Abstract Simultaneous wireless information power transfer with power splitting is a new technology for energy harvesting that splits the received signal into two streams with adjustable power levels for information decoding and energy saving. Traditionally, decode and forward (DF) and amplify and forward (AF) schemes are used for energy harvesting in three‐step two‐way relay networks (TWRNs). In this paper, piece‐wise and forward (PF) scheme are applied with power splitting for energy harvesting in TWRNs. In PF, the received signal from each source at relay is compared to an adaptive threshold, if the signal strength is above the threshold, the relay functions similar to DF scheme, otherwise, the relay transmits a particular combination of its received signals from the sources. Next, the outage performance of PF protocol in a three‐step TWRN is studied and optimal lossless energy saving (OLES) method are presented to minimise the outage probability by computing the optimum power splitting ratio. The simulation and analytical results indicate that the outage probability of PF can be significantly improved using OLES technique in comparison with other related relaying schemes. The results show that the outage probability of PF‐OLES is 13, 3, and lower than AF‐OLES, DF‐OLES, and DAPS [1], respectively.

Identifying whether the wireless channel between two devices (e.g., a base station and a client device) is Line-of Sight (LoS) or non-Line-of-Sight (nLoS) has many applications, e.g., it can be used in device localization. Prior works have addressed this problem, but they are primarily limited to sub6 GHz systems, assume sophisticated radios on the devices, incur additional communication overhead, and/or are specific to a single class of devices (e.g., a specific smartphone). In this paper, we address this channel classification problem for wireless devices with mmWave radios. Specifically, we show that existing beamforming training messages that are exchanged periodically between mmWave wireless devices can also be used in a deep learning model to solve the channel classification problem with no additional overhead. We then extend our work by developing a transfer learning model (t-LNCC) that is trained on simulated data, and can successfully solve the channel classification problem on any commercial-off-the-shelf (COTS) mmWave device with/without any real-world labeled data. The accuracy of t-LNCC is more than 95% across three different COTS wireless devices, when there is a small sample of labeled data for each device. We finally show the application of our classification problem in estimating the distance between two wireless devices, which can be used in localization.

Millimeter-wave (mmWave) systems use highly directional beams with narrow beamwidths to overcome the high path loss associated with their frequency bands. The use of narrow beams complicates the link establishment process as the transmitter and receiver need to search for appropriate beams before they can communicate with each other. Existing mmWave standards address the beam search process as part of the initial access (IA), and use contention based schemes that let multiple clients train their beams in the same search interval. However, there exists a severe power imbalance among competing clients' beams, as clients naturally have different orientations and are at different distances from the same access point. This beam power imbalance coupled with poor contention protocols results in poor IA fairness in dynamic systems with multiple clients. We propose a joint power control and contention adaptation protocol (coined JPOC) that addresses this unfairness problem. JPOC uses an open-loop and client-side power control mechanism that reduces the beam power imbalance among competing clients. It also uses a model-driven contention adaptation protocol that optimally adjusts the duration of the contention time according to the system dynamics. Comprehensive evaluation through a mixture of experiments and simulations show that compared to existing 802.11 ad/ay standards, JPOC substantially reduces the contention overhead and increases the IA fairness.

We present the design and implementation of PAFD, a design methodology that enables full-duplex (FD) in hybrid beamforming systems with constant amplitude phased array antennas. The key novelty in PAFD's design is construction of analog beamformers that maximize the beamforming gains in the desired directions while simultaneously reducing the self-interference (SI). PAFD is implemented on the WARP platform, and its performance is extensively evaluated in both indoor and outdoor environments. Our experimental results reveal that (i) PAFD sacrifices a few dB in beamfomring gain to provide large amounts of reduction in SI power; (ii) the reduction in SI is dependent on the number of phased array antennas and increases as the number of antennas increases; and (iii) finally, PAFD significantly outperforms half-duplex (HD) for small cells even in presence of high interference caused by uplink clients to the downlink clients. The gains increase with a larger array size or less multipath in the propagation environment.

Bounds on the capacity of wireless networks often rely on simplifying assumptions and are given in terms of coarse network parameters, such as the number of nodes. While useful due to their simplicity, such bounds can significantly overestimate the achievable capacity in real-world situations, ignoring actual network topology and traffic patterns. The results of this paper improve such analytical results on network capacity in several ways. At the heart of our methodology lies the concept of transmission arenas, which indicates the presence of active transmissions near any given location in the network. This novel space-based approach is well-suited to untangle the interactions of simultaneous transmissions. Avoiding a graph-based model of the network, it opens new avenues of studying capacities. For homogeneous networks, we recover classical bounds. However, our methodology applies to arbitrary networks and can, thus, inform placing and activating of nodes also in the presence of clustering. Our method works with all classical channel models and dimensions. It provides bounds on the transport capacity which involve only high-level knowledge of node locations, such as the length of Euclidean minimum spanning tree. As an additional novelty, we establish bounds on wireless unicast and multicast capacities.

