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Register a new userScalable Spectrum Allocation for Large Networks Based on Sparse Optimization
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Added by
Binnan Zhuang
Publication date
2017
fields
Informatics Engineering
and research's language is
English
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Joint allocation of spectrum and user association is considered for a large cellular network. The objective is to optimize a network utility function such as average delay given traffic statistics collected over a slow timescale. A key challenge is scalability: given $n$ Access Points (APs), there are $O(2^n)$ ways in which the APs can share the spectrum. The number of variables is reduced from $O(2^n)$ to $O(nk)$, where $k$ is the number of users, by optimizing over local overlapping neighborhoods, defined by interference conditions, and by exploiting the existence of sparse solutions in which the spectrum is divided into $k+1$ segments. We reformulate the problem by optimizing the assignment of subsets of active APs to those segments. An $ell_0$ constraint enforces a one-to-one mapping of subsets to spectrum, and an iterative (reweighted $ell_1$) algorithm is used to find an approximate solution. Numerical results for a network with 100 APs serving several hundred users show the proposed method achieves a substantial increase in total throughput relative to benchmark schemes.
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A scalable framework is developed to allocate radio resources across a large number of densely deployed small cells with given traffic statistics on a slow timescale. Joint user association and spectrum allocation is first formulated as a convex optimization problem by dividing the spectrum among all possible transmission patterns of active access points (APs). To improve scalability with the number of APs, the problem is reformulated using local patterns of interfering APs. To maintain global consistency among local patterns, inter-cluster interaction is characterized as hyper-edges in a hyper-graph with nodes corresponding to neighborhoods of APs. A scalable solution is obtained by iteratively solving a convex optimization problem for bandwidth allocation with reduced complexity and constructing a global spectrum allocation using hyper-graph coloring. Numerical results demonstrate the proposed solution for a network with 100 APs and several hundred user equipments. For a given quality of service (QoS), the proposed scheme can increase the network capacity several fold compared to assigning each user to the strongest AP with full-spectrum reuse.
In this paper, we consider a single-cell multi-user orthogonal frequency division multiple access (OFDMA) network with one unmanned aerial vehicle (UAV), which works as an amplify-and-forward relay to improve the quality-of-service (QoS) of the user equipments (UEs) in the cell edge. Aiming to improve the throughput while guaranteeing the user fairness, we jointly optimize the communication mode, subchannel allocation, power allocation, and UAV trajectory, which is an NP-hard problem. To design the UAV trajectory and resource allocation efficiently, we first decompose the problem into three subproblems, i.e., mode selection and subchannel allocation, trajectory optimization, and power allocation, and then solve these subproblems iteratively. Simulation results show that the proposed algorithm outperforms the random algorithm and the cellular scheme.
We integrate a wireless powered communication network with a cooperative cognitive radio network, where multiple secondary users (SUs) powered wirelessly by a hybrid access point (HAP) help a primary user relay the data. As a reward for the cooperation, the secondary network gains the spectrum access where SUs transmit to HAP using time division multiple access. To maximize the sum-throughput of SUs, we present a secondary sum-throughput optimal resource allocation (STORA) scheme. Under the constraint of meeting target primary rate, the STORA scheme chooses the optimal set of relaying SUs and jointly performs the time and energy allocation for SUs. Specifically, by exploiting the structure of the optimal solution, we find the order in which SUs are prioritized to relay primary data. Since the STORA scheme focuses on the sum-throughput, it becomes inconsiderate towards individual SU throughput, resulting in low fairness. To enhance fairness, we investigate three resource allocation schemes, which are (i) equal time allocation, (ii) minimum throughput maximization, and (iii) proportional time allocation. Simulation results reveal the trade-off between sum-throughput and fairness. The minimum throughput maximization scheme is the fairest one as each SU gets the same throughput, but yields the least SU sum-throughput.
The massive sensing data generated by Internet-of-Things will provide fuel for ubiquitous artificial intelligence (AI), automating the operations of our society ranging from transportation to healthcare. The realistic adoption of this technique however entails labelling of the enormous data prior to the training of AI models via supervised learning. To tackle this challenge, we explore a new perspective of wireless crowd labelling that is capable of downloading data to many imperfect mobile annotators for repetition labelling by exploiting multicasting in wireless networks. In this cross-disciplinary area, the integration of the rate-distortion theory and the principle of repetition labelling for accuracy improvement gives rise to a new tradeoff between radio-and-annotator resources under a constraint on labelling accuracy. Building on the tradeoff and aiming at maximizing the labelling throughput, this work focuses on the joint optimization of encoding rate, annotator clustering, and sub-channel allocation, which results in an NP-hard integer programming problem. To devise an efficient solution approach, we establish an optimal sequential annotator-clustering scheme based on the order of decreasing signal-to-noise ratios. Thereby, the optimal solution can be found by an efficient tree search. Next, the solution is simplified by applying truncated channel inversion. Alternatively, the optimization problem can be recognized as a knapsack problem, which can be efficiently solved in pseudo-polynomial time by means of dynamic programming. In addition, exact polices are derived for the annotators constrained and spectrum constrained cases. Last, simulation results demonstrate the significant throughput gains based on the optimal solution compared with decoupled allocation of the two types of resources.
In this paper, we investigate the performance of a dual-hop block fading cognitive radio network with underlay spectrum sharing over independent but not necessarily identically distributed (i.n.i.d.) Nakagami-$m$ fading channels. The primary network consists of a source and a destination. Depending on whether the secondary network which consists of two source nodes have a single relay for cooperation or multiple relays thereby employs opportunistic relay selection for cooperation and whether the two source nodes suffer from the primary users (PU) interference, two cases are considered in this paper, which are referred to as Scenario (a) and Scenario (b), respectively. For the considered underlay spectrum sharing, the transmit power constraint of the proposed system is adjusted by interference limit on the primary network and the interference imposed by primary user (PU). The developed new analysis obtains new analytical results for the outage capacity (OC) and average symbol error probability (ASEP). In particular, for Scenario (a), tight lower bounds on the OC and ASEP of the secondary network are derived in closed-form. In addition, a closed from expression for the end-to-end OC of Scenario (a) is achieved. With regards to Scenario (b), a tight lower bound on the OC of the secondary network is derived in closed-form. All analytical results are corroborated using Monte Carlo simulation method.
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