Here we define an upper bound on capacity: C VBLAST ZF − unorder ≤ M T log 2 1 + 2 SNR M R + K − 1 M T. Aggregate bit error rate (BER) performance of greedy scheduling over 4×4 multiuser MIMO systems is plotted in Figure 2 for 10 users and at 8 bps/Hz spectral efficiency.
Given the stock-flow production process in fisheries, capacity depends upon the level of the resource stock, where the resource stock abundance also sets an upper bound on capacity.
In particular, we determine upper and lower bounds on the capacity and the capacity region of the Gaussian multiple-input multiple-output (MIMO) relay channel and the Gaussian MIMO two-way relay channel with a half-duplex constrainta.
Unfortunately, the numbers roughly triple with the average power constraint that yields the information theoretic bounds on the capacity and the capacity region of the half-duplex Gaussian relay channel and the half-duplex Gaussian two-way relay channel, respectively.
More specifically, we proposed a dual decomposition approach to evaluate upper and lower bounds on the capacity or the capacity region of the considered MIMO relay channels, for which perfect channel state information (CSI) was assumed.
We present a dual decomposition approach that allows to evaluate upper and lower bounds on the capacity and the capacity region of the half-duplex Gaussian MIMO relay channel and the restricted half-duplex Gaussian MIMO two-way relay channel, respectively.
Assuming perfect channel state information at all nodes and the use of time division duplex communications protocol to separate transmissions and receptions at all nodes, we propose a dual decomposition approach to efficiently determine upper and lower bounds on the capacity and the capacity region of the half-duplex relay channel and the restricted half-duplex two-way relay channel, respectively.
For less than three-hop wireless channel, various works have been done on the capacity or bounds on the capacity [1,13,20-24].
But Endicott bounds on, unperturbed.
Given a network topology and traffic demands, conflict graph models such as the clique model [15], its time-fairness extension [1], as well as Jun and Sichitiu's nominal capacity model [9], may be used to compute optimal bounds on network capacity.
