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Today's automatic train operation technologies must provide reliable short headways that maximize system capacity and permit optimized operations and maintenance regimes.
Opportunistic scheduling policies have also been explored for the channel optimization of MIMO-based wireless networks, where the main goal is to maximize system capacity [21, 22].
Therefore, it's necessary to address the resource allocation between multiple femtocells, in order to eliminate the interference between femtocells and maximize system capacity.
The main goal of any scheduling algorithm in wireless networks is to maximize system capacity while keeping the QoS requirements to a great extent.
These tradeoffs facilitate the design of UPPA strategies to maximize system capacity and satisfy individual target data rates of users without exceeding their allowed BER upper bounds to meet the strict data reliability constraints.
In [17], for an OFDM-based heterogeneous CRN including single network and multi-homing network, the resource allocation problem with the imperfect spectrum sensing is solved to maximize system capacity and the joint subcarrier, and PC problem is formulated under total transmission power constraint, interference constraint, and QoS constraint.
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In other words, a "water-pouring" type of adaptive subcarrier and bit allocation algorithms can be evoked for maximizing system capacity [8].
Scheduling to multiple users, i.e., allowing more than one user to transmit or receive at the same time, is optimal in terms of maximizing system capacity and throughput [3, 4, 8].
To maximize system Erlang capacity and for the adequate and fair performance comparison of the different considered scenarios, fractional channel reservation [1, 38, 39] to prioritize both intra (due to spectrum handoff)- and inter (due to users' mobility -cell handoff call attemobility -cellcall requests is considered in thandoff admission callrol strattempts
In general, schedulers are designed to maximize system throughput and capacity or to minimize error rates.
From the above equation, it is clear that if we want to maximize the system capacity of MU-MIMO, then the SINR of each user should be maximized.
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