Compute-and-forward (C&F) recently finds new applications in random-access networks focusing on the single access point (AP) scenario. In this paper, we extend the use of C&F from the single AP scenario to the multi-AP scenario. To achieve this, we identify two major challenges and propose two novel solutions. First, we introduce an AP cooperation problem and develop an efficient distributed algorithm. Second, we introduce a joint channel estimation and active user recovery problem and propose a solution based on spare recovery techniques. In addition, we provide accurate throughput and delay expressions for C&F-based carrier-sense multiple access (CSMA) protocols. These expressions, together with our trace-driven simulations, demonstrate the significant advantages of C&F-based CSMA over conventional CSMA in the multi-AP scenario.

The problem of carrier sense multiple access (CSMA) with multi-packet reception (MPR) is studied. Most prior work has focused on the homogeneous case, where all the mobile users are assumed to have identical packet arrival rates and transmission probabilities. The inhomogeneous case remains largely open in the literature. In this paper, we make a first step toward this open problem by deriving throughput and delay expressions for inhomogeneous CSMA, with a particular focus on a family of MPR models. This family of MPR models, which allows us to overcome several challenges associated with conventional analysis, is general enough to include a number of interesting MPR techniques-such as successive interference cancellation, compute-and-forward (C&F), and successive C&F (SCF)-as special cases. Based on these throughput and delay expressions, we provide theoretical guidelines for the network design to meet quality-of-service requirements and to achieve global stability; we also evaluate the performances of various MPR techniques, highlighting the clear advantages offered by SCF.

The benefit of applying compute-and-forward (C&F) to slotted ALOHA (S-ALOHA) systems is studied. A Markov chain model is introduced, and an approximate stability region is given. It is shown that the approximate region is asymptotically exact as the number of users tends to infinity. It is also shown that the approximate region is very accurate even for systems with a small number of users. Further, based on the approximate region, simple expressions for the throughput and delay performance of S-ALOHA with C&F are derived, demonstrating the significant advantages offered by C&F.

In this paper, we propose to use binary consensus algorithms for distributed cooperative spectrum sensing in cog-nitive radio networks. We propose to use two binary approaches, namely diversity and fusion binary consensus spectrum sensing. The performance of these algorithms is analyzed over fading channels. The probability of networked detection and false alarm are characterized for the diversity case. We then show that binary consensus cooperative spectrum sensing is superior to quantized average consensus in terms of agility, given the same number of transmitted bits.