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Optimal channel rate allocation is derived depending upon source significance and channel status information.
An optimal solution for power allocation is derived as a closed form using a Lagrangian relaxation method.
Given an idealized utility metric with an unbounded logarithmic relation between perceived quality and data rate, a concave problem is retained, so that the optimum resource allocation is derived in closed form.
The system model is introduced in Section 2. The resource allocation in finite systems is discussed in Section 3. The optimal resource allocation is derived for large systems in Section 4. Numerical results and conclusions are provided in Sections 5 and 6, respectively.
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Moreover, the closed-form expression of optimal time and bandwidth allocation are derived.
In [4], the secrecy capacity of the ergodic slow fading channels was characterized and the optimal power/rate allocation was derived.
In [4], an optimal pilot symbol allocation was derived analytically for phase-shift keying modulation of order two and four, using BER as the optimization criterion.
Closed-form expressions for lower and upper bounds on the achievable ergodic rates of ZF-DPC with Gaussian inputs and uniform power allocation are derived.
Closed-form lower and upper bounds on the achievable ergodic rates of ZF-DPC with Gaussian inputs and uniform power allocation are derived.
Multiple solutions of resource allocation were derived based on convex optimization or game theory via lowering power consumption under certain constraints, such as minimizing the outage probability [22-24] [22-24]zing energy efficiency [25-28], maximizing ovenergythroughput [29,30], maintaining sefficiencylity, or maximizing the sum of SUs' capacities and signal-to-noise power ratio (SNR) [25-28] [25-28]
In[7], the optimal bandwidth and power allocations were derived to maximize the sum ergodic capacity of all the SUs under all possible combinations of transmit power constraint and interference power constraint.
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