We study the resource allocation problem in RAN-level integrated HetNets. This emerging HetNets paradigm allows for dynamic traffic splitting across radio access technologies for each client, and then for aggregating the traffic inside the network to improve the overall resource utilization. We focus on the max-min fair service rate allocation across the clients, and study the properties of the optimal solution. Based on the analysis, we design a low complexity distributed algorithm that tries to achieve max-min fairness. We also design a hybrid network architecture that leverages opportunistic centralized network supervision to augment the distributed solution. We analyze the performance of our proposed algorithms and prove their convergence. We also derive conditions under which the outcome is optimal. When the conditions are not satisfied, we provide constant upper and lower bounds on the optimality gap. Finally, we study the convergence time of our distributed solution and show that leveraging appropriate policies in its design significantly reduces the convergence time.

In this paper, the authors show that using physical layer network coding (PNC) in wireless relay systems does not always increase throughput performance, and to achieve maximum throughput, PNC should be exploited intelligently regarding to the channel conditions. In this study, they propose a selective PNC (SPNC) scheme in bidirectional relay systems for binary phase shift keying. In the proposed scheme, the relay chooses either to detect network‐coded data from superimposed signal by PNC scheme or single source data by a so‐called single node detection (SND) scheme, based on which offers more end‐to‐end throughput for current channel realisation. They analytically determine the region of channel state plane in which SPNC selects SND scheme. Also, they obtain expressions for instantaneous bit error rates (BERs) of PNC and SND protocols. Moreover, they derive upper bounds in closed‐form for average BERs of PNC and SND schemes during multiple access phase at the relay. Then, they investigate SPNC with high‐order modulations. Simulations and analytical results confirm that SPNC achieves considerable throughput gain over PNC in Rayleigh fading channels.

We study the dynamics of network selection in heterogeneous wireless networks based on client-side control. Clients in such networks selfishly select the best radio access technology (RAT) that maximizes their own throughputs. We study two general classes of throughput models that capture the basic properties of random access (e.g., Wi-Fi) and scheduled access (e.g., WiMAX, LTE, and 3G) networks. Formulating the problem as a non-cooperative game, we study its existence of equilibria, convergence time, efficiency, and practicality. Our results reveal that: 1) single-class RAT selection games converge to Nash equilibria, while an improvement path can be repeated infinitely with a mixture of classes; 2) we provide tight bounds on the convergence time of these games; 3) we analyze the Pareto-efficiency of the Nash equilibria of these games, deriving the conditions under which Nash equilibria are Pareto-optimal, and quantifying the distance of equilibria with respect to the set of Pareto-dominant points when the conditions are not satisfied; and 4) with extensive measurement-driven simulations, we show that RAT selection games converge to Nash equilibria in a small number of steps, and are amenable to practical implementation. We also investigate the impact of noisy throughput estimates, and propose solutions to handle them.

We present the design and implementation of FD2, a directional full-duplex (FD) communication system for indoor wireless networks. An FD2AP uses directional transmit and receive antennas to reduce self-interference, and to combat AP-AP and client-client interferences that arise due to FD operation in multi-cell networks. FD2addresses the joint problem of scheduling and beam selection by proposing efficient practical algorithms. FD2is implemented on the WARP platform, and its performance is compared against CSMA/CA and other FD and directional communication systems. Our experimental results reveal that: (i) Simple application of FD to multi-cell networks can result in significant loss of capacity due to high FD induced interference, while FD2can effectively overcome the problem and provide an average gain of ninefold; (ii) FD2's performance depends on the hardware capture properties and the corresponding rate table, and increases when packets can be captured at lower SINR margins, or when dynamic range of the rate table is high; and (iii) FD2's uplink and downlink performances are susceptible to channel dynamics, and are impacted differently due to mobility. However, we show that training FD2's rates according to traffic direction, mobility, and feedback rate, increases its robustness to channel dynamics.

