in the absence of reliable knowledge about channel statistics and network model. This scheme is shown to be optimal with respect to an expected average per-packet reward criterion. The proposed routing scheme jointly addresses the issues of learning and routing in an opportunistic context, where the network structure is characterized by the transmission success probabilities. In particular, this learning framework leads to a stochastic routing scheme that optimally “explores” and “exploits” the opportunities in the network.

This work presents the Congestion Diversity Protocol (CDP), a routing protocol for multi-hop wireless networks that combines important aspects of shortest-path and backpressure routing to achieve improved end-end delay performance. In particular, CDP delivers lower end-to-end delay and fewer packet drops than existing routing protocols while maintaining equivalent throughput. This paper reports on a practical (hardware and software) implementation of CDP in an indoor WiFi network consisting of 12 802.11g nodes. This small test-bed enables an imperical comparison of CDP's performance against a set of state of the art protocols which include both congestion unaware and congestion aware routing protocols. In most topologies and scenarios we consider, CDP provides improvements for UDP traffic with respect to both end-end delay and throughput over the existing protocols.

This paper proposes a method to improve the robustness of the carrier sense system for opportunistic MAC (OMAC). This carrier sense method is specific for MIMO and the new version here illustrated is robust to noise and interference. Network simulations prove that this modified MIMO carrier sense correctly estimates whether a new communication would harm other ongoing transmissions for a wide range of system parameters.

In this paper, an adaptive opportunistic routing scheme for multi-hop wireless ad-hoc networks is proposed. The proposed scheme utilizes a reinforcement learning framework to achieve the optimal performance even in the absence of reliable knowledge about channel statistics and network model. This scheme is shown to be optimal with respect to an expected average per packet cost criterion. The proposed routing scheme jointly addresses the issues of learning and routing in an opportunistic context, where the network structure is characterized by the transmission success probabilities. In particular, this learning framework leads to a stochastic routing scheme which optimally ldquoexploresrdquo and ldquoexploitsrdquo the opportunities in the network.

In this paper, we propose a novel asynchronous media access control (MAC) protocol, opportunistic MAC (OMAC), for multiple input multiple output (MIMO) ad-hoc networks. The proposed solution is based on closed loop minimal feedback antenna selection diversity scheme and optimum receive combining. The use of antenna selection diversity contributes to a reduction in the feedback information and the effective interference produced. To utilize the spatial degrees of freedom offered by MIMO, we propose the use of a novel rank based metric to obtain interference information as well as to enable multiple simultaneous transmissions and to make MAC decisions. The rank of the interference matrix, (RI) is used as a metric. We present the performance of the proposed solution from the throughput perspective for a single hop ad-hoc wireless network. Through analysis and simulation, we found that the proposed protocol significantly outperforms 802.11 MIMO and it obtained as high spatial degree of freedom utilization as 85%.

In this paper, we consider the problem of energy efficient scheduling under average delay constraint for a single user fading channel. We propose a new approach for on-line implementation of the optimal packet scheduling algorithm. This approach is based on reformulating the value iteration equation by introducing a virtual state called post-decision state. The resultant value iteration equation becomes amenable to online implementation based on stochastic approximation. This approach has an advantage that an explicit knowledge of the probability distribution of the channel state as well as the arrivals is not required for the implementation. We prove that the on-line algorithm indeed converges to the optimal policy.

In this paper, we propose a power optimal opportunistic scheduling scheme for a multiuser single hop time division multiple access (TDMA) system. We formulate the problem of minimizing average transmission power with different minimum information theoretic rate constraints for individual users. We suggest a stochastic approximation based scheme to implement the policy and argue the convergence of this algorithm. Finally, we extend the power optimal scheduling algorithm for providing temporal fairness among users.

We present a novel scheme for uplink scheduling in WiMax networks that attempts to balance worst-case fairness in bandwidth allocation with the delay requirements of multiclass traffic, while taking the varying nature of the wireless channel into account. We assume a polling mode of operation at the base station (BS), and provide an analytical technique for obtaining an optimal polling interval k at which the BS should poll the subscriber stations (SSs) to ensure that the delay requirements of traffic are met, while the relative unfairness in bandwidth allocation remains bounded. We also devise an opportunistic deficit round robin (O-DRR) scheme that schedules sessions by taking into account the variations in the wireless channel and the delay constraints of multiclass traffic. We demonstrate that our scheduler provides fairness in bandwidth allocation and robust delay guarantees, thus giving a provider a choice of balancing fairness with delay.