Summary
Chapter 20 Summary: Resource Allocation and Scheduling
Key Points
- 1.
Multiuser diversity transforms fading from an impairment into a resource. By scheduling the user with the best channel in each time slot, the opportunistic scheduler achieves a sum-rate capacity that scales as — a double-logarithmic growth with the number of users . The expected maximum of i.i.d. Rayleigh channel gains grows as , following Gumbel extreme-value statistics.
- 2.
Proportional fair scheduling maximises by serving user . This gradient-ascent interpretation (Kushner--Whiting 2004) guarantees convergence to the log-utility optimum. The -fair utility family parameterises the entire throughput--fairness frontier: (max-rate), (PF), (max-min).
- 3.
OFDMA resource allocation exploits frequency-selective fading by assigning subcarriers to users and distributing power. For unconstrained sum-rate maximisation, the greedy assignment (each subcarrier to its strongest user) followed by water-filling is globally optimal due to the orthogonality of subcarriers. Per-user rate constraints make the problem NP-hard in general.
- 4.
Link adaptation (AMC) closes the gap between channel capacity and practical throughput by dynamically selecting the MCS based on CQI feedback. The target BLER of 10% for initial transmission, combined with HARQ retransmissions, maximises effective throughput. Outer-loop link adaptation (OLLA) tracks bias errors from CQI quantisation and feedback delay.
- 5.
Inter-cell interference coordination (ICIC) manages the dominant impairment for cell-edge users. Frequency reuse with factor improves cell-edge SINR by at the cost of bandwidth per cell. Fractional frequency reuse (FFR) preserves cell-centre throughput (reuse-1) while protecting cell-edge users (reuse-).
- 6.
Cross-layer design integrates the MAC scheduler, link adaptation, HARQ, and buffer management into a unified control loop. Modern systems (5G NR) operate this loop at sub-millisecond granularity with QoS-differentiated scheduling for URLLC, eMBB, and mMTC traffic classes.
Looking Ahead
Chapter 21 extends resource allocation to multi-antenna and multi-cell networks, including coordinated multi-point (CoMP) transmission, cloud-RAN architectures, and network slicing. We will also explore how machine learning can optimise scheduling and resource allocation in real time, replacing hand-crafted heuristics with data-driven policies that adapt to dynamic traffic and interference patterns.