Due to the difficulty of coordination in multi-cell random access, it is a practical challenge how to achieve the optimal throughput with decentralized transmission. In this paper, we propose a decentralized multi-cell-aware opportunistic random access (MA-ORA) protocol that achieves the optimal throughput scaling in an ultra-dense @math -cell random access network with one access point (AP) and @math users in each cell, which is suited for machine-type communications. Unlike opportunistic scheduling for cellular multiple access where users are selected by base stations, under our MA-ORA protocol, each user opportunistically transmits with a predefined physical layer data rate in a decentralized manner if the desired signal power to the serving AP is sufficiently large and the generating interference leakage power to the other APs is sufficiently small (i.e., two threshold conditions are fulfilled). As a main result, it is proved that the aggregate throughput scales as @math in a high signal-to-noise ratio (SNR) regime if @math scales faster than @math for small constants @math . Our analytical result is validated by computer simulations. In addition, numerical evaluation confirms that under a practical setting, the proposed MA-ORA protocol outperforms conventional opportunistic random access protocols in terms of throughput.
On the one hand, there are extensive studies on handling interference management of cellular networks with multiple base stations @cite_31 @cite_44 . While it has been elusive to find the optimal strategy with respect to the Shannon-theoretic capacity in multiuser cellular networks, interference alignment (IA) was recently proposed for fundamentally solving the interference problem when there are multiple communication pairs @cite_41 . It was shown that IA can asymptotically achieve the optimal degrees of freedom, which are equal to @math , in the @math -user interference channel with time-varying coefficients. Subsequent work showed that interference management schemes based on IA can be well applicable to various communication scenarios @cite_35 @cite_18 @cite_43 , including interfering multiple access networks @cite_42 @cite_22 @cite_48 . In addition to the multiple access scenarios in which collisions can be avoided, it is of significant importance how to manage interference in . For multi-cell random access networks, several studies were carried out to manage interference by performing IA @cite_34 @cite_7 @cite_6 or successive interference cancellation (SIC) @cite_40 @cite_36 . In @cite_11 @cite_13 , decentralized power allocation approaches were introduced by means of interference mitigation for random access with capabilities of multi-packet reception and SIC at the receiver.
@cite_35 @cite_18 @cite_22 @cite_7 @cite_41 @cite_48 @cite_36 @cite_42 @cite_6 @cite_44 @cite_43 @cite_40 @cite_31 @cite_34 @cite_13 @cite_11
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This paper considers the optimum single cell power control maximizing the aggregate (uplink) communication rate of the cell when there are peak power constraints at mobile users, and a low-complexity data decoder (without successive decoding) at the base station. It is shown that the optimum power allocation is binary, which means that links are either “on” or “off.” By exploiting further structure of the optimum binary power allocation, a simple polynomial-time algorithm for finding the optimum transmission power allocation is proposed, together with a reduced complexity near-optimal heuristic algorithm. Sufficient conditions under which channel-state aware time division multiple access (TDMA) maximizes the aggregate communication rate are established. In a numerical study, we compare and contrast the performance achieved by the optimum binary power-control policy with other suboptimum policies and the throughput capacity achievable via successive decoding. It is observed that two dominant modes of communication arise, wideband or TDMA, and that successive decoding achieves better sum-rates only under near perfect interference cancellation efficiency. In this paper, we exploit the theory of majorization to obtain the aforementioned results. In the final part of this paper, we do so to solve power-control problems in the areas of femtocells and cognitive radio and find that, again, optimal solutions have a binary (or almost binary) character. We investigate spatial interference statistics for multigigabit outdoor mesh networks operating in the unlicensed 60-GHz "millimeter (mm) wave" band. The links in such networks are highly directional: Because of the small carrier wavelength (an order of magnitude smaller than those for existing cellular and wireless local area networks), narrow beams are essential for overcoming higher path loss and can be implemented using compact electronically steerable antenna arrays. Directionality drastically reduces interference, but it also leads to "deafness," making implicit coordination using carrier sense infeasible. In this paper, we make a quantitative case for rethinking medium access control (MAC) design in such settings. Unlike existing MAC protocols for omnidirectional networks, where the focus is on interference management, we contend that MAC design for 60-GHz mesh networks can essentially ignore interference and must focus instead on the challenge of scheduling half-duplex transmissions with deaf neighbors. Our main contribution is an analytical framework for estimating the collision probability in such networks as a function of the antenna patterns and the density of simultaneously transmitting nodes. The numerical results from our interference analysis show that highly directional links can indeed be modeled as pseudowired, in that the collision probability is small even with a significant density of transmitters. Furthermore, simulation of a rudimentary directional slotted Aloha protocol shows that packet losses due to failed coordination are an order of magnitude higher than those due to collisions, confirming our analytical results and highlighting the need for more sophisticated coordination mechanisms. In this paper, we study resource allocation for multiuser multiple-input–single-output secondary communication systems with multiple system design objectives. We consider cognitive radio (CR) networks where the secondary receivers are able to harvest energy from the radio frequency when they are idle. The secondary system provides simultaneous wireless power and secure information transfer to the secondary receivers. We propose a multiobjective optimization framework for the design of a Pareto-optimal resource allocation algorithm based on the weighted Tchebycheff approach. In particular, the algorithm design incorporates three important system design objectives: total transmit power minimization, energy harvesting efficiency maximization, and interference-power-leakage-to-transmit-power ratio minimization. The proposed framework takes into account a quality-of-service (QoS) requirement regarding communication secrecy in the secondary system and the imperfection of the channel state information (CSI) of potential eavesdroppers (idle secondary receivers and primary receivers) at the secondary transmitter. The proposed framework includes total harvested power maximization and interference power leakage minimization as special cases. The adopted multiobjective optimization problem is nonconvex and is recast as a convex optimization problem via semidefinite programming (SDP) relaxation. It is shown that the global optimal solution of the original problem can be constructed by exploiting both the primal and the dual optimal solutions of the SDP-relaxed problem. Moreover, two suboptimal resource allocation schemes for the case when the solution of the dual problem is unavailable for constructing the optimal solution are proposed. Numerical results not only demonstrate the close-to-optimal performance of the proposed suboptimal schemes but unveil an interesting tradeoff among the considered conflicting system design objectives as well. This paper proposes a decentralized interference control scheme for an OFDMA (Orthogonal Frequency Division Multiple Access) cellular scenario where multiple macrocells and femtocells are deployed. In particular, we model the distributed resource allocation problem by means of a potential game, which is demonstrated to converge to a unique and pure Nash equilibrum. In this game, the femto base stations are the players, their actions are the resource blocks and the corresponding power levels to be allocated for downlink transmission, and the utility is designed to guarantee coexistence with the macro system, guaranteeing a fair tradeoff between femto and macro systems' performances. To do that, the utility function does not only consider the capacity of the femtocells, but also the different sources of inter-system interference: macrocell to femtocell, femtocell to femtocell, and femtocell to macrocell. The game is solved by applying a better response dynamic, which first selects the best resource blocks allocation policy for the femtousers, and then selects the most appropriate power levels for each resource block, through the solution of the corresponding convex optimization problem. Simulation results show that the introduction of femtocells in the scenario increases the total system capacity, and that the considered utility function provides an improvement in macro system performance of up to 40 , while reducing the femto capacity only up to 7 , with respect to the well known iterative waterfilling game.