We study the convergence properties of distributed network selection in HetNets with priority-based service. Clients in such networks have different priority weights (e.g., QoS requirements, scheduling policies, etc.) for different access networks and act selfishly to maximize their own throughput. We formulate the problem as a non-cooperative game, and study its convergence for two models: (i) A purely client-centric model where each client uses its own preference to select a network, and (ii) a hybrid client-network model that uses a combination of client and network preferences to arrive at pairings. Our results reveal that: (a) Pure client-centric network selection with generic weights can result in infinite oscillations for any improvement path (i.e., shows strongly cyclic behavior). However, we show that under several classes of practical priority weights (e.g., weights that achieve different notions of fairness) or under additional client-side policies, convergence can be guaranteed; (b) We study convergence time under client-centric model and provide tight polynomial and linear bounds; (c) We show that applying a minimal amount of network control in the hybrid model, guarantees convergence for clients with generic weights. We also introduce a controllable knob that network controller can employ to balance between convergence time and its network-wide objective with predictable tradeoff.

In this paper, we explore fundamental limitations of the benefit of network coding in multihop wireless networks. We study two well-accepted scenarios in the field: single multicast session and multiple unicast sessions. We assume arbitrary but fixed topology and traffic patterns for the wireless network. We prove that the gain of network coding in terms of throughput and energy saving of a single multicast session is at most a constant factor. Also, we present a lower bound on the average number of transmissions of multiple unicast sessions under any arbitrary network coding. We identify scenarios under which network coding provides no gain at all, in the sense that there exists a simple flow scheme that achieves the same performance. Moreover, we prove that the gain of network coding in terms of the maximum transport capacity is bounded by a constant factor of at most $(\pi)$ in any arbitrary wireless network under all traditional Gaussian channel models. As a corollary, we find that the gain of network coding on the throughput of large homogeneous wireless networks is asymptotically bounded by a constant. Furthermore, we establish theorems which relate a network coding scheme to a simple routing scheme for multiple unicast sessions. The theorems can be used as criteria for evaluating the potential gain of network coding in a given wired or wireless network. Based on these criteria, we find more scenarios where network coding has no gain on throughput or energy saving.

We study the dynamics of network selection in heterogeneous wireless networks (HetNets). Users in such networks selfishly select the best radio access technology (RAT) with the objective of maximizing their own throughputs. We propose two general classes of throughput models that capture the basic properties of random access (e.g., Wi-Fi) and scheduled access (e.g., WiMAX, LTE, 3G) networks. Next, we formulate the problem as a non-cooperative game, and study its convergence, efficiency, and practicality. Our results reveal that: (i) Single-class RAT selection games converge to Nash equilibria, while an improvement path can be repeated infinitely with a mixture of classes. We next introduce a hysteresis mechanism in RAT selection games, and prove that with appropriate hysteresis policies, convergence can still be guaranteed; (ii) We analyze the Pareto-efficiency of the Nash equilibria of these games. We derive the conditions under which Nash equilibria are Pareto-optimal, and we quantify the distance of Nash equilibria with respect to the set of Pareto-dominant points when the conditions are not satisfied; (iii) Finally, with extensive measurement-driven simulations we show that RAT selection games converge to Nash equilibria in a small number of steps, and hence are amenable to practical implementation. We also investigate the impact of noisy throughput measurements, and propose solutions to handle them.

In this paper we address the problem of multicasting individual and common information from a single transmitter (source) to multiple receivers (destinations). We consider the general case where a separate message could be intended for any subset of destinations. We call this problem multicasting in broadcast channel. We derive an achievable rate region for this setup when the `deterministic channel model' is employed. The achievable region is a function of link transfer functions and is characterized by a set of linear inequalities. We show that the obtained region is computable and convex. Moreover, we investigate the tightness of our achievable rate region for some well-known scenarios with up to three destinations. We verify that the derived multicast achievable regions for these particular cases coincide with the best known results in the literature which in fact characterize the multicast capacity regions for these scenarios.

In this paper we introduce a novel linear network coding scheme, namely “rotate-and-add coding”, that possesses low encoding complexity and operates fundamentally different from the traditional network codes. This scheme can operate on a small field (e.g. F2), thereby, it alleviates the computational complexities due to multiplication and addition operations in large finite fields. The key idea is to function on a vector of symbols instead of working with a single symbol of a large field. Each node encodes its received vectors by simply rotationally shifting the vectors and then adding them, i.e., here the addition is done in vector form and the multiplication is replaced by rotation. We verify that the new scheme requires lower computation and overhead than the existing schemes. However, as the cost of reducing the complexity, it provides slightly smaller throughput.

