IEEE 802.11ax partitions a regular 20MHz channel into smaller sub-channels called resource units to support simultaneous multiuser operation using orthogonal frequency division multiple access (OFDMA). Uplink OFDMA random access (UORA) in IEEE 802.11ax allows stations to transmit via a scheduled random access mechanism. UORA is initiated via a trigger frame which aside from serving as a synchronization mechanism, also informs stations which resource units are allowed for random access. Using the trigger frame information, the stations engage in an OFDMA backoff process to win access to a resource unit. Similar to slotted ALOHA, the maximum normalized throughput of UORA is only 37 percent due to high probability of collisions at high loads. To reduce collisions, we equip UORA with carrier sensing capability resulting in anewuplink hybrid UORA (H-UORA) OFDMA access mechanism. Unlike other multi-carrier CSMA methods previously proposed in literature, H-UORA is an easily implementable modification to current 802.11ax WLANs. We show that H-UORA can achieve a normalized throughput of at least 80 percent (which increases further depending on the buffering capabilities of the access point) using various numerical analysis and simulations.

This paper investigates the impact of radar interference on LTE by directly injecting narrow-band radar waveforms into a state of the art LTE down-link simulator. We show detailed results demonstrating the impact of interference on various parts of LTE signaling including reference symbols. Then we show how the presence of radar interference causes errors in the noise and interference estimates, impacts decoding in the LTE soft decoder, and ultimately leads to erroneously received Sub-frames. We propose a mechanism to combat such interference and improve LTE throughput in the presence of narrow-band radar using selective erasures. We simulate the proposed scheme and show that it is extremely effective in mitigating narrow-band interference and realizing the throughput expected from LTE.

We propose wide-band spectrum sensing scheduling solutions for cognitive radios that are equipped with reconfigurable RF front-ends. The wide frequency spectrum of interest is segmented into frequency sub-bands due to software and hardware limitations. These sub-bands can be non-contiguous, and each may contain an arbitrary number of channels from an arbitrary number of systems. It is assumed that the CR can only sense one sub-band at a time. Three sub-band selection policies are proposed to find spectrum opportunities taking into account realistic hardware reconfiguration energy consumptions and time delays. Two of the proposed policies rely on the individual channel Markov properties and the sub-band Markov properties, respectively. Although these two policies may achieve good performance, they rely on complete knowledge of RF environment dynamics and thus may become computationally demanding. The third sub-band selection policy based on Q-learning is proposed to circumvent this. Performance of the three policies are compared and discussed against a performance upper-bound of the optimal solution to the corresponding partially observable Markov decision process formulation. The suitability of the Q-learning technique is validated by showing that it achieves good performance through numerical results in both simulated and real measured RF environments.

In dense IEEE 802.11 networks, improving the efficiency of contention-based media access control is an important and challenging issue. Recently, the IEEE802.11ah Task Group has discussed a group-synchronized distributed coordination function (GS-DCF) for densely deployed wireless networks with a large number of stations. By using the restricted access window (RAW) and RAW slots, the GS-DCF is anticipated to improve the throughput substantially, primarily due to relieving the channel contention. However, optimizing the MAC configurations for the RAW, i.e., the number and duration of RAW slots, is still an open issue. In this paper, we first build an analytical model to track the performance of the GS-DCF in saturated 802.11 networks. Then, we study and compare the GS-DCF throughput using both centralized and decentralized grouping schemes. The accuracy of our model has been validated with simulation results. It is observed that the GS-DCF obtains a throughput gain of seven times or more over DCF in a network of 512 or more stations. Moreover, it is demonstrated that the decentralized grouping scheme can be implemented with a small throughput loss when compared with the centralized grouping scheme.

Spectrum sensing scheduling policies are proposed to find spectrum opportunities for wide-band cognitive radios by taking into account realistic reconfiguration energy consumptions and time delays. The first policy relies on the RF environment Markov properties. Thus, it may become computationally demanding. The second sub-band selection policy based on Q-learning is proposed to circumvent this. Performance of the two policies are compared and discussed against a performance upper-bound of the optimal solution to the corresponding partially observable Markov decision process formulation. The suitability of the Q-learning technique is validated by showing that it achieves good performance in simulation.

A major challenge in low power sensor networks is collecting data from large number of nodes with low latency. Access point (AP) typically needs to poll large number of nodes individually and schedule transmission times for each of the nodes resulting in large latency. In this paper, we propose a physical layer technique that parallelizes the polling response from multiple nodes (so called parallel acknowledgment) in order to decrease the latency. Each node responds to the poll request of an AP by using the well-known Zadoff-Chu (ZC) sequences. By using the orthogonal properties of the ZC sequences, the AP is able to resolve the multiple signatures of different nodes simultaneously. We further develop low complexity transmission scheme for the ZC sequences in order to reduce the number of computations and complexity required by the low power sensor nodes. We also compare the performance of both time and frequency domain receivers.

The potential of successful cognitive radio networks operating in TV White Spaces (and other future bands re-allocated for unlicensed operation) has led to significant upsurge of interest in their design optimization - particularly those that are cross-layer in nature, involving both MAC protocols as well as physical layer aspects such as channel sensing. Typically, these seek to optimize a network-level metric (notably, aggregate throughput) of secondary (cognitive) network subject to interference constraints on the primary. In turn, this requires suitable sensing by cognitive users to detect availability of primary channels (currently unused by the protected incumbents) for opportunistic usage. To date, most studies have used largely hypothetical assumptions regarding channel idleness and resulting spectrum availability due to primary user dynamics. For example, idleness of channels over any spectrum are typically assumed to be an independent and identically distributed Bernoulli sequence. In contrast, nearly all real-time measurements suggest that channel idleness is frequency dependent, i.e., the probability that a channel is idle depends on the channel location. Cognitive radio research thus increasingly needs more realistic and validated models for channel idleness as the foundation of credible cross-layer analysis; this is the primary contribution of our work. We use two sets of real-time measurements conducted in disparate geographic locations over four distinct time intervals to show that channel idleness is appropriately modeled as independent but non-identical (i.n.i.d.) Bernoulli variables characterized by p_i, the probability of idleness for the i-th channel. We validate that Beta distribution can be used for modeling the variations in channel idleness probabilities; the Beta distribution parameters are estimated from the data to produce the best model fit. Based on the validated i.n.i.d. model, we build a predictive model by computing the availability probability of k channels, i.e, P{N_{idle} = k}, where N_{idle} denotes the number of idle channels over the spectrum of N channels. However, the combinatorial complexity inherent in the computation of P{N_{idle} = k} suggests the need for efficient approximations. We accomplish this by classifying idleness of channels based on the magnitude of p_i, and propose a novel Poisson-normal approximation for computing P{N_{idle} = k}. For validation, the distribution obtained from our technique is compared with the exact distribution and normal approximation using the approximation error criterion.

In this paper, we investigate a novel slotted ALOHA-based distributed access cognitive network in which a secondary user (SU) selects a random subset of channels for sensing, detects an idle (unused by licensed users) subset therein, and transmits in any one of those detected idle channels. First, we derive a range for the number of channels to be sensed per SU access. Then, the analytical average system throughput is attained for cases where the number of idle channels is a random variable. Based on that, a relationship between the average system throughput and the number of sensing channels is attained. Subsequently, a joint optimization problem is formulated in order to maximize average system throughput. The analytical results are validated by substantial simulations.

Cognitive radio networks require fast and reliable spectrum sensing to achieve high network utilization by secondary users. Current optimization approaches to spectrum sensing to-date have focussed on maximizing aggregate throughput while considering only a single parameter variable pertinent to sensing, notably the threshold or duration, but not both. In this work, we consider joint minimization of the average detection time for finding a spectrum hole as a function of both parameters. We show that the resulting non-convex problem is actually biconvex under practical conditions and solve for a global optimum detection time. Numerical results show that the proposed approach can considerably improve system performance in terms of the mean time to detect a spectrum hole.