In this paper we establish fundamental limitations to the benefit of network coding in terms of energy and throughput in multihop wireless networks. Thereby we adopt two well accepted scenarios in the field: single multicast session and multiple unicast sessions. Most of our results apply to arbitrary wireless network and are, in particular, not asymptotic in kind. In terms of throughput and energy saving we prove that the gain of network coding of a single multicast session is at most a constant factor. Also, we present a lower bound on the expected number of transmissions of multiple unicast sessions under an arbitrary network coding. We identify scenarios for which the network coding gain for energy saving becomes surprisingly close to 1, in some cases even exactly 1, corresponding to no benefit at all. Interestingly, we prove that the gain of network coding in terms of transport capacity is bounded by a constant factor pi in any arbitrary wireless network and for all traditional channel models. This shows that the traditional bounds on the transport capacity [1]-[4] do not change more than constant factor pi if we employ network coding. As a corollary, we find that the gain of network coding on the throughput of large scale homogeneous wireless networks is asymptotically bounded by a constant. Note that our result is more general than the previous work [5] and it is obtained by a different technique. In conclusion, we show that in contrast to wired networks, the network coding gain in wireless networks is constraint by fundamental limitations.

This paper addresses the dynamics of broadcast flooding in random wireless ad hoc networks. In particular, we study the subset of nodes covered by a flood as well as timing issues related to the first (latency) and the last time (duration of back-chatter) at which a broadcast is received by a fixed node. Notably, this analysis takes into account the MAC-layer as well as background traffic which both are often neglected in related studies. Assuming a protocol model for the transmission channel which accounts for carrier sensing and interference, we find bounds for the probability of survival of the flood and for its coverage probabilities. Moreover, under certain conditions on the parameters, we establish asymptotical linear bounds on the latency as the distance from the origin of the flood increases and show that the duration of the back-chatter is stochastically bounded. The analytical results are compared to simulation.

Existing work on the capacity of wireless networks predominantly considers homogeneous random networks with random work load. The most relevant bounds on the network capacity, e.g., take into account only the number of nodes and the area of the network. However, these bounds can significantly overestimate the achievable capacity in real world situations where network topology or traffic patterns often deviate from these simplistic assumptions. To provide analytically tractable yet asymptotically tight approximations of network capacity we propose a novel space-based approach. At the heart of our methodology lie simple functions which indicate the presence of active transmissions near any given location in the network and which constitute a tool well suited to untangle the interactions of simultaneous transmissions. We are able to provide capacity bounds which are tighter than the traditional ones and which involve topology and traffic patterns explicitly, e.g., through the length of Euclidean Minimum Spanning Tree, or through traffic demands between clusters of nodes. As an additional novelty our results cover unicast, multicast and broadcast and are asymptotically tight. Notably, our capacity bounds are simple enough to require only knowledge of node location, and there is no need for solving or optimizing multi-variable equations in our approach.

In this paper we study the broadcast capacity of multihop wireless networks which we define as the maximum rate at which broadcast packets can be generated in the network such that all nodes receive the packets successfully in a limited time. We employ the Protocol Model for successful packet reception usually adopted in network capacity studies and provide novel upper and lower bounds for the broadcast capacity for arbitrary connected networks. In a homogeneous dense network these bounds simplify to Θ(W/max(1,Δd)) where W is the wireless channel capacity, Δ the interference parameter, and d the number of dimensions of space in which the network lies. Interestingly, we show that the broadcast capacity does not change by more than a constant factor when we vary the number of nodes, the radio range, the area of the network, and even the node mobility. To address the achievability of capacity, we demonstrate that any broadcast scheme based on a backbone of size proportional to the Minimum Connected Dominating Set guarantees a throughput within a constant factor of the broadcast capacity. Finally, we demonstrate that broadcast capacity, in stark contrast to unicast capacity, does not depend on the choice of source nodes or the dimension of the network.

This paper develops a novel color-based broadcast scheme for wireless ad hoc networks where each forwarding of the broadcast message is assigned a color from a given pool of colors. A node only forwards the message if it can assign it a color from the pool which it has not already overheard after a random time. In the closely related counter-based broadcast scheme a node simply counts the number of broadcasts not the colors overheard. The forwarding nodes form a so-called backbone, which is determined by the random timers and, thus, is random itself. Notably, any counter-generated backbone could result from pruning a color-generated backbone; the typical color-generated backbone, however, exhibits a connectivity graph richer than the counter-based ones. As a particular advantage, the colors reveal simple geometric properties of the backbones which we exploit to prove that the size of both, color- and counter-generated back-bones are within a small constant factor of the optimum. We also propose two techniques, boosting and edge-growing, that improve the performance of color- and counter-based broadcast in terms of reachability and number of rebroadcasts. Experiments reveal that the powerful boosting method is considerably more effective with the color-based schemes.