In this paper, we propose a novel spectrum occupancy model designed to generate accurate temporal and frequency behavior of various wireless transmissions. Our proposed work builds upon existing concepts in open literature in order to develop a more accurate time-varying spectrum occupancy model. This model can be employed by wireless researchers for evaluating new wireless communication and networking algorithms and techniques designed to perform dynamic spectrum access (DSA). Using statistical characteristics extracted from actual radio frequency measurements, first- and second-order parameters are employed in a statistical spectrum occupancy model based on a combination of several different probability density functions (PDFs) defining various features of a specific spectrum band with several concurrent transmissions. To assess the accuracy of the model, the output characteristics of the proposed spectrum occupancy model are compared with realtime radio frequency measurements in the television and paging bands.

In this paper, we provide a new perspective for analyzing spectrum occupancy by introduced an M/M/l queueing model to generate accurate temporal and frequency behavior of various wireless transmissions. Our proposed research builds upon existing concepts in the open literature in order to develop a more accurate time-varying spectrum occupancy model. This model can be employed by wireless researchers for evaluating new wireless communication and networking algorithms and techniques designed to perform dynamic spectrum access (DSA). Using statistical characteristics extracted from actual radio frequency measurements, first- and second-order parameters are employed in a statistical spectrum occupancy model based on a combination of several different probability density functions (PDFs) defining various features of a specific spectrum band with several concurrent transmissions. To assess the accuracy of the model, the output characteristics of the proposed spectrum occupancy model are compared with real-time radio frequency measurements in the paging and ISM bands.

We propose a novel graph theoretic matching algorithm to model the dynamic channel allocation problem in cognitive radio networks. In the past, no research work has considered both the channels and the user request priorities in the context of spectrum allocation. In our work, we define a unique model to assign priorities among the channels in a spectrum based on the transmission power allowed in each channel to mitigate interference on the licensed primary users. Crisp logic lines categorize requests into distinct priorities based on the user requirements. These prioritized channels and user requests then constitute the set of vertices V of a bi-partite graph G = (V,E), whose set of edges E represent the communication links between the channels and the users. We propose a novel O(|E|) priority-satisfying matching algorithm to design an allocation scheme to satisfy the quality of service guarantees of the unlicensed secondary users. We evaluate the performance of our proposed algorithm based on critical parameters such as latency and packet dropping probability.

The allocation of channel bandwidth without significantly increasing the interference among existing users is a challenge for the future UWB networks. Our research work presents a novel rake optimization and power aware scheduling (ROPAS) architecture for UWB networks. We propose to use cognitive radio (CR) for dynamic channel allocation among the requesting users while limiting the average power transmitted in each sub-band. In our proposed ROPAS architecture, dynamic channel allocation is achieved by a CR-based cross-layer design between the PHY and medium access control (MAC) layers. The joint power control and link scheduling in the MAC layer reduces adjacent channel and multi-access interference introduced by multiple access of the links. Furthermore, the Rake optimization with its strategic selection of propagation paths achieves the minimal bit-error rate (BER) at the rake receiver by mixed integer programming. Our simulated-based performance evaluation confirms the effectiveness and novelty of our proposed ROPAS architecture.

A wireless sensor network typically consists of a dense deployment of sensor nodes to achieve higher resolution and better network coverage. Having a dense network increases the fault-tolerance and robustness of the system. However, if not properly handled, it can lead to more collisions during transmission and also network congestion. Furthermore, wireless communication is inherently unpredictable and error-prone. Hence, it is imperative to design an efficient medium access control (MAC) protocol that facilitates guaranteed delivery of data over unreliable wireless links. In this paper, we have designed an on-demand reliable MAC (RMAC) protocol that enables timely delivery of data. We have demonstrated its superior performance over existing reliability-enforcing approaches in terms of reliability, latency, scalability and energy-efficiency